Nadav Merlis
17 papers · 2018–2026 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (31) π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π
Grand Slam
π
Keyword Champion
π§
Keyword Pioneer
ποΈ
Keyword Collector
(78)
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Century Club Β· Bronze
π₯
Unstoppable Β· Bronze
Top co-authors
Research topics
Machine Learning
(15)
Optimization & Theory
(11)
Reinforcement Learning
(9)
Learning Types Β· Machine Learning
(9)
Mathematics & Optimization
(8)
Optimization
(8)
Methods
(7)
Artificial Intelligence
(3)
Applications
(3)
Core AI
(3)
Application Areas
(2)
Deep Learning
(1)
Core Methods
(1)
Learning Types Β· Deep Learning
(1)
Keywords
regret bound
(13)
online learning
(7)
multi-armed bandit
(5)
reinforcement learning
(5)
contextual bandit
(3)
markov decision process
(3)
thompson sampling
(3)
stochastic optimization
(2)
lower bound
(2)
regret analysis
(2)
deep q-network
(2)
model-based reinforcement learning
(2)
online algorithm
(2)
combinatorial multi-armed bandit
(2)
exploration-exploitation tradeoff
(1)
offline reinforcement learning
(1)
policy learning
(1)
autonomous driving
(1)
active learning
(1)
sample complexity
(1)
Papers
Online Linear Regression with Paid Stochastic Features
AAAI 2026
On Bits and Bandits: Quantifying the Regret-Information Trade-off
ICLR 2025
Improved Algorithms for Contextual Dynamic Pricing
NeurIPS 2024
Multi-armed bandits with guaranteed revenue per arm
AISTATS 2024
Reinforcement Learning with Lookahead Information
NeurIPS 2024
The Value of Reward Lookahead in Reinforcement Learning
NeurIPS 2024
On Preemption and Learning in Stochastic Scheduling
ICML 2023
Reinforcement Learning with History Dependent Dynamic Contexts
ICML 2023
Reinforcement Learning with a Terminator
NeurIPS 2022
Confidence-Budget Matching for Sequential Budgeted Learning
ICML 2021
Ensemble Bootstrapping for Q-Learning
ICML 2021
Lenient Regret for Multi-Armed Bandits
AAAI 2021
Reinforcement Learning with Trajectory Feedback
AAAI 2021
Tight Lower Bounds for Combinatorial Multi-Armed Bandits
COLT 2020
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem
COLT 2019
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
NeurIPS 2019
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
NeurIPS 2018