| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research 23 (226), 1-61, 2022 | 713 | 2022 |
| Crowding and the shape of COVID-19 epidemics B Rader, SV Scarpino, A Nande, AL Hill, B Adlam, RC Reiner, DM Pigott, ... Nature Medicine, 1-6, 2020 | 288* | 2020 |
| Finite Versus Infinite Neural Networks: an Empirical Study J Lee, S Schoenholz, J Pennington, B Adlam, L Xiao, R Novak, ... Advances in Neural Information Processing Systems, 2020 | 200 | 2020 |
| Current CRISPR gene drive systems are likely to be highly invasive in wild populations C Noble, B Adlam, GM Church, KM Esvelt, MA Nowak Elife 7, e33423, 2018 | 174 | 2018 |
| The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization B Adlam, J Pennington Thirty-seventh International Conference on Machine Learning, 2020 | 136 | 2020 |
| Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition B Adlam, J Pennington Advances in Neural Information Processing Systems, 2020 | 110 | 2020 |
| Dynamics of COVID-19 under social distancing measures are driven by transmission network structure A Nande, B Adlam, J Sheen, MZ Levy, AL Hill PLoS computational biology 17 (2), e1008684, 2021 | 105 | 2021 |
| The effect of eviction moratoria on the transmission of SARS-CoV-2 ALH Anjalika Nande, Justin Sheen, Emma L Walters, Brennan Klein, Matteo ... Nature Communications 12 (2274), 2021 | 94 | 2021 |
| Amplifiers of selection B Adlam, K Chatterjee, MA Nowak Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015 | 76 | 2015 |
| The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks W Hu, L Xiao, B Adlam, J Pennington Advances in Neural Information Processing Systems, 2020 | 71 | 2020 |
| Overparameterization improves robustness to covariate shift in high dimensions N Tripuraneni, B Adlam, J Pennington Advances in Neural Information Processing Systems 34, 13883-13897, 2021 | 47 | 2021 |
| Universality of fixation probabilities in randomly structured populations B Adlam, MA Nowak Scientific Reports 4 (1), 6692, 2014 | 46 | 2014 |
| The time scale of evolutionary innovation K Chatterjee, A Pavlogiannis, B Adlam, MA Nowak PLoS computational biology 10 (9), e1003818, 2014 | 45 | 2014 |
| A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions B Adlam, J Levinson, J Pennington International Conference on Artificial Intelligence and Statistics, 2022 | 36* | 2022 |
| Beyond human data: Scaling self-training for problem-solving with language models A Singh, JD Co-Reyes, R Agarwal, A Anand, P Patil, PJ Liu, J Harrison, ... arXiv preprint arXiv:2312.06585, 2023 | 35 | 2023 |
| Covariate shift in high-dimensional random feature regression N Tripuraneni, B Adlam, J Pennington arXiv preprint arXiv:2111.08234, 2021 | 26 | 2021 |
| Small-scale proxies for large-scale Transformer training instabilities M Wortsman, PJ Liu, L Xiao, K Everett, A Alemi, B Adlam, JD Co-Reyes, ... The Twelfth International Conference on Learning Representations, 2023 | 23 | 2023 |
| Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties C Paquette, E Paquette, B Adlam, J Pennington arXiv preprint arXiv:2205.07069, 2022 | 23 | 2022 |
| Adanet: A scalable and flexible framework for automatically learning ensembles C Weill, J Gonzalvo, V Kuznetsov, S Yang, S Yak, H Mazzawi, E Hotaj, ... arXiv preprint arXiv:1905.00080, 2019 | 22 | 2019 |
| Cold Posteriors and Aleatoric Uncertainty B Adlam, J Snoek, SL Smith ICML Workshop on Uncertainty & Robustness in Deep Learning, 2020 | 19 | 2020 |