Yamini Bansal

According to our database, Yamini Bansal authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

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

Trans. Mach. Learn. Res., 2024

2023

Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning.

Nikhil Vyas, Yamini Bansal, Preetum Nakkiran

Trans. Mach. Learn. Res., 2023

On Privileged and Convergent Bases in Neural Network Representations.

Davis Brown, Nikhil Vyas, Yamini Bansal

CoRR, 2023

The unreasonable effectiveness of few-shot learning for machine translation.

Xavier Garcia, Yamini Bansal, Colin Cherry, George F. Foster, Maxim Krikun, Fangxiaoyu Feng, Melvin Johnson, Orhan Firat

CoRR, 2023

The Unreasonable Effectiveness of Few-shot Learning for Machine Translation.

Xavier Garcia, Yamini Bansal, Colin Cherry, George F. Foster, Maxim Krikun, Melvin Johnson, Orhan Firat

Proceedings of the International Conference on Machine Learning, 2023

2022

Limitations of the NTK for Understanding Generalization in Deep Learning.

Nikhil Vyas, Yamini Bansal, Preetum Nakkiran

CoRR, 2022

Data Scaling Laws in NMT: The Effect of Noise and Architecture.

Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Maxim Krikun, Colin Cherry, Behnam Neyshabur, Orhan Firat

CoRR, 2022

Data Scaling Laws in NMT: The Effect of Noise and Architecture.

Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat

Proceedings of the International Conference on Machine Learning, 2022

2021

Revisiting Model Stitching to Compare Neural Representations.

Yamini Bansal, Preetum Nakkiran, Boaz Barak

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

For self-supervised learning, Rationality implies generalization, provably.

Yamini Bansal, Gal Kaplun, Boaz Barak

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling.

Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

CoRR, 2020

Distributional Generalization: A New Kind of Generalization.

Preetum Nakkiran, Yamini Bansal

CoRR, 2020

Deep Double Descent: Where Bigger Models and More Data Hurt.

Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

Minnorm training: an algorithm for training over-parameterized deep neural networks.

Yamini Bansal, Madhu Advani, David D. Cox, Andrew M. Saxe

CoRR, 2018

On the Information Bottleneck Theory of Deep Learning.

Andrew M. Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D. Tracey, David D. Cox

Proceedings of the 6th International Conference on Learning Representations, 2018