Ben Adlam

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 34

Top Topics

High Dimensions

Kernel Regression

Bregman Divergences

Top Venues

PLoS Comput. Biol.

Publications

Yufan Li, Subhabrata Sen, Ben Adlam
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis. CoRR (2024)
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
Small-scale proxies for large-scale Transformer training instabilities. ICLR (2024)
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
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 2024 (2024)
Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. 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
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR (2023)
Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex 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
Small-scale proxies for large-scale Transformer training instabilities. CoRR (2023)
Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples. CoRR (2023)
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
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR (2023)
Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. CoRR (2022)
Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. NeurIPS (2022)
Ben Adlam, Jake A. Levinson, Jeffrey Pennington
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions. AISTATS (2022)
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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23 (2022)
Neha Gupta, Jamie Smith, Ben Adlam, Zelda E. Mariet
Ensembles of Classifiers: a Bias-Variance Perspective. Trans. Mach. Learn. Res. 2022 (2022)
Ben Adlam, Neha Gupta, Zelda Mariet, Jamie Smith
Understanding the bias-variance tradeoff of Bregman divergences. CoRR (2022)
Neha Gupta, Jamie Smith, Ben Adlam, Zelda Mariet
Ensembling over Classifiers: a Bias-Variance Perspective. CoRR (2022)
Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington
Overparameterization Improves Robustness to Covariate Shift in High Dimensions. NeurIPS (2021)
Anjalika Nande, Ben Adlam, Justin Sheen, Michael Z. Levy, Alison L. Hill
Dynamics of COVID-19 under social distancing measures are driven by transmission network structure. PLoS Comput. Biol. 17 (2) (2021)
Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington
Covariate Shift in High-Dimensional Random Feature Regression. CoRR (2021)
Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. ICLR (2021)
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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR (2020)
Ben Adlam, Jeffrey Pennington
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition. CoRR (2020)
Ben Adlam, Jeffrey Pennington
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. CoRR (2020)
Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein
Finite Versus Infinite Neural Networks: an Empirical Study. NeurIPS (2020)
Ben Adlam, Jasper Snoek, Samuel L. Smith
Cold Posteriors and Aleatoric Uncertainty. CoRR (2020)
Ben Adlam, Jeffrey Pennington
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. ICML (2020)
Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. NeurIPS (2020)
Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. CoRR (2020)
Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. CoRR (2020)
Ben Adlam, Jeffrey Pennington
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition. NeurIPS (2020)
Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein
Finite Versus Infinite Neural Networks: an Empirical Study. CoRR (2020)
Ben Adlam, Jake Levinson, Jeffrey Pennington
A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning. CoRR (2019)
Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang
Learning GANs and Ensembles Using Discrepancy. CoRR (2019)
Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles. CoRR (2019)
Ben Adlam, Charles Weill, Amol Kapoor
Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks. CoRR (2019)
Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang
Learning GANs and Ensembles Using Discrepancy. NeurIPS (2019)
Krishnendu Chatterjee, Andreas Pavlogiannis, Ben Adlam, Martin A. Nowak
The Time Scale of Evolutionary Innovation. PLoS Comput. Biol. 10 (9) (2014)