Ben Adlam
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Underspecification presents challenges for credibility in modern machine learning
A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
arXiv preprint arXiv:2011.03395, 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
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
Amplifiers of selection
B Adlam, K Chatterjee, MA Nowak
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015
Universality of fixation probabilities in randomly structured populations
B Adlam, MA Nowak
Scientific Reports 4 (1), 1-6, 2014
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
The time scale of evolutionary innovation
K Chatterjee, A Pavlogiannis, B Adlam, MA Nowak
PLoS computational biology 10 (9), e1003818, 2014
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
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
Spectral statistics of sparse random graphs with a general degree distribution
B Adlam, Z Che
arXiv preprint arXiv:1509.03368, 2015
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
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
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
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition
B Adlam, J Pennington
Advances in Neural Information Processing Systems, 2020
A random matrix perspective on mixtures of nonlinearities for deep learning
B Adlam, J Levinson, J Pennington
arXiv preprint arXiv:1912.00827, 2019
Cellular cooperation with shift updating and repulsion
A Pavlogiannis, K Chatterjee, B Adlam, MA Nowak
Scientific reports 5 (1), 1-12, 2015
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
Learning GANs and Ensembles Using Discrepancy
B Adlam, C Cortes, M Mohri, N Zhang
Advances in Neural Information Processing Systems, 2019
Cold Posteriors and Aleatoric Uncertainty
B Adlam, J Snoek, SL Smith
ICML Workshop on Uncertainty & Robustness in Deep Learning, 2020
The local Marchenko–Pastur law for sparse covariance matrices
B Adlam
Senior Thesis, Harvard University Department of Mathematics, 2013
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