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Rohan Anil
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2020 – today
- 2024
- [c14]Sai Surya Duvvuri, Devvrit, Rohan Anil, Cho-Jui Hsieh, Inderjit S. Dhillon:
Combining Axes Preconditioners through Kronecker Approximation for Deep Learning. ICLR 2024 - [i30]Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy P. Lillicrap, Jean-Baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, Ioannis Antonoglou, Rohan Anil, Sebastian Borgeaud, Andrew M. Dai, Katie Millican, Ethan Dyer, Mia Glaese, Thibault Sottiaux, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, James Molloy, Jilin Chen, Michael Isard, Paul Barham, Tom Hennigan, Ross McIlroy, Melvin Johnson, Johan Schalkwyk, Eli Collins, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, Clemens Meyer, Gregory Thornton, Zhen Yang, Henryk Michalewski, Zaheer Abbas, Nathan Schucher, Ankesh Anand, Richard Ives, James Keeling, Karel Lenc, Salem Haykal, Siamak Shakeri, Pranav Shyam, Aakanksha Chowdhery, Roman Ring, Stephen Spencer, Eren Sezener, et al.:
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. CoRR abs/2403.05530 (2024) - [i29]Andrew Hard, Antonious M. Girgis, Ehsan Amid, Sean Augenstein, Lara McConnaughey, Rajiv Mathews, Rohan Anil:
Learning from straggler clients in federated learning. CoRR abs/2403.09086 (2024) - [i28]Kiran Vodrahalli, Santiago Ontanon, Nilesh Tripuraneni, Kelvin Xu, Sanil Jain, Rakesh Shivanna, Jeffrey Hui, Nishanth Dikkala, Mehran Kazemi, Bahare Fatemi, Rohan Anil, Ethan Dyer, Siamak Shakeri, Roopali Vij, Harsh Mehta, Vinay V. Ramasesh, Quoc Le, Ed H. Chi, Yifeng Lu, Orhan Firat, Angeliki Lazaridou, Jean-Baptiste Lespiau, Nithya Attaluri, Kate Olszewska:
Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries. CoRR abs/2409.12640 (2024) - [i27]Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh, Ayan Chakrabarti, Wittawat Jitkrittum, Vladimir Feinberg, Seungyeon Kim, Hrayr Harutyunyan, Nikunj Saunshi, Zachary Nado, Rakesh Shivanna, Sashank J. Reddi, Aditya Krishna Menon, Rohan Anil, Sanjiv Kumar:
A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs. CoRR abs/2410.18779 (2024) - 2023
- [j1]Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth:
Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation. Trans. Mach. Learn. Res. 2023 (2023) - [c13]Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon:
A Computationally Efficient Sparsified Online Newton Method. NeurIPS 2023 - [c12]Vladimir Feinberg, Xinyi Chen, Y. Jennifer Sun, Rohan Anil, Elad Hazan:
Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions. NeurIPS 2023 - [i26]Vladimir Feinberg, Xinyi Chen, Y. Jennifer Sun, Rohan Anil, Elad Hazan:
Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions. CoRR abs/2302.03764 (2023) - [i25]Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H. Clark, Laurent El Shafey, Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernández Ábrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan A. Botha, James Bradbury, Siddhartha Brahma, Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, Clément Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du, Ethan Dyer, Vladimir Feinberg, Fangxiaoyu Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas Gonzalez, et al.:
PaLM 2 Technical Report. CoRR abs/2305.10403 (2023) - [i24]George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson:
Benchmarking Neural Network Training Algorithms. CoRR abs/2306.07179 (2023) - [i23]Jared Lichtarge, Ehsan Amid, Shankar Kumar, Tien-Ju Yang, Rohan Anil, Rajiv Mathews:
Heterogeneous Federated Learning Using Knowledge Codistillation. CoRR abs/2310.02549 (2023) - [i22]Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S. Dhillon:
A Computationally Efficient Sparsified Online Newton Method. CoRR abs/2311.10085 (2023) - [i21]Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Slav Petrov, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy P. Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul Ronald Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anaïs White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, et al.:
Gemini: A Family of Highly Capable Multimodal Models. CoRR abs/2312.11805 (2023) - 2022
- [c11]Ehsan Amid, Rohan Anil, Manfred K. Warmuth:
LocoProp: Enhancing BackProp via Local Loss Optimization. AISTATS 2022: 9626-9642 - [c10]Lucas Beyer, Xiaohua Zhai, Amélie Royer, Larisa Markeeva, Rohan Anil, Alexander Kolesnikov:
Knowledge distillation: A good teacher is patient and consistent. CVPR 2022: 10915-10924 - [c9]Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi:
Large-Scale Differentially Private BERT. EMNLP (Findings) 2022: 6481-6491 - [c8]Rohan Anil, Sandra Gadanho, Da Huang, Nijith Jacob, Zhuoshu Li, Dong Lin, Todd Phillips, Cristina Pop, Kevin Regan, Gil I. Shamir, Rakesh Shivanna, Qiqi Yan:
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models. ORSUM@RecSys 2022 - [i20]Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth:
Step-size Adaptation Using Exponentiated Gradient Updates. CoRR abs/2202.00145 (2022) - [i19]Ehsan Amid, Rohan Anil, Wojciech Kotlowski, Manfred K. Warmuth:
Learning from Randomly Initialized Neural Network Features. CoRR abs/2202.06438 (2022) - [i18]Aurko Roy, Rohan Anil, Guangda Lai, Benjamin Lee, Jeffrey Zhao, Shuyuan Zhang, Shibo Wang, Ye Zhang, Shen Wu, Rigel Swavely, Tao Yu, Phuong Dao, Christopher Fifty, Zhifeng Chen, Yonghui Wu:
N-Grammer: Augmenting Transformers with latent n-grams. CoRR abs/2207.06366 (2022) - [i17]Rohan Anil, Sandra Gadanho, Da Huang, Nijith Jacob, Zhuoshu Li, Dong Lin, Todd Phillips, Cristina Pop, Kevin Regan, Gil I. Shamir, Rakesh Shivanna, Qiqi Yan:
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models. CoRR abs/2209.05310 (2022) - [i16]Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth:
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation. CoRR abs/2209.07080 (2022) - 2021
- [c7]Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn:
Efficiently Identifying Task Groupings for Multi-Task Learning. NeurIPS 2021: 27503-27516 - [i15]Zachary Nado, Justin Gilmer, Christopher J. Shallue, Rohan Anil, George E. Dahl:
A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes. CoRR abs/2102.06356 (2021) - [i14]Lucas Beyer, Xiaohua Zhai, Amélie Royer, Larisa Markeeva, Rohan Anil, Alexander Kolesnikov:
Knowledge distillation: A good teacher is patient and consistent. CoRR abs/2106.05237 (2021) - [i13]Ehsan Amid, Rohan Anil, Manfred K. Warmuth:
LocoProp: Enhancing BackProp via Local Loss Optimization. CoRR abs/2106.06199 (2021) - [i12]Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi:
Large-Scale Differentially Private BERT. CoRR abs/2108.01624 (2021) - [i11]Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn:
Efficiently Identifying Task Groupings for Multi-Task Learning. CoRR abs/2109.04617 (2021) - 2020
- [c6]Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang:
Stochastic Optimization with Laggard Data Pipelines. NeurIPS 2020 - [i10]Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer:
Second Order Optimization Made Practical. CoRR abs/2002.09018 (2020) - [i9]Naman Agarwal, Rohan Anil, Elad Hazan, Tomer Koren, Cyril Zhang:
Disentangling Adaptive Gradient Methods from Learning Rates. CoRR abs/2002.11803 (2020) - [i8]Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang:
Stochastic Optimization with Laggard Data Pipelines. CoRR abs/2010.13639 (2020) - [i7]Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn:
Measuring and Harnessing Transference in Multi-Task Learning. CoRR abs/2010.15413 (2020)
2010 – 2019
- 2019
- [c5]Rama Kumar Pasumarthi, Sebastian Bruch
, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. KDD 2019: 2970-2978 - [c4]Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer:
Memory Efficient Adaptive Optimization. NeurIPS 2019: 9746-9755 - [c3]Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren:
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. NeurIPS 2019: 14987-14996 - [i6]Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer:
Memory-Efficient Adaptive Optimization for Large-Scale Learning. CoRR abs/1901.11150 (2019) - [i5]Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia Xu Chen, Ye Jia, Anjuli Kannan, Tara N. Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George F. Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel Bacchiani, Thomas B. Jablin, Robert Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon:
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling. CoRR abs/1902.08295 (2019) - [i4]Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren:
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. CoRR abs/1906.03361 (2019) - 2018
- [c2]Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton:
Large scale distributed neural network training through online distillation. ICLR (Poster) 2018 - [i3]Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton:
Large scale distributed neural network training through online distillation. CoRR abs/1804.03235 (2018) - [i2]Rama Kumar Pasumarthi, Xuanhui Wang, Cheng Li, Sebastian Bruch, Michael Bendersky, Marc Najork
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. CoRR abs/1812.00073 (2018) - 2016
- [c1]Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, Hemal Shah:
Wide & Deep Learning for Recommender Systems. DLRS@RecSys 2016: 7-10 - [i1]Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, Hemal Shah:
Wide & Deep Learning for Recommender Systems. CoRR abs/1606.07792 (2016)
Coauthor Index
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