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Saurabh Garg
This is just a disambiguation page, and is not intended to be the bibliography of an actual person. The links to all actual bibliographies of persons of the same or a similar name can be found below. Any publication listed on this page has not been assigned to an actual author yet. If you know the true author of one of the publications listed below, you are welcome to contact us.
Person information
Other persons with the same name
- Saurabh Kumar Garg 0001
(aka: Saurabh Garg 0001) — University of Tasmania, School of Technology, Environments and Design, Australia
- Saurabh Garg 0002 — University of Southern California, Electrical Engineering Department, Los Angeles, CA, USA
- Saurabh Garg 0003 — Indian Institute of Technology Bombay, Computer Science and Engineering Department, Mumbai, India (and 1 more)
- Saurabh Garg 0004 — University of British Columbia, Pacific Parkinson's Research Centre, Canada
- Saurabh Garg 0005 — Indian Institute of Technology Guwahati, Department of Mechanical Engineering, India
- Saurabh Garg 0006
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2020 – today
- 2024
- [c24]Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst:
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? EMNLP 2024: 12143-12170 - [c23]Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli, Sachin Mehta, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri:
TiC-CLIP: Continual Training of CLIP Models. ICLR 2024 - [c22]Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine:
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models. ICML 2024 - [i21]Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, Zachary Chase Lipton:
Post-Hoc Reversal: Are We Selecting Models Prematurely? CoRR abs/2404.07815 (2024) - [i20]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) - [i19]Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar:
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. CoRR abs/2406.14532 (2024) - [i18]Pravesh Agrawal, Szymon Antoniak, Emma Bou Hanna, Baptiste Bout, Devendra Singh Chaplot, Jessica Chudnovsky, Diogo Costa, Baudouin De Monicault, Saurabh Garg, Théophile Gervet, Soham Ghosh, Amélie Héliou, Paul Jacob, Albert Q. Jiang, Kartik Khandelwal, Timothée Lacroix, Guillaume Lample, Diego de Las Casas, Thibaut Lavril, Teven Le Scao, Andy Lo, William Marshall, Louis Martin, Arthur Mensch, Pavankumar Muddireddy, Valera Nemychnikova, Marie Pellat, Patrick von Platen, Nikhil Raghuraman, Baptiste Rozière, Alexandre Sablayrolles, Lucile Saulnier, Romain Sauvestre, Wendy Shang, Roman Soletskyi, Lawrence Stewart, Pierre Stock, Joachim Studnia, Sandeep Subramanian, Sagar Vaze, Thomas Wang, Sophia Yang:
Pixtral 12B. CoRR abs/2410.07073 (2024) - 2023
- [c21]Kundan Krishna, Saurabh Garg, Jeffrey P. Bigham, Zachary C. Lipton:
Downstream Datasets Make Surprisingly Good Pretraining Corpora. ACL (1) 2023: 12207-12222 - [c20]Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran:
Deconstructing Distributions: A Pointwise Framework of Learning. ICLR 2023 - [c19]Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary Chase Lipton:
Disentangling the Mechanisms Behind Implicit Regularization in SGD. ICLR 2023 - [c18]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. ICML 2023: 10879-10928 - [c17]Zachary Novack, Julian J. McAuley, Zachary Chase Lipton, Saurabh Garg:
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. ICML 2023: 26342-26362 - [c16]Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary C. Lipton, Yu-Xiang Wang:
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. NeurIPS 2023 - [c15]Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan:
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS 2023 - [c14]Elan Rosenfeld, Saurabh Garg:
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy. NeurIPS 2023 - [i17]Zachary Novack, Saurabh Garg, Julian J. McAuley, Zachary C. Lipton:
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. CoRR abs/2302.02551 (2023) - [i16]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. CoRR abs/2302.03020 (2023) - [i15]Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Chase Lipton, Yu-Xiang Wang:
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. CoRR abs/2305.19570 (2023) - [i14]Elan Rosenfeld, Saurabh Garg:
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy. CoRR abs/2306.00312 (2023) - [i13]Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli, Sachin Mehta, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri:
TiC-CLIP: Continual Training of CLIP Models. CoRR abs/2310.16226 (2023) - [i12]Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan:
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR abs/2312.03318 (2023) - 2022
- [c13]Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging unlabeled data to predict out-of-distribution performance. ICLR 2022 - [c12]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Open Set Label Shift. NeurIPS 2022 - [c11]Pratyush Maini, Saurabh Garg, Zachary C. Lipton, J. Zico Kolter:
Characterizing Datapoints via Second-Split Forgetting. NeurIPS 2022 - [c10]Manley Roberts, Pranav Mani, Saurabh Garg, Zachary C. Lipton:
Unsupervised Learning under Latent Label Shift. NeurIPS 2022 - [i11]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance. CoRR abs/2201.04234 (2022) - [i10]Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran:
Deconstructing Distributions: A Pointwise Framework of Learning. CoRR abs/2202.09931 (2022) - [i9]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Open Set Label Shift. CoRR abs/2207.13048 (2022) - [i8]Manley Roberts, Pranav Mani, Saurabh Garg, Zachary C. Lipton:
Unsupervised Learning under Latent Label Shift. CoRR abs/2207.13179 (2022) - [i7]Kundan Krishna, Saurabh Garg, Jeffrey P. Bigham, Zachary C. Lipton:
Downstream Datasets Make Surprisingly Good Pretraining Corpora. CoRR abs/2209.14389 (2022) - [i6]Pratyush Maini, Saurabh Garg, Zachary C. Lipton, J. Zico Kolter:
Characterizing Datapoints via Second-Split Forgetting. CoRR abs/2210.15031 (2022) - [i5]Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary C. Lipton:
Disentangling the Mechanisms Behind Implicit Regularization in SGD. CoRR abs/2211.15853 (2022) - 2021
- [c9]Saurabh Garg, I. Scott MacKenzie:
Comparison of Touch and Touchless Zoom Control Methods for Single-Handed Mobile Interaction. AHFE (7) 2021: 71-78 - [c8]Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton:
RATT: Leveraging Unlabeled Data to Guarantee Generalization. ICML 2021: 3598-3609 - [c7]Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar:
On Proximal Policy Optimization's Heavy-tailed Gradients. ICML 2021: 3610-3619 - [c6]Saurabh Garg, I. Scott MacKenzie
Fingerprint Scroll: Comparison of Touchless and Touch-Based Scroll Navigation Methods. INTERACT (3) 2021: 139-150 - [c5]Saurabh Garg, Yifan Wu, Alexander J. Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
Mixture Proportion Estimation and PU Learning: A Modern Approach. NeurIPS 2021: 8532-8544 - [i4]Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Sivaraman Balakrishnan, Zachary C. Lipton, Ruslan Salakhutdinov, Pradeep Ravikumar:
On Proximal Policy Optimization's Heavy-tailed Gradients. CoRR abs/2102.10264 (2021) - [i3]Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton:
RATT: Leveraging Unlabeled Data to Guarantee Generalization. CoRR abs/2105.00303 (2021) - [i2]Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
Mixture Proportion Estimation and PU Learning: A Modern Approach. CoRR abs/2111.00980 (2021) - 2020
- [c4]Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton:
A Unified View of Label Shift Estimation. NeurIPS 2020 - [i1]Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton:
A Unified View of Label Shift Estimation. CoRR abs/2003.07554 (2020)
2010 – 2019
- 2015
- [c3]Prateek Singh, Saurabh Garg, Vinod Kumar, Zia Saquib:
A testbed for SCADA cyber security and intrusion detection. SSIC 2015: 1-6
2000 – 2009
- 2004
- [j2]Leila Taher
AGenDA: gene prediction by cross-species sequence comparison. Nucleic Acids Res. 32(Web-Server-Issue): 305-308 (2004) - [c2]Saurabh Garg, Bilyana Martinovski, Susan Robinson, Jens Stephan, Joel R. Tetreault, David R. Traum:
Evaluation of Transcription and Annotation Tools for a Multi-modal, Multi-party Dialogue Corpus. LREC 2004 - [c1]Susan Robinson, Bilyana Martinovski, Saurabh Garg, Jens Stephan, David R. Traum:
Issues in Corpus Development for Multi-party Multi-modal Task-oriented Dialogue. LREC 2004 - 2003
- [j1]Leila Taher
AGenDA: homology-based gene prediction. Bioinform. 19(12): 1575-1577 (2003)
Coauthor Index
aka: Zachary Chase Lipton
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last updated on 2024-11-27 21:21 CET by the dblp team
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