Julian Zimmert
30 papers · 2018–2026 · 7 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (28) π Academic Marathon Β· Sprint π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π¬
Deep Specialist
(13)
π
Keyword Champion
(2)
β‘
Prolific Year Β· Productive
π₯
Unstoppable Β· Bronze
ποΈ
Keyword Collector
(103)
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Century Club Β· Bronze
Top co-authors
Research topics
Keywords
regret bound
(26)
multi-armed bandit
(12)
online learning
(12)
contextual bandit
(6)
adversarial bandit
(4)
adversarial learning
(3)
bandit feedback
(3)
model selection
(3)
linear mdp
(2)
optimistic algorithm
(2)
adversarial loss
(2)
delayed feedback
(2)
linear bandit
(2)
stochastic bandit
(2)
reinforcement learning
(2)
asymptotic optimality
(2)
online mirror descent
(2)
stochastic optimization
(2)
stochastic process
(2)
thompson sampling
(2)
Papers
Efficient Opportunistic Approachability
ALT 2026
A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays
NeurIPS 2024
Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback
NeurIPS 2024
PRODuctive bandits: Importance Weighting No More
NeurIPS 2024
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
ICLR 2024
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond
COLT 2023
A Unified Algorithm for Stochastic Path Problems
ALT 2023
Best of Both Worlds Policy Optimization
ICML 2023
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
NeurIPS 2023
Optimal cross-learning for contextual bandits with unknown context distributions
NeurIPS 2023
Refined Regret for Adversarial MDPs with Linear Function Approximation
ICML 2023
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback
NeurIPS 2022
A Model Selection Approach for Corruption Robust Reinforcement Learning
ALT 2022
Efficient Methods for Online Multiclass Logistic Regression
ALT 2022
Open Problem: Finite-Time Instance Dependent Optimality for Stochastic Online Learning with Feedback Graphs
COLT 2022
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States
COLT 2022
Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits
COLT 2022
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
NeurIPS 2022
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
NeurIPS 2021
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
NeurIPS 2021
The Pareto Frontier of model selection for general Contextual Bandits
NeurIPS 2021
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
JMLR 2021
Adapting to Misspecification in Contextual Bandits
NeurIPS 2020
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays
AISTATS 2020
Model Selection in Contextual Stochastic Bandit Problems
NeurIPS 2020
Online Learning for Active Cache Synchronization
ICML 2020
An Optimal Algorithm for Stochastic and Adversarial Bandits
AISTATS 2019
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
ICML 2019
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
NeurIPS 2019
Factored Bandits
NeurIPS 2018