Bio. I am a first-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 Adaptive Gradient Methods.
Daniel Levy, John Duchi.
To appear in NeurIPS 2019. Selected for oral presentation.
Trading-off Learning and Inference in Deep Latent Variable Models.
Daniel Levy, Stefano Ermon.
UAI Uncertainty in Deep Learning Workshop, 2018.
[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]
LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures
Daniel Levy, Danlu Chen, Stefano Ermon.
NeurIPS Deep Learning: Bridging Theory and Practice Workshop, 2017.
[pdf]
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]
Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks
Daniel Levy, Arzav Jain.
NeurIPS Machine Learning for Healthcare Workshop, 2016.
[pdf]
Homotopies and applications to the study of R-algebras in finite dimension (In French)
Daniel Levy.
Report, 2012.
[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).