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2020 – today
- 2024
[j2]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)- 2023
- [j1]Nikhil Vyas, Yamini Bansal, Preetum Nakkiran:
Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c6]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: 10867-10878 - [i11]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 abs/2302.01398 (2023) - [i10]Davis Brown, Nikhil Vyas, Yamini Bansal:
On Privileged and Convergent Bases in Neural Network Representations. CoRR abs/2307.12941 (2023) - [i9]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
- [c5]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: 1466-1482 - [i8]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 abs/2202.01994 (2022) - [i7]Nikhil Vyas, Yamini Bansal, Preetum Nakkiran:
Limitations of the NTK for Understanding Generalization in Deep Learning. CoRR abs/2206.10012 (2022) - 2021
- [c4]Yamini Bansal, Gal Kaplun, Boaz Barak:
For self-supervised learning, Rationality implies generalization, provably. ICLR 2021 - [c3]Yamini Bansal, Preetum Nakkiran, Boaz Barak:
Revisiting Model Stitching to Compare Neural Representations. NeurIPS 2021: 225-236 - [i6]Yamini Bansal, Preetum Nakkiran, Boaz Barak:
Revisiting Model Stitching to Compare Neural Representations. CoRR abs/2106.07682 (2021) - 2020
- [c2]Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever:
Deep Double Descent: Where Bigger Models and More Data Hurt. ICLR 2020 - [i5]Preetum Nakkiran, Yamini Bansal:
Distributional Generalization: A New Kind of Generalization. CoRR abs/2009.08092 (2020) - [i4]Yamini Bansal, Gal Kaplun, Boaz Barak:
For self-supervised learning, Rationality implies generalization, provably. CoRR abs/2010.08508 (2020) - [i3]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 abs/2010.13187 (2020)
2010 – 2019
- 2019
- [i2]Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever:
Deep Double Descent: Where Bigger Models and More Data Hurt. CoRR abs/1912.02292 (2019) - 2018
- [c1]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 - [i1]Yamini Bansal, Madhu Advani, David D. Cox, Andrew M. Saxe:
Minnorm training: an algorithm for training over-parameterized deep neural networks. CoRR abs/1806.00730 (2018)
Coauthor Index
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