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 (ICLR (Poster), 2018)

Minnorm training: an algorithm for training over-parameterized deep neural networks. - Yamini Bansal; Madhu Advani; David D. Cox; Andrew M. Saxe (CoRR, 2018)

Deep Double Descent: Where Bigger Models and More Data Hurt. - Preetum Nakkiran; Gal Kaplun; Yamini Bansal; Tristan Yang; Boaz Barak; Ilya Sutskever (ICLR, 2020)

Distributional Generalization: A New Kind of Generalization. - Preetum Nakkiran; Yamini Bansal (CoRR, 2020)

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. - Akash Srivastava; Yamini Bansal; Yukun Ding; Cole L. Hurwitz; Kai Xu 016; Bernhard Egger 001; Prasanna Sattigeri; Josh Tenenbaum 001; David D. Cox; Dan Gutfreund (CoRR, 2020)

For self-supervised learning, Rationality implies generalization, provably. - Yamini Bansal; Gal Kaplun; Boaz Barak (ICLR, 2021)

Revisiting Model Stitching to Compare Neural Representations. - Yamini Bansal; Preetum Nakkiran; Boaz Barak (NeurIPS, 2021)

Data Scaling Laws in NMT: The Effect of Noise and Architecture. - Yamini Bansal; Behrooz Ghorbani; Ankush Garg; Biao Zhang 006; Colin Cherry; Behnam Neyshabur; Orhan Firat (ICML, 2022)

Data Scaling Laws in NMT: The Effect of Noise and Architecture. - Yamini Bansal; Behrooz Ghorbani; Ankush Garg; Biao Zhang 006; Maxim Krikun; Colin Cherry; Behnam Neyshabur; Orhan Firat (CoRR, 2022)

Limitations of the NTK for Understanding Generalization in Deep Learning. - Nikhil Vyas 001; Yamini Bansal; Preetum Nakkiran (CoRR, 2022)

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 (ICML, 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)

On Privileged and Convergent Bases in Neural Network Representations. - Davis Brown; Nikhil Vyas 001; Yamini Bansal (CoRR, 2023)

Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning. - Nikhil Vyas 001; Yamini Bansal; Preetum Nakkiran (Trans. Mach. Learn. Res., 2023)

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 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)