The Time Scale of Evolutionary Innovation. - Krishnendu Chatterjee; Andreas Pavlogiannis; Ben Adlam; Martin A. Nowak (PLoS Comput. Biol., 2014)

Learning GANs and Ensembles Using Discrepancy. - Ben Adlam; Corinna Cortes; Mehryar Mohri; Ningshan Zhang (NeurIPS, 2019)

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles. - Charles Weill; Javier Gonzalvo; Vitaly Kuznetsov; Scott Yang; Scott Yak; Hanna Mazzawi; Eugen Hotaj; Ghassen Jerfel; Vladimir Macko; Ben Adlam; Mehryar Mohri; Corinna Cortes (CoRR, 2019)

Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks. - Ben Adlam; Charles Weill; Amol Kapoor (CoRR, 2019)

A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning. - Ben Adlam; Jake Levinson; Jeffrey Pennington (CoRR, 2019)

The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. - Ben Adlam; Jeffrey Pennington (ICML, 2020)

Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition. - Ben Adlam; Jeffrey Pennington (NeurIPS, 2020)

The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. - Wei Hu; Lechao Xiao; Ben Adlam; Jeffrey Pennington (NeurIPS, 2020)

Finite Versus Infinite Neural Networks: an Empirical Study. - Jaehoon Lee 001; Samuel S. Schoenholz; Jeffrey Pennington; Ben Adlam; Lechao Xiao; Roman Novak; Jascha Sohl-Dickstein (NeurIPS, 2020)

Cold Posteriors and Aleatoric Uncertainty. - Ben Adlam; Jasper Snoek; Samuel L. Smith (CoRR, 2020)

Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. - Ben Adlam; Jaehoon Lee 001; Lechao Xiao; Jeffrey Pennington; Jasper Snoek (ICLR, 2021)

Overparameterization Improves Robustness to Covariate Shift in High Dimensions. - Nilesh Tripuraneni; Ben Adlam; Jeffrey Pennington (NeurIPS, 2021)

Covariate Shift in High-Dimensional Random Feature Regression. - Nilesh Tripuraneni; Ben Adlam; Jeffrey Pennington (CoRR, 2021)

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure. - Anjalika Nande; Ben Adlam; Justin Sheen; Michael Z. Levy; Alison L. Hill (PLoS Comput. Biol., 2021)

A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions. - Ben Adlam; Jake A. Levinson; Jeffrey Pennington (AISTATS, 2022)

Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. - Courtney Paquette; Elliot Paquette; Ben Adlam; Jeffrey Pennington (NeurIPS, 2022)

Understanding the bias-variance tradeoff of Bregman divergences. - Ben Adlam; Neha Gupta; Zelda Mariet; Jamie Smith (CoRR, 2022)

Ensembling over Classifiers: a Bias-Variance Perspective. - Neha Gupta; Jamie Smith; Ben Adlam; Zelda Mariet (CoRR, 2022)

Underspecification Presents Challenges for Credibility in Modern Machine Learning. - Alexander D'Amour; Katherine A. Heller; Dan Moldovan; Ben Adlam; Babak Alipanahi; Alex Beutel; Christina Chen; Jonathan Deaton; Jacob Eisenstein; Matthew D. Hoffman; Farhad Hormozdiari; Neil Houlsby; et al. (J. Mach. Learn. Res., 2022)

Ensembles of Classifiers: a Bias-Variance Perspective. - Neha Gupta; Jamie Smith; Ben Adlam; Zelda E. Mariet (Trans. Mach. Learn. Res., 2022)

Kernel Regression with Infinite-Width Neural Networks on Millions of Examples. - Ben Adlam; Jaehoon Lee 001; Shreyas Padhy; Zachary Nado; Jasper Snoek (CoRR, 2023)

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? - C. Daniel Freeman; Laura Culp; Aaron Parisi; Maxwell L. Bileschi; Gamaleldin F. Elsayed; Alex Rizkowsky; Isabelle Simpson; Alex Alemi; Azade Nova; Ben Adlam; Bernd Bohnet; Gaurav Mishra; et al. (CoRR, 2023)

Small-scale proxies for large-scale Transformer training instabilities. - Mitchell Wortsman; Peter J. Liu; Lechao Xiao; Katie E. Everett; Alexander A. Alemi; Ben Adlam; John D. Co-Reyes; Izzeddin Gur; Abhishek Kumar; Roman Novak; Jeffrey Pennington; Jascha Sohl-Dickstein; et al. (ICLR, 2024)

Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis. - Yufan Li; Subhabrata Sen; Ben Adlam (CoRR, 2024)

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. - Jiri Hron; Laura Culp; Gamaleldin F. Elsayed; Rosanne Liu; Ben Adlam; Maxwell L. Bileschi; Bernd Bohnet; JD Co-Reyes; Noah Fiedel; C. Daniel Freeman; Izzeddin Gur; Kathleen Kenealy; et al. (CoRR, 2024)

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. - Avi Singh; John D. Co-Reyes; Rishabh Agarwal; Ankesh Anand; Piyush Patil; Xavier Garcia; Peter J. Liu; James Harrison; Jaehoon Lee 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)