David Simchi-Levi
22 papers · 2018–2025 · 4 venues · across top CS/AI venues
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Achievements
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π£ Hot Topic Early Bird π Conference Polyglot Β· Bronze π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Silver πΊοΈ Taxonomy Completionist (36)
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(104)
π
Century Club Β· Bronze
π₯
Unstoppable Β· Bronze
β‘
Prolific Year Β· Productive
Venues
Top co-authors
Research topics
Machine Learning
(19)
Optimization & Theory
(15)
Mathematics & Optimization
(12)
Optimization
(11)
Learning Types
(9)
Reinforcement Learning
(6)
Methods
(6)
Artificial Intelligence
(5)
Core AI
(4)
Core Methods
(3)
Application Areas
(2)
Data Science & Analytics
(1)
Applications
(1)
Bayesian & Probabilistic
(1)
Learning Paradigms
(1)
Keywords
multi-armed bandit
(6)
regret bound
(6)
online learning
(5)
sample complexity
(4)
dynamic regret
(3)
experimental design
(3)
dynamic pricing
(3)
upper confidence bound
(3)
expected regret
(2)
worst-case optimality
(2)
causal inference
(2)
phase transition
(2)
regret minimization
(2)
non-stationary mdp
(2)
contextual bandit
(2)
pareto optimality
(2)
offline reinforcement learning
(2)
tail risk
(2)
reproducing kernel hilbert space
(1)
density estimation
(1)
Papers
Contextual Online Decision Making with Infinite-Dimensional Functional Regression
ICML 2025
Dynamic Service Fee Pricing under Strategic Behavior: Actions as Instruments and Phase Transition
NeurIPS 2024
Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff
NeurIPS 2024
Privacy Preserving Adaptive Experiment Design
ICML 2024
Multi-armed Bandit Experimental Design: Online Decision-making and Adaptive Inference
AISTATS 2023
Non-stationary Experimental Design under Linear Trends
NeurIPS 2023
Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk
ICML 2023
Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk
NeurIPS 2023
A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk
NeurIPS 2022
Context-Based Dynamic Pricing with Partially Linear Demand Model
NeurIPS 2022
Learning Mixed Multinomial Logits with Provable Guarantees
NeurIPS 2022
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
COLT 2022
Sobolev Norm Learning Rates for Conditional Mean Embeddings
AISTATS 2022
Dynamic Planning and Learning under Recovering Rewards
ICML 2021
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
COLT 2021
Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs
ICML 2021
Reaping the Benefits of Bundling under High Production Costs
AISTATS 2021
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
ICML 2020
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
ICML 2020
Learning to Optimize under Non-Stationarity
AISTATS 2019
Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints
NeurIPS 2019
The Lingering of Gradients: How to Reuse Gradients Over Time
NeurIPS 2018