Anil, Rohan

13 publications

ICLR 2024 Combining Axes Preconditioners Through Kronecker Approximation for Deep Learning Sai Surya Duvvuri, Fnu Devvrit, Rohan Anil, Cho-Jui Hsieh, Inderjit S Dhillon

NeurIPS 2023 A Computationally Efficient Sparsified Online Newton Method Fnu Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon

TMLR 2023 Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K Warmuth

NeurIPS 2023 Sketchy: Memory-Efficient Adaptive Regularization with Frequent Directions Vladimir Feinberg, Xinyi Chen, Y. Jennifer Sun, Rohan Anil, Elad Hazan

AISTATS 2022 LocoProp: Enhancing BackProp via Local Loss Optimization Ehsan Amid, Rohan Anil, Manfred Warmuth

NeurIPSW 2022 Fishy: Layerwise Fisher Approximation for Higher-Order Neural Network Optimization Abel Peirson, Ehsan Amid, Yatong Chen, Vladimir Feinberg, Manfred K Warmuth, Rohan Anil

CVPR 2022 Knowledge Distillation: A Good Teacher Is Patient and Consistent Lucas Beyer, Xiaohua Zhai, Amélie Royer, Larisa Markeeva, Rohan Anil, Alexander Kolesnikov

NeurIPS 2021 Efficiently Identifying Task Groupings for Multi-Task Learning Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn

ICLR 2020 Revisiting the Generalization of Adaptive Gradient Methods Naman Agarwal, Rohan Anil, Elad Hazan, Tomer Koren, Cyril Zhang

NeurIPS 2020 Stochastic Optimization with Laggard Data Pipelines Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang

NeurIPS 2019 Memory Efficient Adaptive Optimization Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer

NeurIPS 2019 Robust Bi-Tempered Logistic Loss Based on Bregman Divergences Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren

ICLR 2018 Large Scale Distributed Neural Network Training Through Online Distillation Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton