2024

• François Bachoc, Tommaso Cesari, Roberto Colomboni, Andrea Paudice. A theoretical framework for zeroth-order budget convex optimization. TMLR 2024.

• Kyoungseok Jang, Junpei Komiyama, Kazutoshi Yamazaki. Fixed-confidence best arm identification in the Bayesian setting. NeurIPS 2024.

• Stephen Pasteris, Alberto Rumi, Maximilian Thiessen, Shota Saito, Atsushi Miyauchi, Fabio Vitale, Mark Herbster. Bandits with abstention under expert advice. NeurIPS 2024.

• Yann Bourreau, Marco Bressan, T-H. Hubert Chan, Qipeng Kuang, Mauro Sozio. Efficient streaming algorithms for graphlet sampling. NeurIPS 2024.

• François Bachoc, Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni. Fair online bilateral trade. NeurIPS 2024.

• Tianyuan Jin, Kyoungseok Jang, Nicolò Cesa-Bianchi. Sparsity-agnostic linear bandits with adaptive adversaries. NeurIPS 2024.

• Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi. Regret analysis of bilateral trade with a smoothed adversary. Journal of Machine Learning Research 25(234):1−36, 2024.

• Nicolò Cesa-Bianchi, Tommaso Cesari, Riccardo Della Vecchia. Cooperative online learning with feedback graphs. TMLR, 2024.

• Giulia Clerici, Pierre Laforgue, Nicolò Cesa-Bianchi. Linear bandits with memory. TMLR featured paper, 2024.

• Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil, Saverio Salzo. High probability bounds for stochastic subgradient schemes with heavy tailed noise. SIAM Journal on Mathematics of Data Science. To appear.

• Marco Bressan, Nicolò Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen. A theory of interpretable approximations. COLT 2024.

• Marco Bressan, Emmanuel Esposito, Maximilian Thiessen. Efficient algorithms for learning monophonic halfspaces in graphs. COLT 2024.

• Marco Bressan, Mauro Sozio. Fully-dynamic approximate decision trees with worst-case update time guarantees. ICML 2024.

• Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice.  Margin-based active learning of multiclass classifiers. Journal of Machine Learning Research 25(127):1−45, 2024.

• Marco Bressan, Leslie Ann Goldberg, Kitty Meeks, Marc Roth.  Counting subgraphs in somewhere dense graphs. SIAM Journal on Computing.  To appear.

• Nataša Bolić, Tommaso Cesari, Roberto Colomboni.  An online learning theory of brokerage. AAMAS 2024.

• Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi. The role of transparency in repeated first-price auctions with unknown valuations. STOC 2024.

• Khaled Eldowa, Andrea Paudice. General tail bounds for non-smooth stochastic mirror descent. AISTATS 2024.

• Juliette Achddou, Nicolò Cesa-Bianchi, Pierre Laforgue. Multitask online learning: listen to the neighborhood buzz. AISTATS 2024.

• Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi. Best-of-both-worlds algorithms for linear contextual bandits. AISTATS 2024. Oral presentation.

• Stephen Pasteris, Alberto Rumi, Fabio Vitale, Nicolò Cesa-Bianchi. Sum-max submodular bandits. AISTATS 2024.

• Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alché-Buc. Sketch in, sketch out: accelerating both learning and inference for structured prediction with kernels. AISTATS 2024.

2023

• Pier Giuseppe Sessa, Pierre Laforgue, Nicolò Cesa-Bianchi, Andreas Krause. Multitask learning with no regret: from improved confidence bounds to active learning. NeurIPS 2023.

• Khaled Eldowa, Emmanuel Esposito, Tommaso Cesari, Nicolò Cesa-Bianchi. On the minimax regret for online learning with feedback graphs. NeurIPS 2023. Spotlight presentation.

• Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc. Fast kernel methods for generic Lipschitz losses via p-sparsified sketches. Transactions on Machine Learning Research, 2023.

• Stephan Clémençon, Pierre Laforgue, Robin Vogel. Fighting selection bias in statistical learning: application to visual recognition from biased image databases. Journal of Nonparametric Statistics, 2023.

• Dirk van der Hoeven, Lukas Zierahn, Tal Lancewicki, Aviv Rosenberg, Nicolò Cesa-Bianchi. A unified analysis of nonstochastic delayed feedback for combinatorial semi-bandits, linear bandits, and MDPs. COLT 2023.

• Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi. Repeated bilateral trade against a smoothed adversary. COLT 2023.

• Emmanuel Esposito, Saeed Masoudian, Hao Qiu, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Yevgeny Seldin. Delayed bandits: when do intermediate observations help? ICML 2023.

• Dirk van der Hoeven, Ciara Pike-Burke, Hao Qiu, Nicolò Cesa-Bianchi. Trading-off payments and accuracy in online classification with paid stochastic experts. ICML 2023.

• Giulia Clerici, Marco Tiraboschi. Citation is not collaboration: music-genre dependence of graph-related metrics in a music credits network. Sound and Music Computing Conference 2023.

• Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Metelli, Marcello Restelli. Information-theoretic regret bounds for bandits with fixed expert advice. ITW 2023.

• Marco Bressan, Matthias Lanzinger, Marc Roth. The complexity of pattern counting in directed graphs, parameterised by the outdegree. ACM STOC 2023.

• Lukas Zierahn, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Gergely Neu. Nonstochastic contextual combinatorial bandits. AISTATS 2023.

• Nicolò Cesa-Bianchi, Tommaso Cesari, Takayuki Osogami, Marco Scarsini, Segev Wasserkrug. Learning the Stackelberg equilibrium in a newsvendor game. AAMAS 2023.

• Marco Bressan, Gabriel Damay, Mauro Sozio. Fully-dynamic decision trees. AAAI 2023.

• Marco Bressan, Leslie Ann Goldberg, Kitty Meeks, Marc Roth. Counting subgraphs in somewhere dense graphs. ITCS 2023.

• Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi. Bilateral trade: a regret minimization perspective. Mathematics of Operations Research, 2023.

• Marco Bressan, Enoch Peserico, Luca Pretto. Sublinear algorithms for local graph-centrality estimation. SIAM Journal on Computing 52(4):968-1008, 2023.

2022

• Stephan Clémençon, Pierre Laforgue. Statistical learning from biased training samples. Electron. J. Statist. 16(2):6086-6134, 2022. 

• Chloé Rouyer, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Yevgeny Seldin. A near-optimal best-of-both-worlds algorithm for online learning with feedback graphs. NeurIPS 2022.

• Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice, Maximilian Thiessen. Active learning of classifiers with label and seed queries. NeurIPS 2022.

• Dirk van der Hoeven, Nikita Zhivotovskiy, Nicolò Cesa-Bianchi. A regret-variance trade-off in online learning. NeurIPS 2022.

• Emmanuel Esposito, Federico Fusco, Dirk van der Hoeven, Nicolò Cesa-Bianchi. Learning on the edge: online learning with stochastic feedback graphs. NeurIPS 2022.

• Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Claudio Gentile, and Yishay Mansour. Nonstochastic bandits with composite anonymous feedback. Journal of Machine Learning Research, 23(227):1-24, 2022.

• Dirk van der Hoeven and Nicolò Cesa-Bianchi. Nonstochastic bandits and experts with arm-dependent delays. AISTATS 2022.

• Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, and Ran Gilad-Bachrach. A last switch dependent analysis of satiation and seasonality in bandits. AISTATS 2022.

• Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, and Massimiliano Pontil. Multitask online mirror descent. Transactions on Machine Learning Research, 2022.

• Dirk van der Hoeven, Hédi Hadiji, and Tim van Erven. Distributed online learning for joint regret with communication constraints. ALT 2022.