Garg, Saurabh

32 publications

TMLR 2025 Expert Routing with Synthetic Data for Domain Incremental Learning Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell, Michael Oberst, Bryan Wilder, Zachary Chase Lipton

NeurIPS 2024 DataComp-LM: In Search of the Next Generation of Training Sets for Language Models Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash 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 Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Chandu, Thao Nguyen, Igor Vasiljevic, Sham 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

ICMLW 2024 Learning to Reason by Failing: Offline RL on Sub-Optimal Rollouts Scales Synthetic Data by 8x Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar

NeurIPS 2024 Post-Hoc Reversal: Are We Selecting Models Prematurely? Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, Zachary Lipton

ICML 2024 Prompting Is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine

ICLRW 2024 Prompting for Robustness: Extracting Robust Classifiers from Foundation Models Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine

NeurIPS 2024 RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar

ICLR 2024 TiC-CLIP: Continual Training of CLIP Models Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli, Sachin Mehta, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri

NeurIPS 2023 (Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy Elan Rosenfeld, Saurabh Garg

ICMLW 2023 (Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy Elan Rosenfeld, Saurabh Garg

ICML 2023 CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets Zachary Novack, Julian Mcauley, Zachary Chase Lipton, Saurabh Garg

ICLRW 2023 CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets Zachary Novack, Julian McAuley, Zachary Chase Lipton, Saurabh Garg

NeurIPS 2023 Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift Saurabh Garg, Amrith Setlur, Zachary Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan

ICMLW 2023 Conditional Diffusion Replay for Continual Learning in Medical Settings Yewon Byun, Saurabh Garg, Sanket Vaibhav Mehta, Praveer Singh, Jayashree Kalpathy-cramer, Bryan Wilder, Zachary Chase Lipton

ICLR 2023 Deconstructing Distributions: A Pointwise Framework of Learning Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran

ICLR 2023 Disentangling the Mechanisms Behind Implicit Regularization in SGD Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary Chase Lipton

NeurIPS 2023 Online Label Shift: Optimal Dynamic Regret Meets Practical Algorithms Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Lipton, Yu-Xiang Wang

ICML 2023 RLSbench: Domain Adaptation Under Relaxed Label Shift Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton

NeurIPSW 2023 TiC-CLIP: Continual Training of CLIP Models Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli, Sachin Mehta, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri

NeurIPS 2022 Characterizing Datapoints via Second-Split Forgetting Pratyush Maini, Saurabh Garg, Zachary Lipton, J. Zico Kolter

ICMLW 2022 Characterizing Datapoints via Second-Split Forgetting Pratyush Maini, Saurabh Garg, Zachary Chase Lipton, J Zico Kolter

NeurIPS 2022 Domain Adaptation Under Open Set Label Shift Saurabh Garg, Sivaraman Balakrishnan, Zachary Lipton

ICMLW 2022 Domain Adaptation Under Open Set Label Shift Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton

ICLR 2022 Leveraging Unlabeled Data to Predict Out-of-Distribution Performance Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi

NeurIPSW 2022 RLSBench: A Large-Scale Empirical Study of Domain Adaptation Under Relaxed Label Shift Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton

NeurIPS 2022 Unsupervised Learning Under Latent Label Shift Manley Roberts, Pranav Mani, Saurabh Garg, Zachary Lipton

ICMLW 2022 Unsupervised Learning Under Latent Label Shift Pranav Mani, Manley Roberts, Saurabh Garg, Zachary Chase Lipton

NeurIPSW 2021 Leveraging Unlabeled Data to Predict Out-of-Distribution Performance Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi

NeurIPS 2021 Mixture Proportion Estimation and PU Learning:A Modern Approach Saurabh Garg, Yifan Wu, Alexander J Smola, Sivaraman Balakrishnan, Zachary Lipton

ICML 2021 On Proximal Policy Optimization’s Heavy-Tailed Gradients Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, Zico Kolter, Zachary Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar

ICML 2021 RATT: Leveraging Unlabeled Data to Guarantee Generalization Saurabh Garg, Sivaraman Balakrishnan, Zico Kolter, Zachary Lipton

NeurIPS 2020 A Unified View of Label Shift Estimation Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary Lipton