| A Unified View of Label Shift Estimation S Garg, Y Wu, S Balakrishnan, ZC Lipton Advances in Neural Information Processing Systems (NeurIPS) 2020, 2020 | 77 | 2020 |
| 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 | 39 | 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 | 36 | 2018 |
| 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 | 33 | 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 | 18* | 2018 |
| 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 | 17 | 2021 |
| RATT: Leveraging Unlabeled Data to Guarantee Generalization S Garg, S Balakrishnan, JZ Kolter, ZC Lipton International Conference on Machine Learning (ICML) 2021, Oral, 2021 | 15 | 2021 |
| 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 | 9 | 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 | 8 | 2021 |
| Deconstructing distributions: A pointwise framework of learning G Kaplun, N Ghosh, S Garg, B Barak, P Nakkiran arXiv preprint arXiv:2202.09931, 2022 | 7 | 2022 |
| 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 |
| Domain adaptation under open set label shift S Garg, S Balakrishnan, ZC Lipton Advances in Neural Information Processing (NeurIPS) 2022, 2022 | 4 | 2022 |
| Characterizing Datapoints via Second-Split Forgetting P Maini, S Garg, ZC Lipton, JZ Kolter Advances in Neural Information Processing (NeurIPS) 2022, 2022 | 3 | 2022 |
| Downstream datasets make surprisingly good pretraining corpora K Krishna, S Garg, JP Bigham, ZC Lipton arXiv preprint arXiv:2209.14389, 2022 | 3 | 2022 |
| Chils: Zero-shot image classification with hierarchical label sets Z Novack, S Garg, J McAuley, ZC Lipton arXiv preprint arXiv:2302.02551, 2023 | 1 | 2023 |
| RLSbench: Domain Adaptation Under Relaxed Label Shift S Garg, N Erickson, J Sharpnack, A Smola, S Balakrishnan, ZC Lipton arXiv preprint arXiv:2302.03020, 2023 | 1* | 2023 |
| Unsupervised Learning under Latent Label Shift M Roberts, P Mani, S Garg, ZC Lipton Advances in Neural Information Processing (NeurIPS) 2022, 2022 | 1 | 2022 |
| End to End Speech Recognition System S Garg Seminar Report, 2017 | 1 | 2017 |
| Disentangling the Mechanisms Behind Implicit Regularization in SGD Z Novack, S Kaur, T Marwah, S Garg, ZC Lipton arXiv preprint arXiv:2211.15853, 2022 | | 2022 |
| Language Modelling for Code-Switched Text T Parekh, S GARG Indian Institute of Technology Bombay, 2018 | | 2018 |
Zachary C. LiptonAssistant Professor at Carnegie Mellon University; Chief Scientific Officer at AbridgeVerified email at cmu.edu
