• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 190 matches

• 2024
• 
  Pedram Akbarian, Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho:
  Improving Computational Complexity in Statistical Models with Local Curvature Information. ICML 2024
• 
  Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal, Sujay Sanghavi:
  Understanding the Training Speedup from Sampling with Approximate Losses. ICML 2024
• 
  Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep P. Chinchali:
  Time Weaver: A Conditional Time Series Generation Model. ICML 2024
• 
  Anish Acharya, Sujay Sanghavi:
  Contrastive Approach to Prior Free Positive Unlabeled Learning. CoRR abs/2402.06038 (2024)
• 
  Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal, Sujay Sanghavi:
  Understanding the Training Speedup from Sampling with Approximate Losses. CoRR abs/2402.07052 (2024)
• 
  Rudrajit Das, Naman Agarwal, Sujay Sanghavi, Inderjit S. Dhillon:
  Towards Quantifying the Preconditioning Effect of Adam. CoRR abs/2402.07114 (2024)
• 
  Liam Collins, Advait Parulekar, Aryan Mokhtari, Sujay Sanghavi, Sanjay Shakkottai:
  In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness. CoRR abs/2402.11639 (2024)
• 
  Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep Chinchali:
  Time Weaver: A Conditional Time Series Generation Model. CoRR abs/2403.02682 (2024)
• 
  Sunny Sanyal, Sujay Sanghavi, Alexandros G. Dimakis:
  Pre-training Small Base LMs with Fewer Tokens. CoRR abs/2404.08634 (2024)
• 
  Vijay Lingam, Atula Tejaswi, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Alex Dimakis, Eunsol Choi, Aleksandar Bojchevski, Sujay Sanghavi:
  SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors. CoRR abs/2405.19597 (2024)
• 
  Ruichen Jiang, Ali Kavis, Qiujiang Jin, Sujay Sanghavi, Aryan Mokhtari:
  Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization. CoRR abs/2406.02016 (2024)
• 
  Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong:
  Retraining with Predicted Hard Labels Provably Increases Model Accuracy. CoRR abs/2406.11206 (2024)
• 
  Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar:
  DataComp-LM: In search of the next generation of training sets for language models. CoRR abs/2406.11794 (2024)
• 
  Anish Acharya, Inderjit S. Dhillon, Sujay Sanghavi:
  Geometric Median (GM) Matching for Robust Data Pruning. CoRR abs/2406.17188 (2024)
• 2023
• 
  Shuo Yang, Yijun Dong, Rachel A. Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei:
  Sample Efficiency of Data Augmentation Consistency Regularization. AISTATS 2023: 3825-3853
• 
  Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai:
  Latent Variable Representation for Reinforcement Learning. ICLR 2023
• 
  Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
  Beyond Uniform Lipschitz Condition in Differentially Private Optimization. ICML 2023: 7066-7101
• 
  Rudrajit Das, Sujay Sanghavi:
  Understanding Self-Distillation in the Presence of Label Noise. ICML 2023: 7102-7140
• 
  Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi:
  Logarithmic Bayes Regret Bounds. NeurIPS 2023
• 
  Rudrajit Das, Sujay Sanghavi:
  Understanding Self-Distillation in the Presence of Label Noise. CoRR abs/2301.13304 (2023)
• 
  Sunny Sanyal, Jean Kaddour, Abhishek Kumar, Sujay Sanghavi:
  Understanding the Effectiveness of Early Weight Averaging for Training Large Language Models. CoRR abs/2306.03241 (2023)
• 
  Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi:
  Logarithmic Bayes Regret Bounds. CoRR abs/2306.09136 (2023)
• 
  Charlie Hou, Kiran Koshy Thekumparampil, Michael Shavlovsky, Giulia Fanti, Yesh Dattatreya, Sujay Sanghavi:
  Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity. CoRR abs/2308.00177 (2023)
• 2022
• 
  Abolfazl Hashemi, Anish Acharya, Rudrajit Das, Haris Vikalo, Sujay Sanghavi, Inderjit S. Dhillon:
  On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. IEEE Trans. Parallel Distributed Syst. 33(11): 2727-2739 (2022)
• 
  Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
  Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. AISTATS 2022: 11145-11168
• 
  Tongzheng Ren, Fuheng Cui, Alexia Atsidakou, Sujay Sanghavi, Nhat Ho:
  Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. AISTATS 2022: 3930-3961
• 
  Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai:
  Asymptotically-Optimal Gaussian Bandits with Side Observations. ICML 2022: 1057-1077
• 
  Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi:
  Linear Bandit Algorithms with Sublinear Time Complexity. ICML 2022: 25241-25260
• 
  Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant:
  Minimax Regret for Cascading Bandits. NeurIPS 2022
• 
  Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan:
  Toward Understanding Privileged Features Distillation in Learning-to-Rank. NeurIPS 2022

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