Justin Romberg
14 papers · 2015–2024 · 6 venues · across top CS/AI venues
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Achievements
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Research topics
Machine Learning
(13)
Optimization & Theory
(11)
Mathematics & Optimization
(8)
Optimization
(8)
Deep Learning
(3)
Core Methods
(3)
Mathematics
(3)
Artificial Intelligence
(2)
Reinforcement Learning
(2)
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Core AI
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Learning Paradigms
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Keywords
sample complexity
(4)
convex optimization
(2)
low-rank recovery
(2)
sparse recovery
(2)
convex relaxation
(2)
low-rank matrix
(2)
entropy regularization
(2)
policy optimization
(1)
policy evaluation
(1)
multi-agent reinforcement learning
(1)
statistical learning theory
(1)
function approximation
(1)
ridge regression
(1)
compressive sensing
(1)
minimax optimization
(1)
temporal difference learning
(1)
robust reinforcement learning
(1)
distributed optimization
(1)
semidefinite programming
(1)
network pruning
(1)
Papers
Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks
NeurIPS 2024
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
NeurIPS 2023
PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems
NeurIPS 2023
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
NeurIPS 2022
A decentralized policy gradient approach to multi-task reinforcement learning
UAI 2021
Convex Programming for Estimation in Nonlinear Recurrent Models
JMLR 2020
Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation
L4DC 2020
Sample complexity and effective dimension for regression on manifolds
NeurIPS 2020
Sample complexity bounds for localized sketching
AISTATS 2020
Decentralized sketching of low rank matrices
NeurIPS 2019
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning
ICML 2019
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
NeurIPS 2017
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
AISTATS 2017
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors
NeurIPS 2015