| Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast S Venkataram, B Dunn, Y Li, A Agarwala, J Chang, ER Ebel, ... Cell 166 (6), 1585-1596. e22, 2016 | 207 | 2016 |
| Stabilization of extensive fine-scale diversity by ecologically driven spatiotemporal chaos MT Pearce, A Agarwala, DS Fisher Proceedings of the National Academy of Sciences 117 (25), 14572-14583, 2020 | 89* | 2020 |
| Hidden complexity of yeast adaptation under simple evolutionary conditions Y Li, S Venkataram, A Agarwala, B Dunn, DA Petrov, G Sherlock, ... Current Biology 28 (4), 515-525. e6, 2018 | 58 | 2018 |
| Learning Dota 2 team compositions A Agarwala, M Pearce Sl: sn, 2014 | 40 | 2014 |
| Adaptive walks on high-dimensional fitness landscapes and seascapes with distance-dependent statistics A Agarwala, DS Fisher Theoretical population biology 130, 13-49, 2019 | 35 | 2019 |
| Temperature check: theory and practice for training models with softmax-cross-entropy losses A Agarwala, J Pennington, Y Dauphin, S Schoenholz arXiv preprint arXiv:2010.07344, 2020 | 21 | 2020 |
| Second-order regression models exhibit progressive sharpening to the edge of stability A Agarwala, F Pedregosa, J Pennington arXiv preprint arXiv:2210.04860, 2022 | 16 | 2022 |
| One network fits all? modular versus monolithic task formulations in neural networks A Agarwala, A Das, B Juba, R Panigrahy, V Sharan, X Wang, Q Zhang arXiv preprint arXiv:2103.15261, 2021 | 13 | 2021 |
| SAM operates far from home: eigenvalue regularization as a dynamical phenomenon A Agarwala, Y Dauphin International Conference on Machine Learning, 152-168, 2023 | 11 | 2023 |
| Hé rissant L, Blundell JR, Levy SF, Fisher DS, Sherlock G, Petrov DA. 2016. Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast S Venkataram, B Dunn, Y Li, A Agarwala, J Chang, ER Ebel, ... Cell 166, 1585-1596, 0 | 7 | |
| Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design N Thomas, A Agarwala, D Belanger, YS Song, LJ Colwell bioRxiv, 2022.10. 28.514293, 2022 | 4 | 2022 |
| Deep equilibrium networks are sensitive to initialization statistics A Agarwala, SS Schoenholz International Conference on Machine Learning, 136-160, 2022 | 4 | 2022 |
| Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks D Beaglehole, I Mitliagkas, A Agarwala arXiv preprint arXiv:2402.05271, 2024 | 1 | 2024 |
| Far From Home: Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments VK Chen, MS Johnson, L Hérissant, PT Humphrey, DC Yuan, Y Li, ... bioRxiv, 2023.02. 28.530341, 2023 | 1 | 2023 |
| Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments V Chen, MS Johnson, L Hérissant, PT Humphrey, DC Yuan, Y Li, ... Elife 12, 2023 | 1 | 2023 |
| Learning the gravitational force law and other analytic functions A Agarwala, A Das, R Panigrahy, Q Zhang arXiv preprint arXiv:2005.07724, 2020 | 1 | 2020 |
| Neglected Hessian component explains mysteries in Sharpness regularization YN Dauphin, A Agarwala, H Mobahi arXiv preprint arXiv:2401.10809, 2024 | | 2024 |
| On the Interplay Between Stepsize Tuning and Progressive Sharpening V Roulet, A Agarwala, F Pedregosa OPT 2023: Optimization for Machine Learning, 2023 | | 2023 |
| How Hessian structure explains mysteries in sharpness regularization Y Dauphin, A Agarwala, H Mobahi | | 2023 |
| A Second-order Regression Model Shows Edge of Stability Behavior F Pedregosa, A Agarwala, J Pennington OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 2022 | | 2022 |
