Ben Adlam

According to our database, Ben Adlam authored at least 26 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

Trans. Mach. Learn. Res., 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, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith

CoRR, 2024

Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis.

Yufan Li, Subhabrata Sen, Ben Adlam

CoRR, 2024

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, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein

CoRR, 2023

Kernel Regression with Infinite-Width Neural Networks on Millions of Examples.

Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek

CoRR, 2023

2022

Ensembles of Classifiers: a Bias-Variance Perspective.

Neha Gupta, Jamie Smith, Ben Adlam, Zelda E. Mariet

Trans. Mach. Learn. Res., 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, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley

J. Mach. Learn. Res., 2022

Ensembling over Classifiers: a Bias-Variance Perspective.

Neha Gupta, Jamie Smith, Ben Adlam, Zelda Mariet

CoRR, 2022

Understanding the bias-variance tradeoff of Bregman divergences.

Ben Adlam, Neha Gupta, Zelda Mariet, Jamie Smith

CoRR, 2022

Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions.

Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions.

Ben Adlam, Jake A. Levinson, Jeffrey Pennington

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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

Covariate Shift in High-Dimensional Random Feature Regression.

Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington

CoRR, 2021

Overparameterization Improves Robustness to Covariate Shift in High Dimensions.

Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit.

Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Cold Posteriors and Aleatoric Uncertainty.

Ben Adlam, Jasper Snoek, Samuel L. Smith

CoRR, 2020

Finite Versus Infinite Neural Networks: an Empirical Study.

Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks.

Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition.

Ben Adlam, Jeffrey Pennington

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization.

Ben Adlam, Jeffrey Pennington

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning.

Ben Adlam, Jake Levinson, Jeffrey Pennington

CoRR, 2019

Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks.

Ben Adlam, Charles Weill, Amol Kapoor

CoRR, 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

Learning GANs and Ensembles Using Discrepancy.

Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2014

The Time Scale of Evolutionary Innovation.

Krishnendu Chatterjee, Andreas Pavlogiannis, Ben Adlam, Martin A. Nowak

PLoS Comput. Biol., 2014