| Deep learning on a data diet: Finding important examples early in training M Paul, S Ganguli, GK Dziugaite Advances in neural information processing systems 34, 20596-20607, 2021 | 432 | 2021 |
| Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel S Fort, GK Dziugaite, M Paul, S Kharaghani, DM Roy, S Ganguli Advances in Neural Information Processing Systems 33, 5850-5861, 2020 | 198 | 2020 |
| Lora learns less and forgets less D Biderman, J Portes, JJG Ortiz, M Paul, P Greengard, C Jennings, ... arXiv preprint arXiv:2405.09673, 2024 | 86 | 2024 |
| Pretraining task diversity and the emergence of non-bayesian in-context learning for regression A Raventós, M Paul, F Chen, S Ganguli Advances in Neural Information Processing Systems 36, 2024 | 69 | 2024 |
| Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? M Paul, F Chen, BW Larsen, J Frankle, S Ganguli, GK Dziugaite arXiv preprint arXiv:2210.03044, 2022 | 43 | 2022 |
| Lottery tickets on a data diet: Finding initializations with sparse trainable networks M Paul, B Larsen, S Ganguli, J Frankle, GK Dziugaite Advances in Neural Information Processing Systems 35, 18916-18928, 2022 | 19 | 2022 |
| Critique-out-loud reward models Z Ankner, M Paul, B Cui, JD Chang, P Ammanabrolu arXiv preprint arXiv:2408.11791, 2024 | 13 | 2024 |
| Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models Z Ankner, C Blakeney, K Sreenivasan, M Marion, ML Leavitt, M Paul arXiv preprint arXiv:2405.20541, 2024 | 13 | 2024 |
| Scaling laws for precision T Kumar, Z Ankner, BF Spector, B Bordelon, N Muennighoff, M Paul, ... arXiv preprint arXiv:2411.04330, 2024 | 11 | 2024 |
| Does your data spark joy? Performance gains from domain upsampling at the end of training C Blakeney, M Paul, BW Larsen, S Owen, J Frankle arXiv preprint arXiv:2406.03476, 2024 | 6 | 2024 |
| The effects of pretraining task diversity on in-context learning of ridge regression A Raventos, M Paul, F Chen, S Ganguli ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation …, 2023 | 5 | 2023 |
| Predicting Task Forgetting in Large Language Models A Kleiman, J Frankle, SM Kakade, M Paul | 2 | 2023 |
| Unmasking the Lottery Ticket Hypothesis: Efficient Adaptive Pruning for Finding Winning Tickets M Paul, F Chen, BW Larsen, J Frankle, S Ganguli, GK Dziugaite Has it Trained Yet? NeurIPS 2022 Workshop, 0 | 2 | |
| Perplexed by Perplexity: Perplexity-Based Pruning with Small Reference Models Z Ankner, C Blakeney, K Sreenivasan, M Marion, ML Leavitt, M Paul ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation …, 0 | 1 | |
| Pre-Training on a Data Diet: Identifying Sufficient Examples for Early Training M Paul, BW Larsen, S Ganguli, J Frankle, GK Dziugaite First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward at …, 0 | 1 | |
| Soup to go: mitigating forgetting during continual learning with model averaging A Kleiman, GK Dziugaite, J Frankle, S Kakade, M Paul arXiv preprint arXiv:2501.05559, 2025 | | 2025 |
| Deep Learning on a Diet: An Error Landscape Perspective on Parameter and Data Efficiency in Deep Learning M Paul Stanford University, 2023 | | 2023 |
