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2020 – today
- 2024
[j4]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-trained Gaussian Processes for Bayesian Optimization. J. Mach. Learn. Res. 25: 212:1-212:83 (2024)- [j3]Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c37]Kevin Clark, Paul Vicol, Kevin Swersky, David J. Fleet:
Directly Fine-Tuning Diffusion Models on Differentiable Rewards. ICLR 2024 - [i45]Cristina Nader Vasconcelos, Abdullah Rashwan, Austin Waters, Trevor Walker, Keyang Xu, Jimmy Yan, Rui Qian, Shixin Luo, Zarana Parekh, Andrew Bunner, Hongliang Fei, Roopal Garg, Mandy Guo, Ivana Kajic, Yeqing Li, Henna Nandwani, Jordi Pont-Tuset, Yasumasa Onoe, Sarah Rosston, Su Wang, Wenlei Zhou, Kevin Swersky, David J. Fleet, Jason M. Baldridge, Oliver Wang:
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models. CoRR abs/2405.16759 (2024) - [i44]Bernd Bohnet, Kevin Swersky, Rosanne Liu, Pranjal Awasthi, Azade Nova, Javier Snaider, Hanie Sedghi, Aaron T. Parisi, Michael Collins, Angeliki Lazaridou, Orhan Firat, Noah Fiedel:
Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation. CoRR abs/2406.00179 (2024) - [i43]Bernd Bohnet, Azade Nova, Aaron T. Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi:
Exploring and Benchmarking the Planning Capabilities of Large Language Models. CoRR abs/2406.13094 (2024) - [i42]Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith:
Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR abs/2408.07852 (2024) - 2023
- [j2]Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao:
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. Trans. Mach. Learn. Res. 2023 (2023) - [c36]Cristina Nader Vasconcelos, A. Cengiz Öztireli, Mark J. Matthews
, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi:
CUF: Continuous Upsampling Filters. CVPR 2023: 9999-10008 - [i41]Paul Vicol, Zico Kolter, Kevin Swersky:
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single. CoRR abs/2304.11153 (2023) - [i40]Kevin Clark, Paul Vicol, Kevin Swersky, David J. Fleet:
Directly Fine-Tuning Diffusion Models on Differentiable Rewards. CoRR abs/2309.17400 (2023) - [i39]C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein:
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR abs/2311.07587 (2023) - [i38]Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR abs/2312.06585 (2023) - 2022
- [c35]Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra:
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. ICDM 2022: 1287-1292 - [c34]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization for Architecting Hardware Accelerators. ICLR 2022 - [i37]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-training helps Bayesian optimization too. CoRR abs/2207.03084 (2022) - [i36]Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Ariel Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan:
Learning to Improve Code Efficiency. CoRR abs/2208.05297 (2022) - [i35]Cristina Nader Vasconcelos, Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi:
CUF: Continuous Upsampling Filters. CoRR abs/2210.06965 (2022) - [i34]Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao:
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. CoRR abs/2211.00692 (2022) - 2021
- [c33]Zhan Shi, Akanksha Jain, Kevin Swersky, Milad Hashemi, Parthasarathy Ranganathan, Calvin Lin:
A hierarchical neural model of data prefetching. ASPLOS 2021: 861-873 - [c32]Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud:
No MCMC for me: Amortized sampling for fast and stable training of energy-based models. ICLR 2021 - [c31]Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison:
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. ICML 2021: 3831-3841 - [i33]Amir Yazdanbakhsh, Christof Angermüller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon:
Apollo: Transferable Architecture Exploration. CoRR abs/2102.01723 (2021) - [i32]Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison:
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. CoRR abs/2102.04509 (2021) - [i31]Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra:
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. CoRR abs/2102.06462 (2021) - [i30]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. CoRR abs/2109.08215 (2021) - [i29]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization For Architecting Hardware Accelerators. CoRR abs/2110.11346 (2021) - 2020
- [c30]Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky:
Your classifier is secretly an energy based model and you should treat it like one. ICLR 2020 - [c29]Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi:
Learning Execution through Neural Code fusion. ICLR 2020 - [c28]Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle:
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. ICLR 2020 - [c27]Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn:
An Imitation Learning Approach for Cache Replacement. ICML 2020: 6237-6247 - [c26]Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier:
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. ICML 2020: 6987-6998 - [c25]Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton:
Big Self-Supervised Models are Strong Semi-Supervised Learners. NeurIPS 2020 - [c24]Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi:
Neural Execution Engines: Learning to Execute Subroutines. NeurIPS 2020 - [c23]Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy:
Amortized Bayesian Optimization over Discrete Spaces. UAI 2020: 769-778 - [i28]Micha Livne, Kevin Swersky, David J. Fleet:
SentenceMIM: A Latent Variable Language Model. CoRR abs/2003.02645 (2020) - [i27]Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi:
Neural Execution Engines: Learning to Execute Subroutines. CoRR abs/2006.08084 (2020) - [i26]Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton:
Big Self-Supervised Models are Strong Semi-Supervised Learners. CoRR abs/2006.10029 (2020) - [i25]Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn:
An Imitation Learning Approach for Cache Replacement. CoRR abs/2006.16239 (2020) - [i24]Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier:
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. CoRR abs/2008.00104 (2020) - [i23]Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin:
Learned Hardware/Software Co-Design of Neural Accelerators. CoRR abs/2010.02075 (2020) - [i22]Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud:
No MCMC for me: Amortized sampling for fast and stable training of energy-based models. CoRR abs/2010.04230 (2020) - [i21]Francis Williams, Or Litany, Avneesh Sud, Kevin Swersky, Andrea Tagliasacchi:
Human 3D keypoints via spatial uncertainty modeling. CoRR abs/2012.10518 (2020)
2010 – 2019
- 2019
- [c22]Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel:
Flexibly Fair Representation Learning by Disentanglement. ICML 2019: 1436-1445 - [c21]Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky:
Graph Normalizing Flows. NeurIPS 2019: 13556-13566 - [i20]Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle:
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. CoRR abs/1903.03096 (2019) - [i19]Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow:
Neural Networks for Modeling Source Code Edits. CoRR abs/1904.02818 (2019) - [i18]Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky:
Graph Normalizing Flows. CoRR abs/1905.13177 (2019) - [i17]Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton:
Learning Sparse Networks Using Targeted Dropout. CoRR abs/1905.13678 (2019) - [i16]Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel:
Flexibly Fair Representation Learning by Disentanglement. CoRR abs/1906.02589 (2019) - [i15]Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi:
Learning Execution through Neural Code Fusion. CoRR abs/1906.07181 (2019) - [i14]Micha Livne, Kevin Swersky, David J. Fleet:
MIM: Mutual Information Machine. CoRR abs/1910.03175 (2019) - [i13]Micha Livne, Kevin Swersky, David J. Fleet:
High Mutual Information in Representation Learning with Symmetric Variational Inference. CoRR abs/1910.04153 (2019) - [i12]Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky:
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One. CoRR abs/1912.03263 (2019) - 2018
- [c20]Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly:
Learning Hard Alignments with Variational Inference. ICASSP 2018: 5799-5803 - [c19]Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel:
Meta-Learning for Semi-Supervised Few-Shot Classification. ICLR (Poster) 2018 - [c18]Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan:
Learning Memory Access Patterns. ICML 2018: 1924-1933 - [i11]Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel:
Meta-Learning for Semi-Supervised Few-Shot Classification. CoRR abs/1803.00676 (2018) - [i10]Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan:
Learning Memory Access Patterns. CoRR abs/1803.02329 (2018) - 2017
- [b1]Kevin Swersky:
Improving Bayesian Optimization for Machine Learning using Expert Priors. University of Toronto, Canada, 2017 - [c17]Jake Snell, Kevin Swersky, Richard S. Zemel:
Prototypical Networks for Few-shot Learning. NIPS 2017: 4077-4087 - [i9]Jake Snell, Kevin Swersky, Richard S. Zemel:
Prototypical Networks for Few-shot Learning. CoRR abs/1703.05175 (2017) - [i8]Dieterich Lawson, George Tucker, Chung-Cheng Chiu, Colin Raffel, Kevin Swersky, Navdeep Jaitly:
Learning Hard Alignments with Variational Inference. CoRR abs/1705.05524 (2017) - [i7]Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly:
An online sequence-to-sequence model for noisy speech recognition. CoRR abs/1706.06428 (2017) - 2016
- [j1]Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas:
Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 104(1): 148-175 (2016) - [c16]Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel:
The Variational Fair Autoencoder. ICLR 2016 - 2015
- [c15]Lei Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov:
Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions. ICCV 2015: 4247-4255 - [c14]Yujia Li, Kevin Swersky, Richard S. Zemel:
Generative Moment Matching Networks. ICML 2015: 1718-1727 - [c13]Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams:
Scalable Bayesian Optimization Using Deep Neural Networks. ICML 2015: 2171-2180 - [i6]Yujia Li, Kevin Swersky, Richard S. Zemel:
Generative Moment Matching Networks. CoRR abs/1502.02761 (2015) - [i5]Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov:
Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions. CoRR abs/1506.00511 (2015) - 2014
- [c12]Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams:
Input Warping for Bayesian Optimization of Non-Stationary Functions. ICML 2014: 1674-1682 - [i4]Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams:
Input Warping for Bayesian Optimization of Non-stationary Functions. CoRR abs/1402.0929 (2014) - [i3]Kevin Swersky, Jasper Snoek, Ryan Prescott Adams:
Freeze-Thaw Bayesian Optimization. CoRR abs/1406.3896 (2014) - [i2]Yujia Li, Kevin Swersky, Richard S. Zemel:
Learning unbiased features. CoRR abs/1412.5244 (2014) - 2013
- [c11]Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel:
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. ICML (3) 2013: 199-207 - [c10]Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork:
Learning Fair Representations. ICML (3) 2013: 325-333 - [c9]Kevin Swersky, Jasper Snoek, Ryan Prescott Adams:
Multi-Task Bayesian Optimization. NIPS 2013: 2004-2012 - 2012
- [c8]Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas:
Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults. AAAI 2012: 349-355 - [c7]James Martens, Ilya Sutskever, Kevin Swersky:
Estimating the Hessian by Back-propagating Curvature. ICML 2012 - [c6]Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey:
Probabilistic n-Choose-k Models for Classification and Ranking. NIPS 2012: 3059-3067 - [c5]Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams:
Cardinality Restricted Boltzmann Machines. NIPS 2012: 3302-3310 - [c4]Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey:
Fast Exact Inference for Recursive Cardinality Models. UAI 2012: 825-834 - [i1]Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey:
Fast Exact Inference for Recursive Cardinality Models. CoRR abs/1210.4899 (2012) - 2011
- [c3]Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas:
On Autoencoders and Score Matching for Energy Based Models. ICML 2011: 1201-1208 - 2010
- [c2]Kevin Swersky, Bo Chen, Benjamin M. Marlin, Nando de Freitas:
A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets. ITA 2010: 80-89 - [c1]Benjamin M. Marlin, Kevin Swersky, Bo Chen, Nando de Freitas:
Inductive Principles for Restricted Boltzmann Machine Learning. AISTATS 2010: 509-516
Coauthor Index
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