Simon S. Du
17 papers · 2017–2024 · 8 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (33) π Academic Marathon Β· Sprint π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
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Keyword Collector
(94)
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Prolific Year Β· Productive
π₯
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(4)
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Top co-authors
Research topics
Keywords
convergence rate
(3)
linear function approximation
(2)
sample complexity
(2)
multi-agent reinforcement learning
(2)
gradient descent
(2)
linear regression
(2)
stochastic gradient descent
(1)
reinforcement learning
(1)
learning theory
(1)
game theory
(1)
principal component analysis
(1)
bayesian inference
(1)
particle filtering
(1)
uncertainty quantification
(1)
convex optimization
(1)
neural tangent kernel
(1)
global convergence
(1)
few-shot learning
(1)
expectation maximization
(1)
knowledge distillation
(1)
Papers
Decoding-Time Language Model Alignment with Multiple Objectives
NeurIPS 2024
Learning Optimal Tax Design in Nonatomic Congestion Games
NeurIPS 2024
Learning to Cooperate with Humans using Generative Agents
NeurIPS 2024
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation (Extended Abstract)
COLT 2024
Toward Global Convergence of Gradient EM for Over-Paramterized Gaussian Mixture Models
NeurIPS 2024
Understanding the Gains from Repeated Self-Distillation
NeurIPS 2024
When is particle filtering efficient for planning in partially observed linear dynamical systems?
UAI 2021
DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs
IJCAI 2020
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
ICLR 2020
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
ICLR 2020
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
JMLR 2020
What Can Neural Networks Reason About?
ICLR 2020
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
ICLR 2019
Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity
AISTATS 2019
When is a Convolutional Filter Easy to Learn?
ICLR 2018
Computationally Efficient Robust Sparse Estimation in High Dimensions
COLT 2017
Stochastic Variance Reduction Methods for Policy Evaluation
ICML 2017