Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning. - Vladimir Feinberg; Alvin Wan; Ion Stoica; Michael I. Jordan; Joseph E. Gonzalez; Sergey Levine (CoRR, 2018)

On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent. - Noah Golmant; Nikita Vemuri; Zhewei Yao; Vladimir Feinberg; Amir Gholami; Kai Rothauge; Michael W. Mahoney; Joseph Gonzalez 001 (CoRR, 2018)

Chromatic Learning for Sparse Datasets. - Vladimir Feinberg; Peter Bailis (CoRR, 2020)

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)

FutureFill: Fast Generation from Convolutional Sequence Models. - Naman Agarwal; Xinyi Chen 001; Evan Dogariu; Vladimir Feinberg; Daniel Suo; Peter L. Bartlett; Elad Hazan (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)