The Power of Asymmetry in Binary Hashing. - Behnam Neyshabur; Nati Srebro; Ruslan Salakhutdinov; Yury Makarychev; Payman Yadollahpour (NIPS, 2013)

NETAL: a new graph-based method for global alignment of protein-protein interaction networks. - Behnam Neyshabur; Ahmadreza Khadem; Somaye Hashemifar; Seyed Shahriar Arab (Bioinform., 2013)

Sparse Matrix Factorization. - Behnam Neyshabur; Rina Panigrahy (CoRR, 2013)

Clustering, Hamming Embedding, Generalized LSH and the Max Norm. - Behnam Neyshabur; Yury Makarychev; Nathan Srebro (ALT, 2014)

Joint inference of tissue-specific networks with a scale free topology. - Somaye Hashemifar; Behnam Neyshabur; Jinbo Xu (BIBM, 2015)

Norm-Based Capacity Control in Neural Networks. - Behnam Neyshabur; Ryota Tomioka; Nathan Srebro (COLT, 2015)

On Symmetric and Asymmetric LSHs for Inner Product Search. - Behnam Neyshabur; Nathan Srebro (ICML, 2015)

Path-SGD: Path-Normalized Optimization in Deep Neural Networks. - Behnam Neyshabur; Ruslan Salakhutdinov; Nathan Srebro (NIPS, 2015)

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning. - Behnam Neyshabur; Ryota Tomioka; Nathan Srebro (ICLR (Workshop), 2015)

Global Optimality of Local Search for Low Rank Matrix Recovery. - Srinadh Bhojanapalli; Behnam Neyshabur; Nati Srebro (NIPS, 2016)

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. - Behnam Neyshabur; Yuhuai Wu; Ruslan Salakhutdinov; Nati Srebro (NIPS, 2016)

Data-Dependent Path Normalization in Neural Networks. - Behnam Neyshabur; Ryota Tomioka; Ruslan Salakhutdinov; Nathan Srebro (ICLR (Poster), 2016)

Corralling a Band of Bandit Algorithms. - Alekh Agarwal; Haipeng Luo; Behnam Neyshabur; Robert E. Schapire (COLT, 2017)

Implicit Regularization in Matrix Factorization. - Suriya Gunasekar; Blake E. Woodworth; Srinadh Bhojanapalli; Behnam Neyshabur; Nati Srebro (NIPS, 2017)

Exploring Generalization in Deep Learning. - Behnam Neyshabur; Srinadh Bhojanapalli; David McAllester; Nati Srebro (NIPS, 2017)

Stabilizing GAN Training with Multiple Random Projections. - Behnam Neyshabur; Srinadh Bhojanapalli; Ayan Chakrabarti (CoRR, 2017)

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. - Behnam Neyshabur; Srinadh Bhojanapalli; David McAllester; Nathan Srebro (CoRR, 2017)

Geometry of Optimization and Implicit Regularization in Deep Learning. - Behnam Neyshabur; Ryota Tomioka; Ruslan Salakhutdinov; Nathan Srebro (CoRR, 2017)

Implicit Regularization in Deep Learning. - Behnam Neyshabur (CoRR, 2017)

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. - Behnam Neyshabur; Srinadh Bhojanapalli; Nathan Srebro (ICLR (Poster), 2018)

Stronger Generalization Bounds for Deep Nets via a Compression Approach. - Sanjeev Arora; Rong Ge 001; Behnam Neyshabur; Yi Zhang 074 (ICML, 2018)

Predicting protein-protein interactions through sequence-based deep learning. - Somaye Hashemifar; Behnam Neyshabur; Aly A. Khan; Jinbo Xu (Bioinform., 2018)

Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks. - Behnam Neyshabur; Zhiyuan Li 005; Srinadh Bhojanapalli; Yann LeCun; Nathan Srebro (CoRR, 2018)

The role of over-parametrization in generalization of neural networks. - Behnam Neyshabur; Zhiyuan Li 005; Srinadh Bhojanapalli; Yann LeCun; Nathan Srebro (ICLR (Poster), 2019)

The intriguing role of module criticality in the generalization of deep networks. - Niladri S. Chatterji; Behnam Neyshabur; Hanie Sedghi (ICLR, 2020)

Fantastic Generalization Measures and Where to Find Them. - Yiding Jiang; Behnam Neyshabur; Hossein Mobahi; Dilip Krishnan; Samy Bengio (ICLR, 2020)

Observational Overfitting in Reinforcement Learning. - Xingyou Song; Yiding Jiang; Stephen Tu; Yilun Du; Behnam Neyshabur (ICLR, 2020)

Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. - Yiding Jiang; Parth Natekar; Manik Sharma; Sumukh K. Aithal; Dhruva Kashyap; Natarajan Subramanyam; Carlos Lassance; Daniel M. Roy 001; Gintare Karolina Dziugaite; Suriya Gunasekar; Isabelle Guyon; Pierre Foret; et al. (NeurIPS (Competition and Demos), 2020)

Towards Learning Convolutions from Scratch. - Behnam Neyshabur (NeurIPS, 2020)

What is being transferred in transfer learning? - Behnam Neyshabur; Hanie Sedghi; Chiyuan Zhang (NeurIPS, 2020)

The Deep Bootstrap: Good Online Learners are Good Offline Generalizers. - Preetum Nakkiran; Behnam Neyshabur; Hanie Sedghi (CoRR, 2020)

NeurIPS 2020 Competition: Predicting Generalization in Deep Learning. - Yiding Jiang; Pierre Foret; Scott Yak; Daniel M. Roy 001; Hossein Mobahi; Gintare Karolina Dziugaite; Samy Bengio; Suriya Gunasekar; Isabelle Guyon; Behnam Neyshabur (CoRR, 2020)

Sharpness-aware Minimization for Efficiently Improving Generalization. - Pierre Foret; Ariel Kleiner; Hossein Mobahi; Behnam Neyshabur (ICLR, 2021)

Are wider nets better given the same number of parameters? - Anna Golubeva; Guy Gur-Ari; Behnam Neyshabur (ICLR, 2021)

Extreme Memorization via Scale of Initialization. - Harsh Mehta; Ashok Cutkosky; Behnam Neyshabur (ICLR, 2021)

Understanding the failure modes of out-of-distribution generalization. - Vaishnavh Nagarajan; Anders Andreassen; Behnam Neyshabur (ICLR, 2021)

The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers. - Preetum Nakkiran; Behnam Neyshabur; Hanie Sedghi (ICLR, 2021)

When Do Curricula Work? - Xiaoxia Wu; Ethan Dyer; Behnam Neyshabur (ICLR, 2021)

Deep Learning Through the Lens of Example Difficulty. - Robert J. N. Baldock; Hartmut Maennel; Behnam Neyshabur (NeurIPS, 2021)

A Loss Curvature Perspective on Training Instability in Deep Learning. - Justin Gilmer; Behrooz Ghorbani; Ankush Garg; Sneha Kudugunta; Behnam Neyshabur; David Cardoze; George E. Dahl; Zachary Nado; Orhan Firat (CoRR, 2021)

Exploring the Limits of Large Scale Pre-training. - Samira Abnar; Mostafa Dehghani 001; Behnam Neyshabur; Hanie Sedghi (ICLR, 2022)

The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks. - Rahim Entezari; Hanie Sedghi; Olga Saukh; Behnam Neyshabur (ICLR, 2022)

Leveraging unlabeled data to predict out-of-distribution performance. - Saurabh Garg; Sivaraman Balakrishnan; Zachary Chase Lipton; Behnam Neyshabur; Hanie Sedghi (ICLR, 2022)

A Loss Curvature Perspective on Training Instabilities of Deep Learning Models. - Justin Gilmer; Behrooz Ghorbani; Ankush Garg; Sneha Kudugunta; Behnam Neyshabur; David Cardoze; George Edward Dahl; Zachary Nado; Orhan Firat (ICLR, 2022)

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)

Revisiting Neural Scaling Laws in Language and Vision. - Ibrahim M. Alabdulmohsin; Behnam Neyshabur; Xiaohua Zhai (NeurIPS, 2022)

Exploring Length Generalization in Large Language Models. - Cem Anil; Yuhuai Wu; Anders Andreassen; Aitor Lewkowycz; Vedant Misra; Vinay V. Ramasesh; Ambrose Slone; Guy Gur-Ari; Ethan Dyer; Behnam Neyshabur (NeurIPS, 2022)

Block-Recurrent Transformers. - DeLesley Hutchins; Imanol Schlag; Yuhuai Wu; Ethan Dyer; Behnam Neyshabur (NeurIPS, 2022)

Solving Quantitative Reasoning Problems with Language Models. - Aitor Lewkowycz; Anders Andreassen; David Dohan; Ethan Dyer; Henryk Michalewski; Vinay V. Ramasesh; Ambrose Slone; Cem Anil; Imanol Schlag; Theo Gutman-Solo; Yuhuai Wu; Behnam Neyshabur; et al. (NeurIPS, 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)

Understanding the effect of sparsity on neural networks robustness. - Lukas Timpl; Rahim Entezari; Hanie Sedghi; Behnam Neyshabur; Olga Saukh (CoRR, 2022)

Teaching Algorithmic Reasoning via In-context Learning. - Hattie Zhou; Azade Nova; Hugo Larochelle; Aaron C. Courville; Behnam Neyshabur; Hanie Sedghi (CoRR, 2022)

Layer-Stack Temperature Scaling. - Amr Khalifa; Michael C. Mozer; Hanie Sedghi; Behnam Neyshabur; Ibrahim Alabdulmohsin (CoRR, 2022)

Convexifying Transformers: Improving optimization and understanding of transformer networks. - Tolga Ergen; Behnam Neyshabur; Harsh Mehta (CoRR, 2022)

The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning. - Anders Johan Andreassen; Yasaman Bahri; Behnam Neyshabur; Rebecca Roelofs (Trans. Mach. Learn. Res., 2022)

REPAIR: REnormalizing Permuted Activations for Interpolation Repair. - Keller Jordan; Hanie Sedghi; Olga Saukh; Rahim Entezari; Behnam Neyshabur (ICLR, 2023)

Long Range Language Modeling via Gated State Spaces. - Harsh Mehta; Ankit Gupta 001; Ashok Cutkosky; Behnam Neyshabur (ICLR, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Gemma 2: Improving Open Language Models at a Practical Size. - Morgane Rivière; Shreya Pathak; Pier Giuseppe Sessa; Cassidy Hardin; Surya Bhupatiraju; Léonard Hussenot; Thomas Mesnard; Bobak Shahriari; Alexandre Ramé; Johan Ferret; Peter Liu; Pouya Tafti; et al. (CoRR, 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 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)