Ben Adlam - RankMe

37 papers A* 9A 1Journal 27

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Homogenization of SGD in high-dimensions: exact dynamics and generalization properties. Math. Program. Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington
2024	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 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
2024	A*	`conf`	Small-scale proxies for large-scale Transformer training instabilities. ICLR 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
2024	J	`jnl`	Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR 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
2024	J	`jnl`	Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis. CoRR Yufan Li, Subhabrata Sen, Ben Adlam
2023	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR 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
2023	J	`jnl`	Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR 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
2023	J	`jnl`	Kernel Regression with Infinite-Width Neural Networks on Millions of Examples. CoRR Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek
2023	J	`jnl`	Small-scale proxies for large-scale Transformer training instabilities. CoRR 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
2022	A	`conf`	A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions. AISTATS Ben Adlam, Jake A. Levinson, Jeffrey Pennington
2022	J	`jnl`	Ensembles of Classifiers: a Bias-Variance Perspective. Trans. Mach. Learn. Res. Neha Gupta, Jamie Smith, Ben Adlam, Zelda E. Mariet
2022	J	`jnl`	Ensembling over Classifiers: a Bias-Variance Perspective. CoRR Neha Gupta, Jamie Smith, Ben Adlam, Zelda Mariet
2022	A*	`conf`	Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. NeurIPS Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington
2022	J	`jnl`	Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. CoRR Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington
2022	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 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
2022	J	`jnl`	Understanding the bias-variance tradeoff of Bregman divergences. CoRR Ben Adlam, Neha Gupta, Zelda Mariet, Jamie Smith
2021	J	`jnl`	Covariate Shift in High-Dimensional Random Feature Regression. CoRR Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington
2021	J	`jnl`	Dynamics of COVID-19 under social distancing measures are driven by transmission network structure. PLoS Comput. Biol. Anjalika Nande, Ben Adlam, Justin Sheen, Michael Z. Levy, Alison L. Hill
2021	A*	`conf`	Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. ICLR Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek
2021	A*	`conf`	Overparameterization Improves Robustness to Covariate Shift in High Dimensions. NeurIPS Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington
2020	J	`jnl`	Cold Posteriors and Aleatoric Uncertainty. CoRR Ben Adlam, Jasper Snoek, Samuel L. Smith
2020	J	`jnl`	Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. CoRR Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek
2020	A*	`conf`	Finite Versus Infinite Neural Networks: an Empirical Study. NeurIPS Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein
2020	J	`jnl`	Finite Versus Infinite Neural Networks: an Empirical Study. CoRR Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein
2020	A*	`conf`	The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. ICML Ben Adlam, Jeffrey Pennington
2020	J	`jnl`	The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. CoRR Ben Adlam, Jeffrey Pennington
2020	A*	`conf`	The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. NeurIPS Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington
2020	J	`jnl`	The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. CoRR Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington
2020	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR 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
2020	A*	`conf`	Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition. NeurIPS Ben Adlam, Jeffrey Pennington
2020	J	`jnl`	Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition. CoRR Ben Adlam, Jeffrey Pennington
2019	J	`jnl`	A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning. CoRR Ben Adlam, Jake Levinson, Jeffrey Pennington
2019	J	`jnl`	AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles. CoRR Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes
2019	J	`jnl`	Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks. CoRR Ben Adlam, Charles Weill, Amol Kapoor
2019	A*	`conf`	Learning GANs and Ensembles Using Discrepancy. NeurIPS Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang
2019	J	`jnl`	Learning GANs and Ensembles Using Discrepancy. CoRR Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang
2014	J	`jnl`	The Time Scale of Evolutionary Innovation. PLoS Comput. Biol. Krishnendu Chatterjee, Andreas Pavlogiannis, Ben Adlam, Martin A. Nowak