Wide & Deep Learning for Recommender Systems. - Heng-Tze Cheng; Levent Koc 001; Jeremiah Harmsen; Tal Shaked; Tushar Chandra; Hrishi Aradhye; Glen Anderson; Greg Corrado; Wei Chai; Mustafa Ispir; Rohan Anil; Zakaria Haque; et al. (DLRS@RecSys, 2016)

Large scale distributed neural network training through online distillation. - Rohan Anil; Gabriel Pereyra; Alexandre Passos; Róbert Ormándi; George E. Dahl; Geoffrey E. Hinton (ICLR (Poster), 2018)

TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. - Rama Kumar Pasumarthi; Sebastian Bruch 001; Xuanhui Wang; Cheng Li 012; Michael Bendersky; Marc Najork; Jan Pfeifer; Nadav Golbandi; Rohan Anil; Stephan Wolf (KDD, 2019)

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

Memory Efficient Adaptive Optimization. - Rohan Anil; Vineet Gupta 001; Tomer Koren; Yoram Singer (NeurIPS, 2019)

Memory-Efficient Adaptive Optimization for Large-Scale Learning. - Rohan Anil; Vineet Gupta 001; Tomer Koren; Yoram Singer (CoRR, 2019)

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling. - Jonathan Shen; Patrick Nguyen; Yonghui Wu; Zhifeng Chen; Mia Xu Chen; Ye Jia; Anjuli Kannan; Tara N. Sainath; Yuan Cao 007; Chung-Cheng Chiu; Yanzhang He; Jan Chorowski; et al. (CoRR, 2019)

Stochastic Optimization with Laggard Data Pipelines. - Naman Agarwal; Rohan Anil; Tomer Koren; Kunal Talwar; Cyril Zhang (NeurIPS, 2020)

Second Order Optimization Made Practical. - Rohan Anil; Vineet Gupta 001; Tomer Koren; Kevin Regan 001; Yoram Singer (CoRR, 2020)

Disentangling Adaptive Gradient Methods from Learning Rates. - Naman Agarwal; Rohan Anil; Elad Hazan; Tomer Koren; Cyril Zhang (CoRR, 2020)

Measuring and Harnessing Transference in Multi-Task Learning. - Christopher Fifty; Ehsan Amid; Zhe Zhao 001; Tianhe Yu; Rohan Anil; Chelsea Finn (CoRR, 2020)

Efficiently Identifying Task Groupings for Multi-Task Learning. - Chris Fifty; Ehsan Amid; Zhe Zhao 001; Tianhe Yu; Rohan Anil; Chelsea Finn (NeurIPS, 2021)

A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes. - Zachary Nado; Justin Gilmer; Christopher J. Shallue; Rohan Anil; George E. Dahl (CoRR, 2021)

LocoProp: Enhancing BackProp via Local Loss Optimization. - Ehsan Amid; Rohan Anil; Manfred K. Warmuth (AISTATS, 2022)

Knowledge distillation: A good teacher is patient and consistent. - Lucas Beyer; Xiaohua Zhai; Amélie Royer; Larisa Markeeva; Rohan Anil; Alexander Kolesnikov 003 (CVPR, 2022)

Large-Scale Differentially Private BERT. - Rohan Anil; Badih Ghazi; Vineet Gupta 001; Ravi Kumar 001; Pasin Manurangsi (EMNLP (Findings), 2022)

On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models. - Rohan Anil; Sandra Gadanho; Da Huang; Nijith Jacob; Zhuoshu Li; Dong Lin; Todd Phillips; Cristina Pop 006; Kevin Regan 001; Gil I. Shamir; Rakesh Shivanna; Qiqi Yan (ORSUM@RecSys, 2022)

Step-size Adaptation Using Exponentiated Gradient Updates. - Ehsan Amid; Rohan Anil; Christopher Fifty; Manfred K. Warmuth (CoRR, 2022)

Learning from Randomly Initialized Neural Network Features. - Ehsan Amid; Rohan Anil; Wojciech Kotlowski; Manfred K. Warmuth (CoRR, 2022)

N-Grammer: Augmenting Transformers with latent n-grams. - Aurko Roy; Rohan Anil; Guangda Lai; Benjamin Lee; Jeffrey Zhao; Shuyuan Zhang 002; Shibo Wang; Ye Zhang; Shen Wu; Rigel Swavely; Tao Yu; Phuong Dao; et al. (CoRR, 2022)

Layerwise Bregman Representation Learning with Applications to Knowledge Distillation. - Ehsan Amid; Rohan Anil; Christopher Fifty; Manfred K. Warmuth (CoRR, 2022)

A Computationally Efficient Sparsified Online Newton Method. - Devvrit; Sai Surya Duvvuri; Rohan Anil; Vineet Gupta 001; Cho-Jui Hsieh; Inderjit S. Dhillon (NeurIPS, 2023)

Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions. - Vladimir Feinberg; Xinyi Chen 001; Y. Jennifer Sun; Rohan Anil; Elad Hazan (NeurIPS, 2023)

PaLM 2 Technical Report. - 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; et al. (CoRR, 2023)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (CoRR, 2023)

Heterogeneous Federated Learning Using Knowledge Codistillation. - Jared Lichtarge; Ehsan Amid; Shankar Kumar; Tien-Ju Yang; Rohan Anil; Rajiv Mathews (CoRR, 2023)

Gemini: A Family of Highly Capable Multimodal Models. - 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; et al. (CoRR, 2023)

Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation. - Ehsan Amid; Rohan Anil; Christopher Fifty; Manfred K. Warmuth (Trans. Mach. Learn. Res., 2023)

Combining Axes Preconditioners through Kronecker Approximation for Deep Learning. - Sai Surya Duvvuri; Devvrit; Rohan Anil; Cho-Jui Hsieh; Inderjit S. Dhillon (ICLR, 2024)

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. - 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; et al. (CoRR, 2024)

Learning from straggler clients in federated learning. - Andrew Hard; Antonious M. Girgis; Ehsan Amid; Sean Augenstein; Lara McConnaughey; Rajiv Mathews; Rohan Anil (CoRR, 2024)

Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries. - Kiran Vodrahalli; Santiago Ontanon; Nilesh Tripuraneni; Kelvin Xu; Sanil Jain; Rakesh Shivanna; Jeffrey Hui; Nishanth Dikkala; Mehran Kazemi; Bahare Fatemi; Rohan Anil; Ethan Dyer; et al. (CoRR, 2024)

A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs. - Ankit Singh Rawat; Veeranjaneyulu Sadhanala; Afshin Rostamizadeh; Ayan Chakrabarti; Wittawat Jitkrittum; Vladimir Feinberg; Seungyeon Kim 001; Hrayr Harutyunyan; Nikunj Saunshi; Zachary Nado; Rakesh Shivanna; Sashank J. Reddi; et al. (CoRR, 2024)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)

Fast and Simplex: 2-Simplicial Attention in Triton. - Aurko Roy; Timothy Chou; Sai Surya Duvvuri; Sijia Chen; Jiecao Yu; Xiaodong Wang; Manzil Zaheer; Rohan Anil (CoRR, 2025)