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ORCIDTom Goldstein
Thomas A. Goldstein
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- affiliation: University of Maryland, Department of Computer Science, College Park, MD, USA
- affiliation (PhD 2010): University of California, Los Angeles, CA, USA
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2020 – today
- 2024
[j19]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models. Trans. Mach. Learn. Res. 2024 (2024)- [c148]Kaiyu Yue, Bor-Chun Chen, Jonas Geiping, Hengduo Li, Tom Goldstein, Ser-Nam Lim:
Object Recognition as Next Token Prediction. CVPR 2024: 16645-16656 - [c147]Renkun Ni, Yonghui Xiao, Phoenix Meadowlark, Oleg Rybakov, Tom Goldstein, Ananda Theertha Suresh, Ignacio López-Moreno, Mingqing Chen, Rajiv Mathews:
FedAQT: Accurate Quantized Training with Federated Learning. ICASSP 2024: 6100-6104 - [c146]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. ICLR 2024 - [c145]Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein:
NEFTune: Noisy Embeddings Improve Instruction Finetuning. ICLR 2024 - [c144]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein:
On the Reliability of Watermarks for Large Language Models. ICLR 2024 - [c143]Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang:
WAVES: Benchmarking the Robustness of Image Watermarks. ICML 2024 - [c142]Lichang Chen, Chen Zhu, Jiuhai Chen, Davit Soselia, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro:
ODIN: Disentangled Reward Mitigates Hacking in RLHF. ICML 2024 - [c141]Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou:
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models. ICML 2024 - [c140]Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text. ICML 2024 - [c139]Manli Shu, Le Xue, Ning Yu, Roberto Martín-Martín, Caiming Xiong, Tom Goldstein, Juan Carlos Niebles, Ran Xu:
Hierarchical Point Attention for Indoor 3D Object Detection. ICRA 2024: 4245-4251 - [i200]Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang:
Benchmarking the Robustness of Image Watermarks. CoRR abs/2401.08573 (2024) - [i199]Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text. CoRR abs/2401.12070 (2024) - [i198]Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang:
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models. CoRR abs/2402.06659 (2024) - [i197]Lichang Chen, Chen Zhu, Davit Soselia, Jiuhai Chen, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro:
ODIN: Disentangled Reward Mitigates Hacking in RLHF. CoRR abs/2402.07319 (2024) - [i196]Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, Tom Goldstein:
Coercing LLMs to do and reveal (almost) anything. CoRR abs/2402.14020 (2024) - [i195]Hamid Kazemi, Atoosa Malemir Chegini, Jonas Geiping, Soheil Feizi, Tom Goldstein:
What do we learn from inverting CLIP models? CoRR abs/2403.02580 (2024) - [i194]Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum:
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. CoRR abs/2403.16365 (2024) - [i193]Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, Nicholas Carlini:
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models. CoRR abs/2404.01231 (2024) - [i192]Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta
, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, Tom Goldstein:
Measuring Style Similarity in Diffusion Models. CoRR abs/2404.01292 (2024) - [i191]Sean McLeish, Avi Schwarzschild, Tom Goldstein:
Benchmarking ChatGPT on Algorithmic Reasoning. CoRR abs/2404.03441 (2024) - [i190]John Kirchenbauer, Garrett Honke, Gowthami Somepalli, Jonas Geiping, Daphne Ippolito, Katherine Lee, Tom Goldstein, David Andre:
LMD3: Language Model Data Density Dependence. CoRR abs/2405.06331 (2024) - [i189]Ruchit Rawal, Khalid Saifullah, Ronen Basri, David Jacobs, Gowthami Somepalli, Tom Goldstein:
CinePile: A Long Video Question Answering Dataset and Benchmark. CoRR abs/2405.08813 (2024) - [i188]Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou, Yiyang Zhou, Huaxiu Yao, Tianyi Zhou, Tom Goldstein, Parminder Bhatia, Furong Huang, Cao Xiao:
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement. CoRR abs/2405.15973 (2024) - [i187]Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein:
Transformers Can Do Arithmetic with the Right Embeddings. CoRR abs/2405.17399 (2024) - [i186]Larisa Markeeva, Sean McLeish, Borja Ibarz, Wilfried Bounsi, Olga Kozlova, Alex Vitvitskyi, Charles Blundell, Tom Goldstein, Avi Schwarzschild, Petar Velickovic:
The CLRS-Text Algorithmic Reasoning Language Benchmark. CoRR abs/2406.04229 (2024) - [i185]Lichang Chen, Jiuhai Chen, Chenxi Liu, John Kirchenbauer, Davit Soselia, Chen Zhu, Tom Goldstein, Tianyi Zhou, Heng Huang:
OPTune: Efficient Online Preference Tuning. CoRR abs/2406.07657 (2024) - [i184]Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein:
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs. CoRR abs/2406.10209 (2024) - [i183]Alex Hanson, Allen Tu, Vasu Singla, Mayuka Jayawardhana, Matthias Zwicker, Tom Goldstein:
PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting. CoRR abs/2406.10219 (2024) - [i182]Jiuhai Chen, Rifaa Qadri, Yuxin Wen, Neel Jain, John Kirchenbauer, Tianyi Zhou, Tom Goldstein:
GenQA: Generating Millions of Instructions from a Handful of Prompts. CoRR abs/2406.10323 (2024) - [i181]Vasu Singla, Kaiyu Yue, Sukriti Paul, Reza Shirkavand, Mayuka Jayawardhana, Alireza Ganjdanesh, Heng Huang, Abhinav Bhatele, Gowthami Somepalli, Tom Goldstein:
From Pixels to Prose: A Large Dataset of Dense Image Captions. CoRR abs/2406.10328 (2024) - [i180]Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum:
LiveBench: A Challenging, Contamination-Free LLM Benchmark. CoRR abs/2406.19314 (2024) - [i179]Michael-Andrei Panaitescu-Liess, Zora Che, Bang An, Yuancheng Xu, Pankayaraj Pathmanathan, Souradip Chakraborty, Sicheng Zhu, Tom Goldstein, Furong Huang:
Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data? CoRR abs/2407.17417 (2024) - [i178]Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang:
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. CoRR abs/2409.18433 (2024) - 2023
- [j18]Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein:
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1563-1580 (2023) - [j17]Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang, Larry S. Davis:
Towards Transferable Adversarial Attacks on Image and Video Transformers. IEEE Trans. Image Process. 32: 6346-6358 (2023) - [c138]Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, John P. Dickerson, Micah Goldblum, Tom Goldstein:
A Deep Dive into Dataset Imbalance and Bias in Face Identification. AIES 2023: 229-247 - [c137]Shishira R. Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava:
Unifying the Harmonic Analysis of Adversarial Attacks and Robustness. BMVC 2023: 620-621 - [c136]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. CVPR Workshops 2023: 843-852 - [c135]Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models. CVPR 2023: 6048-6058 - [c134]Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein:
STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning. ICASSP 2023: 1-5 - [c133]Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein:
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. ICLR 2023 - [c132]Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein:
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. ICLR 2023 - [c131]Liam H. Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein:
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. ICLR 2023 - [c130]Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson:
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. ICLR 2023 - [c129]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Provable Robustness against Wasserstein Distribution Shifts via Input Randomization. ICLR 2023 - [c128]Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum:
Transfer Learning with Deep Tabular Models. ICLR 2023 - [c127]Khalid Saifullah, Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein:
Seeing in Words: Learning to Classify through Language Bottlenecks. Tiny Papers @ ICLR 2023 - [c126]Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. ICLR 2023 - [c125]Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang:
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. ICLR 2023 - [c124]Jonas Geiping, Tom Goldstein:
Cramming: Training a Language Model on a single GPU in one day. ICML 2023: 11117-11143 - [c123]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein:
A Watermark for Large Language Models. ICML 2023: 17061-17084 - [c122]Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein:
GOAT: A Global Transformer on Large-scale Graphs. ICML 2023: 17375-17390 - [c121]Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. NeurIPS 2023 - [c120]Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum:
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. NeurIPS 2023 - [c119]Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein:
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. NeurIPS 2023 - [c118]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein:
What Can We Learn from Unlearnable Datasets? NeurIPS 2023 - [c117]Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein:
On the Exploitability of Instruction Tuning. NeurIPS 2023 - [c116]Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Understanding and Mitigating Copying in Diffusion Models. NeurIPS 2023 - [c115]Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery. NeurIPS 2023 - [c114]Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein:
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images. NeurIPS 2023 - [i177]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein:
A Watermark for Large Language Models. CoRR abs/2301.10226 (2023) - [i176]Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang:
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. CoRR abs/2302.03015 (2023) - [i175]Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery. CoRR abs/2302.03668 (2023) - [i174]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. CoRR abs/2302.07121 (2023) - [i173]Alex Stein, Avi Schwarzschild, Michael J. Curry, Tom Goldstein, John P. Dickerson:
Neural Auctions Compromise Bidder Information. CoRR abs/2303.00116 (2023) - [i172]Pedro Sandoval Segura, Jonas Geiping, Tom Goldstein:
JPEG Compressed Images Can Bypass Protections Against AI Editing. CoRR abs/2304.02234 (2023) - [i171]Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum:
A Cookbook of Self-Supervised Learning. CoRR abs/2304.12210 (2023) - [i170]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein:
What Can We Learn from Unlearnable Datasets? CoRR abs/2305.19254 (2023) - [i169]Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein:
Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust. CoRR abs/2305.20030 (2023) - [i168]Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Understanding and Mitigating Copying in Diffusion Models. CoRR abs/2305.20086 (2023) - [i167]Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou:
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models. CoRR abs/2306.03082 (2023) - [i166]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein:
On the Reliability of Watermarks for Large Language Models. CoRR abs/2306.04634 (2023) - [i165]Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models. CoRR abs/2306.13651 (2023) - [i164]Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein:
On the Exploitability of Instruction Tuning. CoRR abs/2306.17194 (2023) - [i163]Khalid Saifullah, Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein:
Seeing in Words: Learning to Classify through Language Bottlenecks. CoRR abs/2307.00028 (2023) - [i162]Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein:
Baseline Defenses for Adversarial Attacks Against Aligned Language Models. CoRR abs/2309.00614 (2023) - [i161]Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein:
NEFTune: Noisy Embeddings Improve Instruction Finetuning. CoRR abs/2310.05914 (2023) - [i160]Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein:
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. CoRR abs/2310.19909 (2023) - [i159]Vasu Singla, Pedro Sandoval Segura, Micah Goldblum, Jonas Geiping, Tom Goldstein:
A Simple and Efficient Baseline for Data Attribution on Images. CoRR abs/2311.03386 (2023) - [i158]Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum:
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. CoRR abs/2311.05877 (2023) - [i157]Kaiyu Yue, Bor-Chun Chen, Jonas Geiping, Hengduo Li, Tom Goldstein, Ser-Nam Lim:
Object Recognition as Next Token Prediction. CoRR abs/2312.02142 (2023) - [i156]Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson:
Perspectives on the State and Future of Deep Learning - 2023. CoRR abs/2312.09323 (2023) - [i155]Ping-yeh Chiang, Yipin Zhou, Omid Poursaeed, Satya Narayan Shukla, Ashish Shah, Tom Goldstein, Ser-Nam Lim:
Universal Pyramid Adversarial Training for Improved ViT Performance. CoRR abs/2312.16339 (2023) - 2022
- [j16]Haochuan Song, Tom Goldstein, Xiaohu You, Chuan Zhang, Olav Tirkkonen, Christoph Studer:
Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems. IEEE Trans. Wirel. Commun. 21(6): 4068-4084 (2022) - [c113]Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang:
Towards Transferable Adversarial Attacks on Vision Transformers. AAAI 2022: 2668-2676 - [c112]Michael J. Curry, Uro Lyi, Tom Goldstein, John P. Dickerson:
Learning Revenue-Maximizing Auctions With Differentiable Matching. AISTATS 2022: 6062-6073 - [c111]Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein:
Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR 2022: 60-69 - [c110]Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein:
Poisons that are learned faster are more effective. CVPR Workshops 2022: 197-204 - [c109]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CVPR 2022: 13689-13698 - [c108]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features. ICLR 2022 - [c107]Liam H. Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, Tom Goldstein:
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. ICLR 2022 - [c106]Jonas Geiping, Micah Goldblum, Phillip Pope, Michael Moeller, Tom Goldstein:
Stochastic Training is Not Necessary for Generalization. ICLR 2022 - [c105]Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein:
The Close Relationship Between Contrastive Learning and Meta-Learning. ICLR 2022 - [c104]Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein:
The Uncanny Similarity of Recurrence and Depth. ICLR 2022 - [c103]Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein:
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. ICLR 2022 - [c102]Arpit Bansal, Ping-Yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. ICML 2022: 1450-1465 - [c101]Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein:
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. ICML 2022: 7484-7512 - [c100]Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein:
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. ICML 2022: 23668-23684 - [c99]Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking. NeurIPS 2022 - [c98]Samuel Dooley, George Z. Wei, Tom Goldstein, John Dickerson:
Robustness Disparities in Face Detection. NeurIPS 2022 - [c97]Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein:
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. NeurIPS 2022 - [c96]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David Jacobs:
Autoregressive Perturbations for Data Poisoning. NeurIPS 2022 - [c95]Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao:
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. NeurIPS 2022 - [c94]Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein:
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. NeurIPS 2022 - [i154]Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor:
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning. CoRR abs/2201.00762 (2022) - [i153]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Certifying Model Accuracy under Distribution Shifts. CoRR abs/2201.12440 (2022) - [i152]Liam Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojtek Czaja, Micah Goldblum, Tom Goldstein:
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. CoRR abs/2201.12675 (2022) - [i151]Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein:
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. CoRR abs/2201.12961 (2022) - [i150]Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein:
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. CoRR abs/2202.00580 (2022) - [i149]Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking. CoRR abs/2202.05826 (2022) - [i148]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CoRR abs/2203.08124 (2022) - [i147]Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, Micah Goldblum, Tom Goldstein:
A Deep Dive into Dataset Imbalance and Bias in Face Identification. CoRR abs/2203.08235 (2022) - [i146]Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein:
Poisons that are learned faster are more effective. CoRR abs/2204.08615 (2022) - [i145]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David W. Jacobs:
Autoregressive Perturbations for Data Poisoning. CoRR abs/2206.03693 (2022) - [i144]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features. CoRR abs/2206.08473 (2022) - [i143]Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum:
Transfer Learning with Deep Tabular Models. CoRR abs/2206.15306 (2022) - [i142]Arpit Bansal, Ping-yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. CoRR abs/2207.07972 (2022) - [i141]Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. CoRR abs/2208.09392 (2022) - [i140]Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao:
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. CoRR abs/2209.07511 (2022) - [i139]Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson:
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. CoRR abs/2210.06441 (2022) - [i138]Yuxin Wen, Jonas Geiping, Liam Fowl, Hossein Souri, Rama Chellappa, Micah Goldblum, Tom Goldstein:
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning. CoRR abs/2210.09305 (2022) - [i137]Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. CoRR abs/2210.10750 (2022) - [i136]Renkun Ni, Ping-yeh Chiang, Jonas Geiping, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein:
K-SAM: Sharpness-Aware Minimization at the Speed of SGD. CoRR abs/2210.12864 (2022) - [i135]Samuel Dooley, George Z. Wei, Tom Goldstein, John P. Dickerson:
Robustness Disparities in Face Detection. CoRR abs/2211.15937 (2022) - [i134]Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models. CoRR abs/2212.03860 (2022) - [i133]Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein:
What do Vision Transformers Learn? A Visual Exploration. CoRR abs/2212.06727 (2022) - [i132]Jonas Geiping, Tom Goldstein:
Cramming: Training a Language Model on a Single GPU in One Day. CoRR abs/2212.14034 (2022) - 2021
- [c93]Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang:
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. AAAI 2021: 10815-10823 - [c92]Gian Marti, Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Hybrid Jammer Mitigation for All-Digital mmWave Massive MU-MIMO. ACSCC 2021: 93-99 - [c91]Micah Goldblum, Avi Schwarzschild, Ankit B. Patel, Tom Goldstein:
Adversarial attacks on machine learning systems for high-frequency trading. ICAIF 2021: 2:1-2:9 - [c90]Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta:
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff. ICASSP 2021: 3855-3859 - [c89]Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein:
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. ICLR 2021 - [c88]Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein:
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. ICLR 2021 - [c87]Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein:
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic. ICLR 2021 - [c86]Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein:
The Intrinsic Dimension of Images and Its Impact on Learning. ICLR 2021 - [c85]Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein:
Data Augmentation for Meta-Learning. ICML 2021: 8152-8161 - [c84]Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein:
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. ICML 2021: 9389-9398 - [c83]Manli Shu, Yu Shen, Ming C. Lin, Tom Goldstein:
Adversarial Differentiable Data Augmentation for Autonomous Systems. ICRA 2021: 14069-14075 - [c82]Aounon Kumar, Tom Goldstein:
Center Smoothing: Certified Robustness for Networks with Structured Outputs. NeurIPS 2021: 5560-5575 - [c81]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein:
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. NeurIPS 2021: 6695-6706 - [c80]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. NeurIPS 2021: 6733-6746 - [c79]Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein:
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. NeurIPS 2021: 16410-16422 - [c78]Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro:
Long-Short Transformer: Efficient Transformers for Language and Vision. NeurIPS 2021: 17723-17736 - [c77]Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin:
Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering. NeurIPS 2021: 26250-26263 - [c76]Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein:
Encoding Robustness to Image Style via Adversarial Feature Perturbations. NeurIPS 2021: 28042-28053 - [c75]Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein:
Adversarial Examples Make Strong Poisons. NeurIPS 2021: 30339-30351 - [c74]Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein:
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients. ECML/PKDD (3) 2021: 628-643 - [i131]Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein:
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. CoRR abs/2101.07922 (2021) - [i130]Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P. Dickerson, Tom Goldstein:
Technical Challenges for Training Fair Neural Networks. CoRR abs/2102.06764 (2021) - [i129]Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein:
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. CoRR abs/2102.08098 (2021) - [i128]Aounon Kumar, Tom Goldstein:
Center Smoothing for Certifiably Robust Vector-Valued Functions. CoRR abs/2102.09701 (2021) - [i127]Avi Schwarzschild, Arjun Gupta, Micah Goldblum, Tom Goldstein:
Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems. CoRR abs/2102.11011 (2021) - [i126]Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin:
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images. CoRR abs/2102.13262 (2021) - [i125]Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein:
What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors. CoRR abs/2102.13624 (2021) - [i124]Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein:
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations. CoRR abs/2103.02079 (2021) - [i123]Liam Fowl, Ping-yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, Tom Goldstein:
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release. CoRR abs/2103.02683 (2021) - [i122]Shivam Akhauri, Laura Y. Zheng, Tom Goldstein, Ming C. Lin:
Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving. CoRR abs/2103.08116 (2021) - [i121]Zuxuan Wu, Tom Goldstein, Larry S. Davis, Ser-Nam Lim:
THAT: Two Head Adversarial Training for Improving Robustness at Scale. CoRR abs/2103.13612 (2021) - [i120]Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein:
The Intrinsic Dimension of Images and Its Impact on Learning. CoRR abs/2104.08894 (2021) - [i119]Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein:
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. CoRR abs/2106.01342 (2021) - [i118]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein:
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. CoRR abs/2106.04537 (2021) - [i117]Michael J. Curry, Uro Lyi, Tom Goldstein, John Dickerson:
Learning Revenue-Maximizing Auctions With Differentiable Matching. CoRR abs/2106.07877 (2021) - [i116]Hossein Souri, Micah Goldblum, Liam Fowl, Rama Chellappa, Tom Goldstein:
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. CoRR abs/2106.08970 (2021) - [i115]Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein:
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. CoRR abs/2106.09643 (2021) - [i114]Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojtek Czaja, Tom Goldstein:
Adversarial Examples Make Strong Poisons. CoRR abs/2106.10807 (2021) - [i113]Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro:
Long-Short Transformer: Efficient Transformers for Language and Vision. CoRR abs/2107.02192 (2021) - [i112]Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein:
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. CoRR abs/2108.01335 (2021) - [i111]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
Datasets for Studying Generalization from Easy to Hard Examples. CoRR abs/2108.06011 (2021) - [i110]Samuel Dooley, Tom Goldstein, John P. Dickerson:
Robustness Disparities in Commercial Face Detection. CoRR abs/2108.12508 (2021) - [i109]Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang:
Towards Transferable Adversarial Attacks on Vision Transformers. CoRR abs/2109.04176 (2021) - [i108]Jonas Geiping, Micah Goldblum, Phillip E. Pope, Michael Moeller, Tom Goldstein:
Stochastic Training is Not Necessary for Generalization. CoRR abs/2109.14119 (2021) - [i107]Samuel Dooley, Ryan Downing, George Z. Wei, Nathan Shankar, Bradon Thymes, Gudrun Thorkelsdottir, Tiye Kurtz-Miott, Rachel Mattson, Olufemi Obiwumi, Valeriia Cherepanova, Micah Goldblum, John P. Dickerson, Tom Goldstein:
Comparing Human and Machine Bias in Face Recognition. CoRR abs/2110.08396 (2021) - [i106]Liam Fowl, Jonas Geiping, Wojtek Czaja, Micah Goldblum, Tom Goldstein:
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. CoRR abs/2110.13057 (2021) - [i105]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features. CoRR abs/2110.13413 (2021) - [i104]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P. Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. CoRR abs/2110.14363 (2021) - [i103]Haochuan Song, Tom Goldstein, Xiaohu You, Chuan Zhang, Olav Tirkkonen, Christoph Studer:
Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems. CoRR abs/2110.15928 (2021) - [i102]Shishira R. Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava:
A Frequency Perspective of Adversarial Robustness. CoRR abs/2111.00861 (2021) - [i101]Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang, Wojciech Czaja, Richard Leapman, Micah Goldblum, Tom Goldstein:
Active Learning at the ImageNet Scale. CoRR abs/2111.12880 (2021) - [i100]Gian Marti, Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Hybrid Jammer Mitigation for All-Digital mmWave Massive MU-MIMO. CoRR abs/2111.13055 (2021) - 2020
- [j15]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication. IEEE J. Sel. Areas Commun. 38(9): 2128-2141 (2020) - [j14]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO With Processing-in-Memory. IEEE Trans. Circuits Syst. II Express Briefs 67-II(5): 891-895 (2020) - [c73]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. AAAI 2020: 3996-4003 - [c72]Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein:
Universal Adversarial Training. AAAI 2020: 5636-5643 - [c71]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Hardware-Friendly Two-Stage Spatial Equalization for All-Digital mmWave Massive MU-MIMO. ACSSC 2020: 388-392 - [c70]Abhay Kumar Yadav, Tom Goldstein, David W. Jacobs:
Making L-BFGS Work with Industrial-Strength Nets. BMVC 2020 - [c69]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
MSE-Optimal Neural Network Initialization via Layer Fusion. CISS 2020: 1-6 - [c68]Zuxuan Wu, Ser-Nam Lim, Larry S. Davis, Tom Goldstein:
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors. ECCV (4) 2020: 1-17 - [c67]Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. ECCV Workshops (1) 2020: 55-70 - [c66]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Soft-Output Finite Alphabet Equalization for mmWave Massive MIMO. ICASSP 2020: 1763-1767 - [c65]Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu:
Headless Horseman: Adversarial Attacks on Transfer Learning Models. ICASSP 2020: 3087-3091 - [c64]Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi:
Witchcraft: Efficient PGD Attacks with Random Step Size. ICASSP 2020: 3747-3751 - [c63]W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization Through Visualizations. ICBINB@NeurIPS 2020: 87-97 - [c62]Ping-yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein:
Certified Defenses for Adversarial Patches. ICLR 2020 - [c61]Amin Ghiasi, Ali Shafahi, Tom Goldstein:
Breaking Certified Defenses: Semantic Adversarial Examples with Spoofed robustness Certificates. ICLR 2020 - [c60]Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein:
Truth or backpropaganda? An empirical investigation of deep learning theory. ICLR 2020 - [c59]Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David W. Jacobs, Tom Goldstein:
Adversarially robust transfer learning. ICLR 2020 - [c58]Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke, Cornelia Fermüller, Yiannis Aloimonos:
Network Deconvolution. ICLR 2020 - [c57]Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu:
FreeLB: Enhanced Adversarial Training for Natural Language Understanding. ICLR 2020 - [c56]Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein:
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks. ICML 2020: 3607-3616 - [c55]Chuan Guo, Tom Goldstein, Awni Y. Hannun, Laurens van der Maaten:
Certified Data Removal from Machine Learning Models. ICML 2020: 3832-3842 - [c54]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. ICML 2020: 5458-5467 - [c53]Parsa Saadatpanah, Ali Shafahi, Tom Goldstein:
Adversarial Attacks on Copyright Detection Systems. ICML 2020: 8307-8315 - [c52]Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein:
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent. ICML 2020: 8469-8479 - [c51]Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein:
Detection as Regression: Certified Object Detection with Median Smoothing. NeurIPS 2020 - [c50]Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John Dickerson:
Certifying Strategyproof Auction Networks. NeurIPS 2020 - [c49]Micah Goldblum, Liam Fowl, Tom Goldstein:
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach. NeurIPS 2020 - [c48]W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein:
MetaPoison: Practical General-purpose Clean-label Data Poisoning. NeurIPS 2020 - [c47]Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein:
Certifying Confidence via Randomized Smoothing. NeurIPS 2020 - [i99]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
MSE-Optimal Neural Network Initialization via Layer Fusion. CoRR abs/2001.10509 (2020) - [i98]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. CoRR abs/2002.03239 (2020) - [i97]Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein:
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks. CoRR abs/2002.06753 (2020) - [i96]Micah Goldblum, Avi Schwarzschild, Naftali Cohen, Tucker Balch, Ankit B. Patel, Tom Goldstein:
Adversarial Attacks on Machine Learning Systems for High-Frequency Trading. CoRR abs/2002.09565 (2020) - [i95]Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein:
Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers. CoRR abs/2002.09766 (2020) - [i94]Ping-yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein:
Certified Defenses for Adversarial Patches. CoRR abs/2003.06693 (2020) - [i93]Amin Ghiasi, Ali Shafahi, Tom Goldstein:
Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates. CoRR abs/2003.08937 (2020) - [i92]W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein:
MetaPoison: Practical General-purpose Clean-label Data Poisoning. CoRR abs/2004.00225 (2020) - [i91]Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu:
Headless Horseman: Adversarial Attacks on Transfer Learning Models. CoRR abs/2004.09007 (2020) - [i90]Zheng Xu, Ali Shafahi, Tom Goldstein:
Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training. CoRR abs/2006.00387 (2020) - [i89]Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John P. Dickerson:
Certifying Strategyproof Auction Networks. CoRR abs/2006.08742 (2020) - [i88]Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein:
Adaptive Learning Rates with Maximum Variation Averaging. CoRR abs/2006.11918 (2020) - [i87]Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein:
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. CoRR abs/2006.12557 (2020) - [i86]Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John P. Dickerson, Tom Goldstein:
Detection as Regression: Certified Object Detection by Median Smoothing. CoRR abs/2007.03730 (2020) - [i85]Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein:
WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic. CoRR abs/2007.13242 (2020) - [i84]Jonas Geiping, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein:
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. CoRR abs/2009.02276 (2020) - [i83]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication. CoRR abs/2009.02747 (2020) - [i82]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Soft-Output Finite Alphabet Equalization for mmWAVE Massive MIMO. CoRR abs/2009.02990 (2020) - [i81]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO with Processing-In-Memory. CoRR abs/2009.03874 (2020) - [i80]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems. CoRR abs/2009.05133 (2020) - [i79]Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein:
Certifying Confidence via Randomized Smoothing. CoRR abs/2009.08061 (2020) - [i78]Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein:
Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization. CoRR abs/2009.08965 (2020) - [i77]David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, Tom Goldstein:
An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process. CoRR abs/2010.05137 (2020) - [i76]Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J. Curry, Samuel Dooley, Ping-yeh Chiang, Tom Goldstein, John P. Dickerson:
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning. CoRR abs/2010.06398 (2020) - [i75]Liam Fowl, Micah Goldblum, Arjun Gupta, Amr Sharaf, Tom Goldstein:
Random Network Distillation as a Diversity Metric for Both Image and Text Generation. CoRR abs/2010.06715 (2020) - [i74]Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein:
Data Augmentation for Meta-Learning. CoRR abs/2010.07092 (2020) - [i73]Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein:
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. CoRR abs/2010.07334 (2020) - [i72]Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein:
FLAG: Adversarial Data Augmentation for Graph Neural Networks. CoRR abs/2010.09891 (2020) - [i71]Alexander Levine, Aounon Kumar, Thomas A. Goldstein, Soheil Feizi:
Tight Second-Order Certificates for Randomized Smoothing. CoRR abs/2010.10549 (2020) - [i70]Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang:
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. CoRR abs/2010.12989 (2020) - [i69]Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta:
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff. CoRR abs/2011.09527 (2020) - [i68]David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, Tom Goldstein:
Analyzing the Machine Learning Conference Review Process. CoRR abs/2011.12919 (2020) - [i67]Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein:
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. CoRR abs/2012.10544 (2020)
2010 – 2019
- 2019
- [c46]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems. ACSSC 2019: 178-183 - [c45]Eric Lei, Oscar Castañeda, Olav Tirkkonen, Tom Goldstein, Christoph Studer:
Siamese Neural Networks for Wireless Positioning and Channel Charting. Allerton 2019: 200-207 - [c44]Ali Shafahi, Amin Ghiasi, Mahyar Najibi, Furong Huang, John P. Dickerson, Tom Goldstein:
Batch-wise Logit-Similarity: Generalizing Logit-Squeezing and Label-Smoothing. BMVC 2019: 72 - [c43]Zuxuan Wu, Xin Wang, Joseph Gonzalez, Tom Goldstein, Larry Davis:
ACE: Adapting to Changing Environments for Semantic Segmentation. ICCV 2019: 2121-2130 - [c42]Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein:
Are adversarial examples inevitable? ICLR (Poster) 2019 - [c41]Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein:
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. ICML 2019: 7614-7623 - [c40]Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein:
Adversarial training for free! NeurIPS 2019: 3353-3364 - [c39]Pengzhi Huang, Oscar Castañeda, Emre Gönültas, Saïd Medjkouh, Olav Tirkkonen, Tom Goldstein, Christoph Studer:
Improving Channel Charting with Representation -Constrained Autoencoders. SPAWC 2019: 1-5 - [i66]Ryen Krusinga, Sohil Shah, Matthias Zwicker, Tom Goldstein, David W. Jacobs:
Understanding the (un)interpretability of natural image distributions using generative models. CoRR abs/1901.01499 (2019) - [i65]Zuxuan Wu, Xin Wang, Joseph E. Gonzalez, Tom Goldstein, Larry S. Davis:
ACE: Adapting to Changing Environments for Semantic Segmentation. CoRR abs/1904.06268 (2019) - [i64]Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein:
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent. CoRR abs/1904.06963 (2019) - [i63]Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein:
Adversarial Training for Free! CoRR abs/1904.12843 (2019) - [i62]Chen Zhu, W. Ronny Huang, Ali Shafahi, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein:
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. CoRR abs/1905.05897 (2019) - [i61]Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David W. Jacobs, Tom Goldstein:
Adversarially robust transfer learning. CoRR abs/1905.08232 (2019) - [i60]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. CoRR abs/1905.09747 (2019) - [i59]Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke, Cornelia Fermüller, Yiannis Aloimonos:
Network Deconvolution. CoRR abs/1905.11926 (2019) - [i58]W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization through Visualizations. CoRR abs/1906.03291 (2019) - [i57]Parsa Saadatpanah, Ali Shafahi, Tom Goldstein:
Adversarial attacks on Copyright Detection Systems. CoRR abs/1906.07153 (2019) - [i56]Pengzhi Huang, Oscar Castañeda, Emre Gönültas, Saïd Medjkouh, Olav Tirkkonen, Tom Goldstein, Christoph Studer:
Improving Channel Charting with Representation-Constrained Autoencoders. CoRR abs/1908.02878 (2019) - [i55]Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu:
FreeLB: Enhanced Adversarial Training for Language Understanding. CoRR abs/1909.11764 (2019) - [i54]Eric Lei, Oscar Castañeda, Olav Tirkkonen, Tom Goldstein, Christoph Studer:
Siamese Neural Networks for Wireless Positioning and Channel Charting. CoRR abs/1909.13355 (2019) - [i53]Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Strong Baseline Defenses Against Clean-Label Poisoning Attacks. CoRR abs/1909.13374 (2019) - [i52]Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein:
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory. CoRR abs/1910.00359 (2019) - [i51]Micah Goldblum, Liam Fowl, Tom Goldstein:
Robust Few-Shot Learning with Adversarially Queried Meta-Learners. CoRR abs/1910.00982 (2019) - [i50]Yogesh Balaji, Tom Goldstein, Judy Hoffman:
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets. CoRR abs/1910.08051 (2019) - [i49]Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein:
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training? CoRR abs/1910.11585 (2019) - [i48]Zuxuan Wu, Ser-Nam Lim, Larry Davis, Tom Goldstein:
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors. CoRR abs/1910.14667 (2019) - [i47]Chuan Guo, Tom Goldstein, Awni Y. Hannun, Laurens van der Maaten:
Certified Data Removal from Machine Learning Models. CoRR abs/1911.03030 (2019) - [i46]Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi:
WITCHcraft: Efficient PGD attacks with random step size. CoRR abs/1911.07989 (2019) - 2018
- [j13]Christoph Studer, Said Medjkouh, Emre Gönültas, Tom Goldstein, Olav Tirkkonen:
Channel Charting: Locating Users Within the Radio Environment Using Channel State Information. IEEE Access 6: 47682-47698 (2018) - [j12]Soumyadip Sengupta, Hao Zhou, Walter Forkel, Ronen Basri, Tom Goldstein, David W. Jacobs:
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability. J. Math. Imaging Vis. 60(4): 563-575 (2018) - [j11]Oscar Castañeda, Tom Goldstein, Christoph Studer:
VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems. IEEE Trans. Circuits Syst. I Regul. Pap. 65-I(3): 1120-1132 (2018) - [j10]Tom Goldstein, Christoph Studer:
PhaseMax: Convex Phase Retrieval via Basis Pursuit. IEEE Trans. Inf. Theory 64(4): 2675-2689 (2018) - [c38]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
PhaseLin: Linear phase retrieval. CISS 2018: 1-6 - [c37]Zuxuan Wu, Xintong Han, Yen-Liang Lin, Mustafa Gökhan Uzunbas, Tom Goldstein, Ser-Nam Lim, Larry S. Davis:
DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation. ECCV (5) 2018: 535-552 - [c36]Said Medjkouh, Emre Gönültas, Tom Goldstein, Olav Tirkkonen, Christoph Studer:
Unsupervised Charting of Wireless Channels. GLOBECOM 2018: 1-7 - [c35]Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein:
Stabilizing Adversarial Nets with Prediction Methods. ICLR (Poster) 2018 - [c34]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
Linear Spectral Estimators and an Application to Phase Retrieval. ICML 2018: 1729-1738 - [c33]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
VLSI Design of a 3-bit Constant-Modulus Precoder for Massive MU-MIMO. ISCAS 2018: 1-5 - [c32]Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein:
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks. NeurIPS 2018: 6106-6116 - [c31]Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein:
Visualizing the Loss Landscape of Neural Nets. NeurIPS 2018: 6391-6401 - [c30]Tom Goldstein:
Challenges for Machine Learning on Distributed Platforms (Invited Talk). DISC 2018: 2:1-2:3 - [i45]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
PhaseLin: Linear Phase Retrieval. CoRR abs/1802.00432 (2018) - [i44]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer:
VLSI Design of a 3-bit Constant-Modulus Precoder for Massive MU-MIMO. CoRR abs/1803.00558 (2018) - [i43]Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein:
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks. CoRR abs/1804.00792 (2018) - [i42]Zuxuan Wu, Xintong Han, Yen-Liang Lin, Mustafa Gökhan Uzunbas, Tom Goldstein, Ser-Nam Lim, Larry S. Davis:
DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation. CoRR abs/1804.05827 (2018) - [i41]Sohil Shah, Pallabi Ghosh, Larry S. Davis, Tom Goldstein:
Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation. CoRR abs/1804.10343 (2018) - [i40]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
Linear Spectral Estimators and an Application to Phase Retrieval. CoRR abs/1806.03547 (2018) - [i39]Christoph Studer, Saïd Medjkouh, Emre Gönültas, Tom Goldstein, Olav Tirkkonen:
Channel Charting: Locating Users within the Radio Environment using Channel State Information. CoRR abs/1807.05247 (2018) - [i38]Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein:
Are adversarial examples inevitable? CoRR abs/1809.02104 (2018) - [i37]Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein:
Universal Adversarial Training. CoRR abs/1811.11304 (2018) - 2017
- [j9]Kaipeng Li, Rishi Sharan, Yujun Chen, Tom Goldstein, Joseph R. Cavallaro, Christoph Studer:
Decentralized Baseband Processing for Massive MU-MIMO Systems. IEEE J. Emerg. Sel. Topics Circuits Syst. 7(4): 491-507 (2017) - [j8]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Mikael Coldrey, Tom Goldstein, Christoph Studer:
1-bit Massive MU-MIMO Precoding in VLSI. IEEE J. Emerg. Sel. Topics Circuits Syst. 7(4): 508-522 (2017) - [j7]Richard G. Baraniuk, Thomas A. Goldstein, Aswin C. Sankaranarayanan, Christoph Studer, Ashok Veeraraghavan, Michael B. Wakin:
Compressive Video Sensing: Algorithms, architectures, and applications. IEEE Signal Process. Mag. 34(1): 52-66 (2017) - [j6]Sven Jacobsson, Giuseppe Durisi, Mikael Coldrey, Tom Goldstein, Christoph Studer:
Quantized Precoding for Massive MU-MIMO. IEEE Trans. Commun. 65(11): 4670-4684 (2017) - [c29]Gavin Taylor, Zheng Xu, Tom Goldstein:
Scalable Classifiers with ADMM and Transpose Reduction. AAAI Workshops 2017 - [c28]Rohan Chandra, Ziyuan Zhong, Justin Hontz, Val McCulloch, Christoph Studer, Tom Goldstein:
PhasePack: A phase retrieval library. ACSSC 2017: 1617-1621 - [c27]Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein:
Adaptive ADMM with Spectral Penalty Parameter Selection. AISTATS 2017: 718-727 - [c26]Soham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein:
Automated Inference with Adaptive Batches. AISTATS 2017: 1504-1513 - [c25]Soumyadip Sengupta, Tal Amir, Meirav Galun, Tom Goldstein, David W. Jacobs, Amit Singer, Ronen Basri:
A New Rank Constraint on Multi-view Fundamental Matrices, and Its Application to Camera Location Recovery. CVPR 2017: 2413-2421 - [c24]Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein:
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation. CVPR 2017: 7234-7243 - [c23]Carlos Domingo Castillo, Soham De, Xintong Han, Bharat Singh, Abhay Kumar Yadav, Tom Goldstein:
Son of Zorn's lemma: Targeted style transfer using instance-aware semantic segmentation. ICASSP 2017: 1348-1352 - [c22]Oscar Castañeda, Tom Goldstein, Christoph Studer:
POKEMON: A non-linear beamforming algorithm for 1-bit massive MIMO. ICASSP 2017: 3464-3468 - [c21]Tom Goldstein, Christoph Studer:
Convex Phase Retrieval without Lifting via PhaseMax. ICML 2017: 1273-1281 - [c20]Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein:
Adaptive Consensus ADMM for Distributed Optimization. ICML 2017: 3841-3850 - [c19]Oscar Castañeda, Tom Goldstein, Christoph Studer:
FPGA design of low-complexity joint channel estimation and data detection for large SIMO wireless systems. ISCAS 2017: 1-4 - [c18]Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein:
Training Quantized Nets: A Deeper Understanding. NIPS 2017: 5811-5821 - [i36]Carlos Domingo Castillo, Soham De, Xintong Han, Bharat Singh, Abhay Kumar Yadav, Tom Goldstein:
Son of Zorn's Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation. CoRR abs/1701.02357 (2017) - [i35]Soumyadip Sengupta, Hao Zhou, Walter Forkel, Ronen Basri, Tom Goldstein, David W. Jacobs:
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability. CoRR abs/1702.00506 (2017) - [i34]Soumyadip Sengupta, Tal Amir, Meirav Galun, Tom Goldstein, David W. Jacobs, Amit Singer, Ronen Basri:
A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery. CoRR abs/1702.03023 (2017) - [i33]Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Mikael Coldrey, Tom Goldstein, Christoph Studer:
1-bit Massive MU-MIMO Precoding in VLSI. CoRR abs/1702.03449 (2017) - [i32]Kaipeng Li, Rishi Sharan, Yujun Chen, Tom Goldstein, Joseph R. Cavallaro, Christoph Studer:
Decentralized Baseband Processing for Massive MU-MIMO Systems. CoRR abs/1702.04458 (2017) - [i31]Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein:
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation. CoRR abs/1704.02712 (2017) - [i30]Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein:
Stabilizing Adversarial Nets With Prediction Methods. CoRR abs/1705.07364 (2017) - [i29]Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein:
Training Quantized Nets: A Deeper Understanding. CoRR abs/1706.02379 (2017) - [i28]Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein:
Adaptive Consensus ADMM for Distributed Optimization. CoRR abs/1706.02869 (2017) - [i27]Oscar Castañeda, Tom Goldstein, Christoph Studer:
VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems. CoRR abs/1709.07860 (2017) - [i26]Rohan Chandra, Ziyuan Zhong, Justin Hontz, Val McCulloch, Christoph Studer, Tom Goldstein:
PhasePack User Guide. CoRR abs/1711.09777 (2017) - [i25]Rohan Chandra, Ziyuan Zhong, Justin Hontz, Val McCulloch, Christoph Studer, Tom Goldstein:
PhasePack: A Phase Retrieval Library. CoRR abs/1711.10175 (2017) - [i24]Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein:
Visualizing the Loss Landscape of Neural Nets. CoRR abs/1712.09913 (2017) - 2016
- [j5]Oscar Castañeda, Tom Goldstein, Christoph Studer:
Data Detection in Large Multi-Antenna Wireless Systems via Approximate Semidefinite Relaxation. IEEE Trans. Circuits Syst. I Regul. Pap. 63-I(12): 2334-2346 (2016) - [c17]Kaipeng Li, Yujun Chen, Rishi Sharan, Tom Goldstein, Joseph R. Cavallaro, Christoph Studer:
Decentralized data detection for massive MU-MIMO on a Xeon Phi cluster. ACSSC 2016: 468-472 - [c16]Sven Jacobsson, Giuseppe Durisi, Mikael Coldrey, Tom Goldstein, Christoph Studer:
Nonlinear 1-bit precoding for massive MU-MIMO with higher-order modulation. ACSSC 2016: 763-767 - [c15]Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre:
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. AISTATS 2016: 1151-1158 - [c14]Sohil Shah, Tom Goldstein, Christoph Studer:
Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity. CVPR 2016: 5906-5915 - [c13]Thomas A. Goldstein, Paul Hand, Choongbum Lee, Vladislav Voroninski, Stefano Soatto:
ShapeFit and ShapeKick for Robust, Scalable Structure from Motion. ECCV (7) 2016: 289-304 - [c12]Sohil Shah, Abhay Kumar Yadav, Carlos Domingo Castillo, David W. Jacobs, Christoph Studer, Tom Goldstein:
Biconvex Relaxation for Semidefinite Programming in Computer Vision. ECCV (6) 2016: 717-735 - [c11]Kaipeng Li, Riski Skaran, Yujun Chen, Joseph R. Cavallaro, Tom Goldstein, Christoph Studer:
Decentralized beamforming for massive MU-MIMO on a GPU cluster. GlobalSIP 2016: 590-594 - [c10]Soham De, Tom Goldstein:
Efficient Distributed SGD with Variance Reduction. ICDM 2016: 111-120 - [c9]Andrew S. Lan, Tom Goldstein, Richard G. Baraniuk, Christoph Studer:
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data. ICML 2016: 266-275 - [c8]Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein:
Training Neural Networks Without Gradients: A Scalable ADMM Approach. ICML 2016: 2722-2731 - [c7]Oscar Castañeda, Tom Goldstein, Christoph Studer:
FPGA design of approximate semidefinite relaxation for data detection in large MIMO wireless systems. ISCAS 2016: 2659-2662 - [c6]Raajen Patel, Tom Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk:
Deterministic Column Sampling for Low-Rank Matrix Approximation: Nyström vs. Incomplete Cholesky Decomposition. SDM 2016: 594-602 - [i23]Sohil Shah, Tom Goldstein, Christoph Studer:
Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity. CoRR abs/1605.01813 (2016) - [i22]Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein:
Training Neural Networks Without Gradients: A Scalable ADMM Approach. CoRR abs/1605.02026 (2016) - [i21]Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein:
Adaptive ADMM with Spectral Penalty Parameter Selection. CoRR abs/1605.07246 (2016) - [i20]Sohil Shah, Abhay Kumar Yadav, David W. Jacobs, Christoph Studer, Tom Goldstein:
Biconvex Relaxation for Semidefinite Programming in Computer Vision. CoRR abs/1605.09527 (2016) - [i19]Thomas A. Goldstein, Paul Hand, Choongbum Lee, Vladislav Voroninski, Stefano Soatto:
ShapeFit and ShapeKick for Robust, Scalable Structure from Motion. CoRR abs/1608.02165 (2016) - [i18]Oscar Castañeda, Tom Goldstein, Christoph Studer:
Data Detection in Large Multi-Antenna Wireless Systems via Approximate Semidefinite Relaxation. CoRR abs/1609.01797 (2016) - [i17]Soham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein:
Big Batch SGD: Automated Inference using Adaptive Batch Sizes. CoRR abs/1610.05792 (2016) - [i16]Tom Goldstein, Christoph Studer:
PhaseMax: Convex Phase Retrieval via Basis Pursuit. CoRR abs/1610.07531 (2016) - [i15]Sven Jacobsson, Giuseppe Durisi, Mikael Coldrey, Tom Goldstein, Christoph Studer:
Quantized Precoding for Massive MU-MIMO. CoRR abs/1610.07564 (2016) - [i14]Sven Jacobsson, Giuseppe Durisi, Mikael Coldrey, Tom Goldstein, Christoph Studer:
Nonlinear 1-Bit Precoding for Massive MU-MIMO with Higher-Order Modulation. CoRR abs/1612.02685 (2016) - [i13]Zheng Xu, Soham De, Mário A. T. Figueiredo, Christoph Studer, Tom Goldstein:
An Empirical Study of ADMM for Nonconvex Problems. CoRR abs/1612.03349 (2016) - [i12]Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein:
Non-negative Factorization of the Occurrence Tensor from Financial Contracts. CoRR abs/1612.03350 (2016) - 2015
- [j4]Tom Goldstein, Lina Xu, Kevin F. Kelly, Richard G. Baraniuk:
The STOne Transform: Multi-Resolution Image Enhancement and Compressive Video. IEEE Trans. Image Process. 24(12): 5581-5593 (2015) - [c5]Zheng Xu, Xue Li, Kuiyuan Yang, Thomas A. Goldstein:
Exploiting Low-rank Structure for Discriminative Sub-categorization. BMVC 2015: 149.1-149.12 - [c4]Bharat Singh, Soham De, Yangmuzi Zhang, Thomas A. Goldstein, Gavin Taylor:
Layer-Specific Adaptive Learning Rates for Deep Networks. ICMLA 2015: 364-368 - [c3]Tom Goldstein, Min Li, Xiaoming Yuan:
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing. NIPS 2015: 2089-2097 - [i11]Tom Goldstein, Christoph Studer, Richard G. Baraniuk:
FASTA: A Generalized Implementation of Forward-Backward Splitting. CoRR abs/1501.04979 (2015) - [i10]Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre:
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. CoRR abs/1504.02147 (2015) - [i9]Eva L. Dyer, Tom Goldstein, Raajen Patel, Konrad P. Körding, Richard G. Baraniuk:
Self-Expressive Decompositions for Matrix Approximation and Clustering. CoRR abs/1505.00824 (2015) - [i8]Raajen Patel, Thomas A. Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk:
oASIS: Adaptive Column Sampling for Kernel Matrix Approximation. CoRR abs/1505.05208 (2015) - [i7]Bharat Singh, Soham De, Yangmuzi Zhang, Thomas A. Goldstein, Gavin Taylor:
Layer-Specific Adaptive Learning Rates for Deep Networks. CoRR abs/1510.04609 (2015) - [i6]Soham De, Gavin Taylor, Tom Goldstein:
Variance Reduction for Distributed Stochastic Gradient Descent. CoRR abs/1512.01708 (2015) - [i5]Soham De, Gavin Taylor, Tom Goldstein:
Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction. CoRR abs/1512.02970 (2015) - 2014
- [j3]Tom Goldstein, Brendan O'Donoghue, Simon Setzer, Richard G. Baraniuk:
Fast Alternating Direction Optimization Methods. SIAM J. Imaging Sci. 7(3): 1588-1623 (2014) - [i4]Christoph Studer, Tom Goldstein, Wotao Yin, Richard G. Baraniuk:
Democratic Representations. CoRR abs/1401.3420 (2014) - [i3]Tom Goldstein, Christoph Studer, Richard G. Baraniuk:
A Field Guide to Forward-Backward Splitting with a FASTA Implementation. CoRR abs/1411.3406 (2014) - [i2]Ali Ayremlou, Thomas A. Goldstein, Ashok Veeraraghavan, Richard G. Baraniuk:
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit. CoRR abs/1412.0680 (2014) - 2013
- [c2]Amirali Aghazadeh, Ali Ayremlou, Daniel D. Calderon, Tom Goldstein, Raajen Patel, Divyanshu Vats, Richard G. Baraniuk:
Adaptive step size selection for optimization via the ski rental problem. ICASSP 2013: 5383-5387 - [i1]Tom Goldstein, Lina Xu, Kevin F. Kelly, Richard G. Baraniuk:
The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video. CoRR abs/1311.3405 (2013) - 2010
- [j2]Tom Goldstein, Xavier Bresson, Stanley J. Osher:
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction. J. Sci. Comput. 45(1-3): 272-293 (2010)
2000 – 2009
- 2009
- [j1]Tom Goldstein, Stanley J. Osher:
The Split Bregman Method for L1-Regularized Problems. SIAM J. Imaging Sci. 2(2): 323-343 (2009) - 2004
- [c1]Tom Goldstein, Antony W. Rix:
Perceptual speech quality assessment in acoustic and binaural applications. ICASSP (3) 2004: 1064-1067
Coauthor Index
Ping-Yeh Chiang
aka: Ping-yeh Chiang
aka: Ping-yeh Chiang
Larry Davis 0001
aka: Larry S. Davis
aka: Larry S. Davis
John Dickerson 0001
aka: John P. Dickerson
aka: John P. Dickerson
Liam Fowl
aka: Liam H. Fowl
aka: Liam H. Fowl
[c148] [c146] [c145] [c144] [c140] [i199] [i196] [i195] [i194] [i193] [i192] [i190] [i187] [i184] [c136] [c135] [c134] [c133] [c132] [c131] [c130] [c127] [c126] [c124] [c123] [c121] [c120] [c118] [c117] [c116] [c115] [c114] [i177] [i175] [i174] [i172] [i171] [i170] [i169] [i168] [i166] [i165] [i164] [i163] [i162] [i161] [i159] [i158] [i157] [c110] [c107] [c106] [c100] [c96] [i152] [i150] [i146] [i145] [i141] [i139] [i138] [i137] [i136] [i134] [i132] [c90] [c88] [c75] [i125] [i124] [i123] [i114] [i108] [i106] [c64] [c60] [c48] [i92] [i84] [i69] [i52] [i46]
[c146] [c145] [c144] [c140] [i199] [i194] [i192] [i180] [j18] [j17] [c138] [c136] [c135] [c134] [c133] [c132] [c131] [c130] [c128] [c127] [c126] [c125] [c121] [c120] [c119] [c118] [c116] [c115] [i176] [i175] [i174] [i171] [i170] [i168] [i166] [i165] [i163] [i162] [i161] [i160] [i159] [i158] [i156] [c113] [c110] [c109] [c107] [c106] [c105] [c104] [c101] [c100] [c99] [c97] [c96] [c94] [i152] [i151] [i150] [i149] [i148] [i147] [i146] [i145] [i143] [i141] [i139] [i138] [i137] [i136] [i134] [i133] [c91] [c90] [c89] [c86] [c85] [c84] [c81] [c76] [c75] [i131] [i130] [i127] [i125] [i124] [i123] [i120] [i119] [i118] [i116] [i115] [i114] [i112] [i111] [i109] [i108] [i107] [i106] [i101] [c73] [c64] [c63] [c60] [c56] [c49] [i97] [i96] [i87] [i78] [i77] [i75] [i74] [i69] [i68] [i67] [i60] [i58] [i52] [i51] [i46]
David Jacobs 0001
aka: David W. Jacobs
aka: David W. Jacobs
[j16] [c92] [c87] [i103] [i100] [j15] [j14] [c71] [c69] [c66] [c65] [c62] [c59] [i99] [i94] [i91] [i85] [i83] [i82] [i81] [i80] [c46] [c45] [c42] [c41] [c40] [c39] [i63] [i62] [i61] [i56] [i54] [j13] [j11] [j10] [c38] [c36] [c34] [c33] [c32] [c31] [i45] [i44] [i43] [i40] [i39] [i38] [j9] [j8] [j7] [j6] [c28] [c24] [c22] [c21] [c19] [c18] [i33] [i32] [i31] [i29] [i27] [i26] [i25] [j5] [c17] [c16] [c14] [c12] [c11] [c9] [c7] [i23] [i20] [i18] [i16] [i15] [i14] [i13] [i11] [i4] [i3]
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