Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits. - Alexandra Carpentier; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Peter Auer (ALT, 2011)

Finite Time Analysis of Stratified Sampling for Monte Carlo. - Alexandra Carpentier; Rémi Munos (NIPS, 2011)

Sparse Recovery with Brownian Sensing. - Alexandra Carpentier; Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2011)

Minimax Number of Strata for Online Stratified Sampling Given Noisy Samples. - Alexandra Carpentier; Rémi Munos (ALT, 2012)

Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions. - Alexandra Carpentier; Rémi Munos (NIPS, 2012)

Online allocation and homogeneous partitioning for piecewise constant mean-approximation. - Alexandra Carpentier; Odalric-Ambrym Maillard (NIPS, 2012)

Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button. - Joan Fruitet; Alexandra Carpentier; Rémi Munos; Maureen Clerc (NIPS, 2012)

Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit. - Alexandra Carpentier; Rémi Munos (AISTATS, 2012)

Toward Optimal Stratification for Stratified Monte-Carlo Integration. - Alexandra Carpentier; Rémi Munos (ICML (2), 2013)

Stochastic Simultaneous Optimistic Optimization. - Michal Valko; Alexandra Carpentier; Rémi Munos (ICML (2), 2013)

Extreme bandits. - Alexandra Carpentier; Michal Valko (NIPS, 2014)

Minimax number of strata for online stratified sampling: The case of noisy samples. - Alexandra Carpentier; Rémi Munos (Theor. Comput. Sci., 2014)

Implementable confidence sets in high dimensional regression. - Alexandra Carpentier (AISTATS, 2015)

Simple regret for infinitely many armed bandits. - Alexandra Carpentier; Michal Valko (ICML, 2015)

Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits. - Alexandra Carpentier; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Peter Auer; András Antos (CoRR, 2015)

Adaptive strategy for stratified Monte Carlo sampling. - Alexandra Carpentier; Rémi Munos; András Antos (J. Mach. Learn. Res., 2015)

Learning Relationships between Data Obtained Independently. - Alexandra Carpentier; Teresa Schlueter (AISTATS, 2016)

Revealing Graph Bandits for Maximizing Local Influence. - Alexandra Carpentier; Michal Valko (AISTATS, 2016)

Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem. - Alexandra Carpentier; Andrea Locatelli (COLT, 2016)

Pliable Rejection Sampling. - Akram Erraqabi; Michal Valko; Alexandra Carpentier; Odalric-Ambrym Maillard (ICML, 2016)

An optimal algorithm for the Thresholding Bandit Problem. - Andrea Locatelli; Maurilio Gutzeit; Alexandra Carpentier (ICML, 2016)

Adaptivity to Noise Parameters in Nonparametric Active Learning. - Andrea Locatelli; Alexandra Carpentier; Samory Kpotufe (COLT, 2017)

Two-Sample Tests for Large Random Graphs Using Network Statistics. - Debarghya Ghoshdastidar; Maurilio Gutzeit; Alexandra Carpentier; Ulrike von Luxburg (COLT, 2017)

An Adaptive Strategy for Active Learning with Smooth Decision Boundary. - Andrea Locatelli; Alexandra Carpentier; Samory Kpotufe (ALT, 2018)

Adaptivity to Smoothness in X-armed bandits. - Andrea Locatelli; Alexandra Carpentier (COLT, 2018)

Contextual Bandits under Delayed Feedback. - Claire Vernade; Alexandra Carpentier; Giovanni Zappella; Beyza Ermis; Michael Brückner (CoRR, 2018)

Active multiple matrix completion with adaptive confidence sets. - Andrea Locatelli; Alexandra Carpentier; Michal Valko (AISTATS, 2019)

Rotting bandits are no harder than stochastic ones. - Julien Seznec; Andrea Locatelli; Alexandra Carpentier; Alessandro Lazaric; Michal Valko (AISTATS, 2019)

A minimax near-optimal algorithm for adaptive rejection sampling. - Juliette Achdou; Joseph Lam-Weil; Alexandra Carpentier; Gilles Blanchard (ALT, 2019)

Minimax Rate of Testing in Sparse Linear Regression. - Alexandra Carpentier; Olivier Collier; Laëtitia Comminges; Alexandre B. Tsybakov; Yuhao Wang 005 (Autom. Remote. Control., 2019)

Local minimax rates for closeness testing of discrete distributions. - Joseph Lam-Weil; Alexandra Carpentier; Bharath K. Sriperumbudur (CoRR, 2019)

Restless dependent bandits with fading memory. - Oleksandr Zadorozhnyi; Gilles Blanchard; Alexandra Carpentier (CoRR, 2019)

The Influence of Shape Constraints on the Thresholding Bandit Problem. - James Cheshire; Pierre Ménard; Alexandra Carpentier (COLT, 2020)

Stochastic bandits with arm-dependent delays. - Anne Gael Manegueu; Claire Vernade; Alexandra Carpentier; Michal Valko (ICML, 2020)

Linear bandits with Stochastic Delayed Feedback. - Claire Vernade; Alexandra Carpentier; Tor Lattimore; Giovanni Zappella; Beyza Ermis; Michael Brückner (ICML, 2020)

The Elliptical Potential Lemma Revisited. - Alexandra Carpentier; Claire Vernade; Yasin Abbasi-Yadkori (CoRR, 2020)

Problem Dependent View on Structured Thresholding Bandit Problems. - James Cheshire; Pierre Ménard; Alexandra Carpentier (ICML, 2021)

Bandits with many optimal arms. - Rianne de Heide; James Cheshire; Pierre Ménard; Alexandra Carpentier (NeurIPS, 2021)

Generalized non-stationary bandits. - Anne Gael Manegueu; Alexandra Carpentier; Yi Yu (CoRR, 2021)

Goodness-of-Fit Testing for Hölder-Continuous Densities: Sharp Local Minimax Rates. - Julien Chhor; Alexandra Carpentier (CoRR, 2021)

The price of unfairness in linear bandits with biased feedback. - Solenne Gaucher; Alexandra Carpentier; Christophe Giraud 002 (NeurIPS, 2022)

Online Learning with Feedback Graphs: The True Shape of Regret. - Tomás Kocák; Alexandra Carpentier (ICML, 2023)

Active Ranking of Experts Based on their Performances in Many Tasks. - El Mehdi Saad; Nicolas Verzelen; Alexandra Carpentier (ICML, 2023)

On Weak Regret Analysis for Dueling Bandits. - El Mehdi Saad; Alexandra Carpentier; Tomás Kocák; Nicolas Verzelen (NeurIPS, 2024)

Optimal rates for ranking a permuted isotonic matrix in polynomial time. - Emmanuel Pilliat; Alexandra Carpentier; Nicolas Verzelen (SODA, 2024)

Active clustering with bandit feedback. - Victor Thuot; Alexandra Carpentier; Christophe Giraud 002; Nicolas Verzelen (CoRR, 2024)

A simple and improved algorithm for noisy, convex, zeroth-order optimisation. - Alexandra Carpentier (CoRR, 2024)

Optimal level set estimation for non-parametric tournament and crowdsourcing problems. - Maximilian Graf; Alexandra Carpentier; Nicolas Verzelen (CoRR, 2024)

Clustering with bandit feedback: breaking down the computation/information gap. - Victor Thuot; Alexandra Carpentier; Christophe Giraud 002; Nicolas Verzelen (ALT, 2025)