| A Unified View of Label Shift Estimation S Garg, Y Wu, S Balakrishnan, ZC Lipton Advances in Neural Information Processing Systems (NeurIPS) 2020, 2020 | 143 | 2020 |
| Leveraging Unlabeled Data to Predict Out-of-Distribution Performance S Garg, S Balakrishnan, ZC Lipton, B Neyshabur, H Sedghi International Conference on Machine Learning (ICLR), 2022, 2022 | 113 | 2022 |
| Chils: Zero-shot image classification with hierarchical label sets Z Novack, J McAuley, ZC Lipton, S Garg International Conference of Machine Learning (ICML) 2023, 2023 | 58 | 2023 |
| Mixture Proportion Estimation and PU Learning:A Modern Approach S Garg, Y Wu, A Smola, S Balakrishnan, ZC Lipton Advances in Neural Information Processing (NeurIPS) 2021, Spotlight, 2021 | 53 | 2021 |
| Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus U Sawant, S Garg, S Chakrabarti, G Ramakrishnan Information Retrieval Journal, 2019, 2018 | 48 | 2018 |
| Code-switched language models using dual rnns and same-source pretraining S Garg, T Parekh, P Jyothi Empirical Methods in Natural Language Processing (EMNLP), 2018, 2018 | 42 | 2018 |
| Domain adaptation under open set label shift S Garg, S Balakrishnan, ZC Lipton Advances in Neural Information Processing (NeurIPS) 2022, 2022 | 30 | 2022 |
| RATT: Leveraging Unlabeled Data to Guarantee Generalization S Garg, S Balakrishnan, JZ Kolter, ZC Lipton International Conference on Machine Learning (ICML) 2021, Oral, 2021 | 26 | 2021 |
| Characterizing Datapoints via Second-Split Forgetting P Maini, S Garg, ZC Lipton, JZ Kolter Advances in Neural Information Processing (NeurIPS) 2022, 2022 | 24 | 2022 |
| Downstream datasets make surprisingly good pretraining corpora K Krishna, S Garg, JP Bigham, ZC Lipton Association for Computational Linguistics (ACL), 2023, 2022 | 20 | 2022 |
| RLSbench: Domain Adaptation Under Relaxed Label Shift S Garg, N Erickson, J Sharpnack, A Smola, S Balakrishnan, ZC Lipton International Conference of Machine Learning (ICML) 2023, 2023 | 19* | 2023 |
| Deconstructing distributions: A pointwise framework of learning G Kaplun, N Ghosh, S Garg, B Barak, P Nakkiran International Conference on Learning Representations (ICLR) 2023, 2022 | 19 | 2022 |
| Dual Language Models for Code Mixed Speech Recognition S Garg, T Parekh, P Jyothi Proceedings of Interspeech 2018 (19th Annual Conference of ISCA), 2018 | 19* | 2018 |
| Estimating uncertainty in MRF-based image segmentation: A perfect-MCMC approach SP Awate, S Garg, R Jena Medical image analysis (MedIA) 55, 181-196, 2019 | 12 | 2019 |
| On Proximal Policy Optimization's Heavy-tailed Gradients S Garg, J Zhanson, E Parisotto, A Prasad, JZ Kolter, S Balakrishnan, ... International Conference on Machine Learning 139 (38), 3598-3609, 2021 | 10 | 2021 |
| Tic-clip: Continual training of clip models S Garg, M Farajtabar, H Pouransari, R Vemulapalli, S Mehta, O Tuzel, ... arXiv preprint arXiv:2310.16226, 2023 | 7 | 2023 |
| Unsupervised Learning under Latent Label Shift M Roberts, P Mani, S Garg, ZC Lipton Advances in Neural Information Processing (NeurIPS) 2022, 2022 | 7 | 2022 |
| Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms D Baby*, S Garg*, TC Yen*, S Balakrishnan, ZC Lipton, YX Wang Advances in Neural Information Processing Systems (NeurIPS), 2023, Spotlight, 2023 | 5 | 2023 |
| Perfect MCMC sampling in Bayesian MRFs for uncertainty estimation in segmentation S Garg, SP Awate Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018 …, 2018 | 5 | 2018 |
| Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift S Garg, A Setlur, ZC Lipton, S Balakrishnan, V Smith, A Raghunathan Advances in Neural Information Processing Systems (NeurIPS), 2023, 2023 | 4 | 2023 |
Zachary C. LiptonRaj Reddy Associate Professor of Machine Learning @ Carnegie Mellon University; CTO + CSO @ Abridge在 cmu.edu 的电子邮件经过验证
