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2020 – today
- 2024
[j5]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)- [c10]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 - [i21]Yufan Li, Subhabrata Sen, Ben Adlam:
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis. CoRR abs/2404.12481 (2024) - [i20]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:
Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR abs/2408.07852 (2024) - 2023
- [i19]Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek:
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples. CoRR abs/2303.05420 (2023) - [i18]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 abs/2309.14322 (2023) - [i17]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 abs/2311.07587 (2023) - [i16]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 abs/2312.06585 (2023) - 2022
- [j4]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: 226:1-226:61 (2022) - [j3]Neha Gupta, Jamie Smith, Ben Adlam, Zelda E. Mariet:
Ensembles of Classifiers: a Bias-Variance Perspective. Trans. Mach. Learn. Res. 2022 (2022) - [c9]Ben Adlam, Jake A. Levinson, Jeffrey Pennington:
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions. AISTATS 2022: 3434-3457 - [c8]Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington:
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. NeurIPS 2022 - [i15]Ben Adlam, Neha Gupta, Zelda Mariet, Jamie Smith:
Understanding the bias-variance tradeoff of Bregman divergences. CoRR abs/2202.04167 (2022) - [i14]Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington:
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. CoRR abs/2206.07252 (2022) - [i13]Neha Gupta, Jamie Smith, Ben Adlam, Zelda Mariet:
Ensembling over Classifiers: a Bias-Variance Perspective. CoRR abs/2206.10566 (2022) - 2021
- [j2]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) - [c7]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 - [c6]Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington:
Overparameterization Improves Robustness to Covariate Shift in High Dimensions. NeurIPS 2021: 13883-13897 - [i12]Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington:
Covariate Shift in High-Dimensional Random Feature Regression. CoRR abs/2111.08234 (2021) - 2020
- [c5]Ben Adlam, Jeffrey Pennington:
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. ICML 2020: 74-84 - [c4]Ben Adlam, Jeffrey Pennington:
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition. NeurIPS 2020 - [c3]Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington:
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. NeurIPS 2020 - [c2]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 - [i11]Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington:
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. CoRR abs/2006.14599 (2020) - [i10]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 abs/2007.15801 (2020) - [i9]Ben Adlam, Jasper Snoek, Samuel L. Smith:
Cold Posteriors and Aleatoric Uncertainty. CoRR abs/2008.00029 (2020) - [i8]Ben Adlam, Jeffrey Pennington:
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. CoRR abs/2008.06786 (2020) - [i7]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 abs/2010.07355 (2020) - [i6]Ben Adlam, Jeffrey Pennington:
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition. CoRR abs/2011.03321 (2020) - [i5]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 abs/2011.03395 (2020)
2010 – 2019
- 2019
- [c1]Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang:
Learning GANs and Ensembles Using Discrepancy. NeurIPS 2019: 5788-5799 - [i4]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 abs/1905.00080 (2019) - [i3]Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang:
Learning GANs and Ensembles Using Discrepancy. CoRR abs/1910.08965 (2019) - [i2]Ben Adlam, Charles Weill, Amol Kapoor:
Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks. CoRR abs/1910.14137 (2019) - [i1]Ben Adlam, Jake Levinson, Jeffrey Pennington:
A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning. CoRR abs/1912.00827 (2019) - 2014
- [j1]Krishnendu Chatterjee, Andreas Pavlogiannis, Ben Adlam, Martin A. Nowak:
The Time Scale of Evolutionary Innovation. PLoS Comput. Biol. 10(9) (2014)
Coauthor Index
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last updated on 2024-09-25 01:39 CEST by the dblp team
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