Bio. I am a second-year Ph.D. student in Computer Science at Stanford University advised by John Duchi. My research interests are in optimization and machine learning.

Previously, I was a M.S. student at Stanford University advised by Stefano Ermon, working on probabilistic models and reinforcement learning. I completed my undergraduate studies at Lycée Louis-Le-Grand and Ecole Polytechnique from which I obtained a B.S. and a M.S. in 2014 and 2015. I also spent internships at Facebook Applied Machine Learning in 2016 and at Google Brain in 2017 where I worked with Jascha Sohl-Dickstein and Matt Hoffman.

Publications

Necessary and Sufficient Geometries for Gradient Methods.
Daniel Levy, John Duchi.
To appear in NeurIPS, 2019. Selected for oral presentation.
[pdf]
Bayesian Optimization and Attribute Adjustement
Stephan Eismann, Daniel Levy, Rui Shu, Stefan Barztsch, Stefano Ermon.
UAI, 2018.
[pdf]
Generalizing Hamiltonian Monte Carlo with Neural Networks
Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein.
ICLR, 2018.
[pdf] [code]
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
Daniel Levy, Stefano Ermon.
AAAI, 2018.
[pdf]
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search
Stephen Mussman*, Daniel Levy*, Stefano Ermon.
UAI, 2017.
[pdf]
Data Noising as Smoothing in Neural Network Language Models
Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Levy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng.
ICLR, 2017.
[pdf]

Teaching

Fall 2016 I was teaching assistant for CS229: Machine Learning taught by Andrew Ng and John Duchi.

Service

Reviewer: ICLR 2020, AAAI 2020, ICML 2019, ICLR 2019, AABI 2018, R2L Workshop (at NeurIPS 2018).