| 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, 2020 | 395 | 2020 |
| 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 | 223* | 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 | 147 | 2018 |
| 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 | 112 | 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 | 72 | 2021 |
| 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 | 70 | 2020 |
| 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 | 62* | 2021 |
| Amplifiers of selection B Adlam, K Chatterjee, MA Nowak Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015 | 60 | 2015 |
| Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition B Adlam, J Pennington Advances in Neural Information Processing Systems, 2020 | 50 | 2020 |
| Universality of fixation probabilities in randomly structured populations B Adlam, MA Nowak Scientific Reports 4 (1), 1-6, 2014 | 44 | 2014 |
| The time scale of evolutionary innovation K Chatterjee, A Pavlogiannis, B Adlam, MA Nowak PLoS computational biology 10 (9), e1003818, 2014 | 43 | 2014 |
| 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 | 39 | 2020 |
| 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 | 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 | 19 | 2019 |
| Spectral statistics of sparse random graphs with a general degree distribution B Adlam, Z Che arXiv preprint arXiv:1509.03368, 2015 | 19* | 2015 |
| 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 | 17 | 2021 |
| Cold Posteriors and Aleatoric Uncertainty B Adlam, J Snoek, SL Smith ICML Workshop on Uncertainty & Robustness in Deep Learning, 2020 | 13 | 2020 |
| Covariate shift in high-dimensional random feature regression N Tripuraneni, B Adlam, J Pennington arXiv preprint arXiv:2111.08234, 2021 | 12 | 2021 |
| Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks B Adlam, A Kapoor, C Weill ICML Understanding and Improving Generalization in Deep Learning Workshop, 2019 | 12 | 2019 |
| Cellular cooperation with shift updating and repulsion A Pavlogiannis, K Chatterjee, B Adlam, MA Nowak Scientific reports 5 (1), 17147, 2015 | 11 | 2015 |
