Web: http://arxiv.org/abs/2208.02362

Sept. 22, 2022, 1:13 a.m. | Samarth Gupta, Daniel N. Hill, Lexing Ying, Inderjit Dhillon

stat.ML updates on arXiv.org arxiv.org

In most applications of model-based Markov decision processes, the parameters
for the unknown underlying model are often estimated from the empirical data.
Due to noise, the policy learnedfrom the estimated model is often far from the
optimal policy of the underlying model. When applied to the environment of the
underlying model, the learned policy results in suboptimal performance, thus
calling for solutions with better generalization performance. In this work we
take a Bayesian perspective and regularize the objective function of …

arxiv bayesian regularization

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Research Engineer - VFX, Neural Compositing

@ Flawless | Los Angeles, California, United States

[Job-TB] Senior Data Engineer

@ CI&T | Brazil

Data Analytics Engineer

@ The Fork | Paris, France