Yamini Bansal

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 19

Top Topics

Top Venues

Trans. Mach. Learn. Res.

Publications

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)
Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning. Trans. Mach. Learn. Res. 2023 (2023)
Xavier Garcia, Yamini Bansal, Colin Cherry, George F. Foster, Maxim Krikun, Fangxiaoyu Feng, Melvin Johnson, Orhan Firat
The unreasonable effectiveness of few-shot learning for machine translation. CoRR (2023)
Xavier Garcia, Yamini Bansal, Colin Cherry, George F. Foster, Maxim Krikun, Melvin Johnson, Orhan Firat
The Unreasonable Effectiveness of Few-shot Learning for Machine Translation. ICML (2023)
Davis Brown, Nikhil Vyas, Yamini Bansal
On Privileged and Convergent Bases in Neural Network Representations. CoRR (2023)
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat
Data Scaling Laws in NMT: The Effect of Noise and Architecture. ICML (2022)
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Maxim Krikun, Colin Cherry, Behnam Neyshabur, Orhan Firat
Data Scaling Laws in NMT: The Effect of Noise and Architecture. CoRR (2022)
Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
Limitations of the NTK for Understanding Generalization in Deep Learning. CoRR (2022)
Yamini Bansal, Preetum Nakkiran, Boaz Barak
Revisiting Model Stitching to Compare Neural Representations. CoRR (2021)
Yamini Bansal, Preetum Nakkiran, Boaz Barak
Revisiting Model Stitching to Compare Neural Representations. NeurIPS (2021)
Yamini Bansal, Gal Kaplun, Boaz Barak
For self-supervised learning, Rationality implies generalization, provably. ICLR (2021)
Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. CoRR (2020)
Yamini Bansal, Gal Kaplun, Boaz Barak
For self-supervised learning, Rationality implies generalization, provably. CoRR (2020)
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever
Deep Double Descent: Where Bigger Models and More Data Hurt. ICLR (2020)
Preetum Nakkiran, Yamini Bansal
Distributional Generalization: A New Kind of Generalization. CoRR (2020)
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever
Deep Double Descent: Where Bigger Models and More Data Hurt. CoRR (2019)
Andrew M. Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D. Tracey, David D. Cox
On the Information Bottleneck Theory of Deep Learning. ICLR (Poster) (2018)
Yamini Bansal, Madhu Advani, David D. Cox, Andrew M. Saxe
Minnorm training: an algorithm for training over-parameterized deep neural networks. CoRR (2018)