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Importance weighting without importance weights: An efficient algorithm
  for combinatorial semi-bandits
v1v2 (latest)

Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits

17 March 2015
Gergely Neu
Gábor Bartók
ArXiv (abs)PDFHTML

Papers citing "Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits"

22 / 22 papers shown
Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
Chaiwon Kim
Jongyeong Lee
Min-hwan Oh
142
0
0
14 Oct 2025
CrisiText: A dataset of warning messages for LLM training in emergency communication
CrisiText: A dataset of warning messages for LLM training in emergency communication
Giacomo Gonella
Gian Maria Campedelli
Stefano Menini
Marco Guerini
244
0
0
10 Oct 2025
Efficient Best-of-Both-Worlds Algorithms for Contextual Combinatorial Semi-Bandits
Efficient Best-of-Both-Worlds Algorithms for Contextual Combinatorial Semi-Bandits
Mengmeng Li
Philipp Schneider
Jelisaveta Aleksić
Daniel Kuhn
127
1
0
26 Aug 2025
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
Jongyeong Lee
Junya Honda
Shinji Ito
Min-hwan Oh
226
2
0
26 Aug 2025
Note on Follow-the-Perturbed-Leader in Combinatorial Semi-Bandit Problems
Note on Follow-the-Perturbed-Leader in Combinatorial Semi-Bandit Problems
Botao Chen
Junya Honda
203
0
0
14 Jun 2025
Faster Rates for Private Adversarial Bandits
Faster Rates for Private Adversarial Bandits
Hilal Asi
Vinod Raman
Kunal Talwar
PICVFedML
322
0
0
27 May 2025
Efficient and Optimal No-Regret Caching under Partial Observation
Efficient and Optimal No-Regret Caching under Partial Observation
Younes Ben Mazziane
Francescomaria Faticanti
Sara Alouf
Giovanni Neglia
273
0
0
04 Mar 2025
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits
Mengmeng Li
Daniel Kuhn
Bahar Taşkesen
521
2
0
30 Sep 2024
Matroid Semi-Bandits in Sublinear Time
Matroid Semi-Bandits in Sublinear Time
Ruo-Chun Tzeng
Naoto Ohsaka
Kaito Ariu
289
1
0
28 May 2024
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Yuko Kuroki
Alberto Rumi
Taira Tsuchiya
Fabio Vitale
Nicolò Cesa-Bianchi
338
13
0
24 Dec 2023
Bandit-Driven Batch Selection for Robust Learning under Label Noise
Bandit-Driven Batch Selection for Robust Learning under Label Noise
Michal Lisicki
Mihai Nica
Graham W. Taylor
396
1
0
31 Oct 2023
Learning Effective Strategies for Moving Target Defense with Switching
  Costs
Learning Effective Strategies for Moving Target Defense with Switching Costs
Vignesh Viswanathan
Megha Bose
P. Paruchuri
AAML
98
0
0
24 Jan 2023
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes
  with Bandit Feedback
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit FeedbackNeural Information Processing Systems (NeurIPS), 2022
Yan Dai
Haipeng Luo
Liyu Chen
311
21
0
26 May 2022
Online Boosting with Bandit Feedback
Online Boosting with Bandit FeedbackInternational Conference on Algorithmic Learning Theory (ALT), 2020
Nataly Brukhim
Elad Hazan
246
10
0
23 Jul 2020
Online learning in MDPs with linear function approximation and bandit
  feedback
Online learning in MDPs with linear function approximation and bandit feedback
Gergely Neu
Julia Olkhovskaya
315
39
0
03 Jul 2020
Learning with Differentiable Perturbed Optimizers
Learning with Differentiable Perturbed Optimizers
Quentin Berthet
Mathieu Blondel
O. Teboul
Marco Cuturi
Jean-Philippe Vert
Francis R. Bach
405
125
0
20 Feb 2020
Efficient and Robust Algorithms for Adversarial Linear Contextual
  Bandits
Efficient and Robust Algorithms for Adversarial Linear Contextual BanditsAnnual Conference Computational Learning Theory (COLT), 2020
Gergely Neu
Julia Olkhovskaya
476
53
0
01 Feb 2020
Minimax Optimal Algorithms for Adversarial Bandit Problem with Multiple
  Plays
Minimax Optimal Algorithms for Adversarial Bandit Problem with Multiple PlaysIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2019
Nuri Mert Vural
Hakan Gokcesu
Kaan Gokcesu
Suleyman S. Kozat
201
19
0
25 Nov 2019
Adversarial Bandits with Knapsacks
Adversarial Bandits with Knapsacks
Nicole Immorlica
Karthik Abinav Sankararaman
Robert Schapire
Aleksandrs Slivkins
775
133
0
28 Nov 2018
Small-loss bounds for online learning with partial information
Small-loss bounds for online learning with partial information
Thodoris Lykouris
Karthik Sridharan
Éva Tardos
337
42
0
09 Nov 2017
Tight Bounds for Bandit Combinatorial Optimization
Tight Bounds for Bandit Combinatorial OptimizationAnnual Conference Computational Learning Theory (COLT), 2017
Alon Cohen
Tamir Hazan
Tomer Koren
318
24
0
24 Feb 2017
Solving Combinatorial Games using Products, Projections and
  Lexicographically Optimal Bases
Solving Combinatorial Games using Products, Projections and Lexicographically Optimal Bases
Swati Gupta
M. Goemans
Patrick Jaillet
180
12
0
01 Mar 2016
1
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