Nadav Merlis
17 papers · 2018–2026 · 6 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (6) π£ Hot Topic Early Bird π§ Keyword Pioneer π Interdisciplinary Bridge π Academic Marathon (7)
π
Cross-Pollinator
(5)
π
Renaissance Researcher
(5)
πΊοΈ
Taxonomy Completionist
(30)
π
Grand Slam
π§¬
Topic Evolution
π
Century Club
(16)
ποΈ
Keyword Collector
(69)
π₯
Unstoppable
(8)
Conferences
NIPS (6)
ICML (4)
AAAI (3)
COLT (2)
AISTATS (1)
ICLR (1)
Top co-authors
Keywords
regret bound
(12)
online learning
(7)
reinforcement learning
(5)
multi-armed bandit
(4)
markov decision process
(3)
thompson sampling
(3)
contextual bandit
(3)
stochastic optimization
(2)
deep q-network
(2)
online algorithm
(2)
regret analysis
(2)
model-based reinforcement learning
(2)
constraint satisfaction
(1)
dynamic programming
(1)
exploration-exploitation tradeoff
(1)
offline reinforcement learning
(1)
deep reinforcement learning
(1)
policy learning
(1)
autonomous driving
(1)
greedy policy
(1)
Papers
Online Linear Regression with Paid Stochastic Features
AAAI 2026
On Bits and Bandits: Quantifying the Regret-Information Trade-off
ICLR 2025
The Value of Reward Lookahead in Reinforcement Learning
NIPS 2024
Improved Algorithms for Contextual Dynamic Pricing
NIPS 2024
Multi-armed bandits with guaranteed revenue per arm
AISTATS 2024
Reinforcement Learning with Lookahead Information
NIPS 2024
On Preemption and Learning in Stochastic Scheduling
ICML 2023
Reinforcement Learning with History Dependent Dynamic Contexts
ICML 2023
Reinforcement Learning with a Terminator
NIPS 2022
Reinforcement Learning with Trajectory Feedback
AAAI 2021
Lenient Regret for Multi-Armed Bandits
AAAI 2021
Confidence-Budget Matching for Sequential Budgeted Learning
ICML 2021
Ensemble Bootstrapping for Q-Learning
ICML 2021
Tight Lower Bounds for Combinatorial Multi-Armed Bandits
COLT 2020
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
NIPS 2019
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem
COLT 2019
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
NIPS 2018