Yamini Bansal

19 papers A* 5Journal 13Unranked 1
YearRankTypeTitle / Venue / Authors
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
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
Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning.
Trans. Mach. Learn. Res.
Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
2023 J jnl
On Privileged and Convergent Bases in Neural Network Representations.
CoRR
Davis Brown, Nikhil Vyas, Yamini Bansal
2023 A* conf
The Unreasonable Effectiveness of Few-shot Learning for Machine Translation.
ICML
Xavier Garcia, Yamini Bansal, Colin Cherry, George F. Foster, Maxim Krikun, Melvin Johnson, Orhan Firat
2023 J jnl
The unreasonable effectiveness of few-shot learning for machine translation.
CoRR
Xavier Garcia, Yamini Bansal, Colin Cherry, George F. Foster, Maxim Krikun, Fangxiaoyu Feng, Melvin Johnson, Orhan Firat
2022 A* conf
Data Scaling Laws in NMT: The Effect of Noise and Architecture.
ICML
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat
2022 J jnl
Data Scaling Laws in NMT: The Effect of Noise and Architecture.
CoRR
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Maxim Krikun, Colin Cherry, Behnam Neyshabur, Orhan Firat
2022 J jnl
Limitations of the NTK for Understanding Generalization in Deep Learning.
CoRR
Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
2021 A* conf
For self-supervised learning, Rationality implies generalization, provably.
ICLR
Yamini Bansal, Gal Kaplun, Boaz Barak
2021 A* conf
Revisiting Model Stitching to Compare Neural Representations.
NeurIPS
Yamini Bansal, Preetum Nakkiran, Boaz Barak
2021 J jnl
Revisiting Model Stitching to Compare Neural Representations.
CoRR
Yamini Bansal, Preetum Nakkiran, Boaz Barak
2020 A* conf
Deep Double Descent: Where Bigger Models and More Data Hurt.
ICLR
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever
2020 J jnl
Distributional Generalization: A New Kind of Generalization.
CoRR
Preetum Nakkiran, Yamini Bansal
2020 J jnl
For self-supervised learning, Rationality implies generalization, provably.
CoRR
Yamini Bansal, Gal Kaplun, Boaz Barak
2020 J jnl
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling.
CoRR
Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund
2019 J jnl
Deep Double Descent: Where Bigger Models and More Data Hurt.
CoRR
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever
2018 J jnl
Minnorm training: an algorithm for training over-parameterized deep neural networks.
CoRR
Yamini Bansal, Madhu Advani, David D. Cox, Andrew M. Saxe
2018 — conf
On the Information Bottleneck Theory of Deep Learning.
ICLR (Poster)
Andrew M. Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D. Tracey, David D. Cox