Multi-Agent Online Learning

• N. Cesa-Bianchi, T. Cesari, and R. Della Vecchia.
  Cooperative online learning with feedback graphs.
  Transactions on Machine Learning Research, 2024.

• J. Achddou, N. Cesa-Bianchi, and P. Laforgue
  Multitask online learning: listen to the neighborhood buzz
  Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

• P.G. Sessa, P. Laforgue, N. Cesa-Bianchi, and A. Krause
  Multitask learning with no regret: from improved confidence bounds to active learning
  Advances in Neural Information Processing Systems 36 (NeurIPS), 2023.

• N. Cesa-Bianchi, P. Laforgue, A. Paudice, and M. Pontil
  Multitask online mirror descent
  Transactions on Machine Learning Research, 2022.

• N. Cesa-Bianchi, T. Cesari, C. Monteleoni
  Cooperative online learning: keeping your neighbors updated
  Proceedings of the 31st International Conference on Algorithmic Learning Theory (ALT 2020). PMLR, 117:234-250, 2020.

• N. Cesa-Bianchi, C. Gentile, and Y. Mansour
  Delay and cooperation in nonstochastic bandits
  Journal of Machine Learning Research. JMLR, 20(17):1-38, 2019.

• M. Bressan, N. Cesa-Bianchi, S. Lattanzi, and A. Paudice
  Margin-based active learning of classifiers
  Journal of Machine Learning Research. 25(127):1-45, 2024.

• M. Bressan, N. Cesa-Bianchi, S. Lattanzi, A. Paudice, and M. Thiessen
  Active learning of classifiers with label and seed queries
  Advances in Neural Information Processing Systems 35 (NeurIPS), 2022.

• M. Bressan, N. Cesa-Bianchi, S. Lattanzi, and A. Paudice
  On margin-based cluster recovery with oracle queries
  Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

• M. Bressan, N. Cesa-Bianchi, S. Lattanzi, and A. Paudice
  Exact recovery of clusters in finite metric spaces using oracle queries
  Proceedings of the 34th Annual Conference on Learning Theory (COLT). PMLR 134:775-803, 2021.

• M. Bressan, N. Cesa-Bianchi, S. Lattanzi, and A. Paudice
  Exact recovery of mangled clusters with same-cluster queries
  Advances in Neural Information Processing Systems 33 (NeurIPS), 2020 (oral presentation).

• M. Bressan, N. Cesa-Bianchi, A. Paudice, and F. Vitale
  Correlation clustering with adaptive similarity queries
  Advances in Neural Information Processing Systems 32 (NeurIPS), 2019.

• N. Cesa-Bianchi, T. Cesari, R. Colomboni, F. Fusco, and S. Leonardi.
  Regret analysis of bilateral trade with a smoothed adversary.
  Journal of Machine Learning Research 25(234):1-36, 2024.

• N. Cesa-Bianchi, T. Cesari, R. Colomboni, F. Fusco, and S. Leonardi.
  The role of transparency in repeated first-price auctions with unknown valuations.
  56th Annual ACM Symposium on Theory of Computing (STOC), 2024.

• N. Cesa-Bianchi, T. Cesari, R. Colomboni, F. Fusco, and S. Leonardi
  Bilateral trade: A regret minimization perspective
  Mathematics of Operations Research. 49(1):171-203, 2024.

• N. Cesa-Bianchi, T. Cesari, T. Osogami, M. Scarsini, and S. Wasserkrug
  Learning the Stackelberg equilibrium in a newsvendor game
  Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Pages 242-250, 2023.

• N. Cesa-Bianchi, T. Cesari, and V. Perchet
  Dynamic pricing with finitely many unknown valuations
  Proceedings of the 30th International Conference on Algorithmic Learning Theory (ALT). PMLR, 98:247-273, 2019.

• N. Cesa-Bianchi, C. Gentile, and Y. Mansour
  Regret minimization for reserve prices in second-price auctions
  IEEE Transactions on Information Theory, 61(1):549-564, 2015.

G. Clerici, P. Laforgue, and N. Cesa-Bianchi.
Linear bandits with memory.
Transactions on Machine Learning Research (featured paper), 2024.
• Y. Kuroki, A. Rumi, T. Tsuchiya, F. Vitale, and N. Cesa-Bianchi.
  Best-of-both-worlds algorithms for linear contextual bandits.
  Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

• S. Pasteris, A. Rumi, F. Vitale, and N. Cesa-Bianchi.
  Sum-max submodular bandits.
  Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

• K. Eldowa, E. Esposito, T. Cesari, and N. Cesa-Bianchi
  On the minimax regret for online learning with feedback graphs
  Advances in Neural Information Processing Systems 36 (NeurIPS), 2023.

• D. van der Hoeven, L. Zierahn, T. Lancewicki, A. Rosenberg, and N. Cesa-Bianchi
  A unified analysis of nonstochastic delayed feedback for combinatorial semi-bandits, linear bandits, and MDPs
  Proceedings of the 36th Annual Conference on Learning Theory (COLT). PMLR 195:1285-1321, 2023.

• E. Esposito, S. Masoudian, H. Qiu, D. van der Hoeven, N. Cesa-Bianchi, and Y. Seldin
  Delayed bandits: when do intermediate observations help?
  Proceedings of the 40th International Conference on Machine Learning (ICML). PMLR 202:9374-9395, 2023.

• D. van der Hoeven, C. Pike-Burke, H. Qiu, and N. Cesa-Bianchi
  Trading-off payments and accuracy in online classification with paid stochastic experts
  Proceedings of the 40th International Conference on Machine Learning (ICML). PMLR 202:34809-34830, 2023.

• K. Eldowa, N. Cesa-Bianchi, A. Metelli, and M. Restelli
  Information-theoretic regret bounds for bandits with fixed expert advice
  IEEE Information Theory Workshop (ITW), Pages 30-35, 2023.

• L. Zierahn, D. van der Hoeven, N. Cesa-Bianchi, and G. Neu
  Nonstochastic contextual combinatorial bandits
  Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 206:8771-8813, 2023.

• C. Rouyer, D. van der Hoeven, N. Cesa-Bianchi, and Y. Seldin
  A near-optimal best-of-both-worlds algorithm for online learning with feedback graphs
  Advances in Neural Information Processing Systems 35 (NeurIPS), 2022.

• E. Esposito, F. Fusco, D. van der Hoeven, N. Cesa-Bianchi
  Learning on the edge: Online learning with stochastic feedback graphs
  Advances in Neural Information Processing Systems 35 (NeurIPS), 2022.

• N. Cesa-Bianchi, T. Cesari, R. Colomboni, C. Gentile, and Y. Mansour
  Nonstochastic bandits with composite anonymous feedback
  Journal of Machine Learning Research. JMLR, 23(227):1-24, 2022.

• D. van der Hoeven and N. Cesa-Bianchi
  Nonstochastic bandits and experts with arm-dependent delays
  Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 151:2022-2044, 2022.

• P. Laforgue, G. Clerici, N. Cesa-Bianchi, and Ran Gilad-Bachrach
  A last switch dependent analysis of satiation and seasonality in bandits
  Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 151:971-990, 2022.

• N. Cesa-Bianchi, T. Cesari, Y. Mansour, and V. Perchet
  ROI maximization in stochastic online decision-making
  Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

• D. van der Hoeven, F. Fusco, and N. Cesa-Bianchi
  Beyond bandit feedback in online multiclass classification
  Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

• C. Rouyer, Y. Seldin, and N. Cesa-Bianchi
  An algorithm for stochastic and adversarial bandits with switching costs
  Proceedings of the 38th International Conference on Machine Learning (ICML). PMLR 139:9127-9135, 2021.

• L. Cella and N. Cesa-Bianchi
  Stochastic bandits with delay-dependent payoffs
  Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 108:1168-1177, 2020.

• T. Thune, N. Cesa-Bianchi, and Y. Seldin
  Nonstochastic multiarmed bandits with unrestricted delays
  Advances in Neural Information Processing Systems 32 (NeurIPS), 2019.

• I. Kuzborskij, L. Cella, and N. Cesa-Bianchi
  Efficient linear bandits through matrix sketching
  Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 89:177-185, 2019.

• N. Cesa-Bianchi and O. Shamir
  Bandit regret scaling with the effective loss range (revised version)
  Proceedings of the 29th International Conference on Algorithmic Learning Theory (ALT). PMLR 83:128-151, 2018.

• N. Cesa-Bianchi, C. Gentile, G. Lugosi, G. Neu
  Boltzmann exploration done right
  Advances in Neural Information Processing Systems 30 (NIPS). Pages 6284-6293, 2017.

• N. Alon, N. Cesa-Bianchi, C. Gentile, S. Mannor, Y. Mansour, and O. Shamir
  Nonstochastic multi-armed bandits with graph-structured feedback
  SIAM Journal on Computing. 46(6):1785-1826, 2017.

• N. Cesa-Bianchi, P. Gaillard, C. Gentile, and S. Gerchinovitz
  Algorithmic chaining and the role of partial feedback in online nonparametric learning
  Proceedings of the 30th Conference on Learning Theory (COLT). PMLR 65:465-481, 2017.

• N. Cesa-Bianchi
  Multi-armed bandit problem
  Encyclopedia of Algorithms. Pages 1356-1359, 2016.

• N. Alon, N. Cesa-Bianchi, O. Dekel, and T. Koren
  Online learning with feedback graphs: beyond bandits
  Proceedings of the 28th Conference on Learning Theory (COLT). JMLR Workshop and Conference Proceedings, 40:23-35, 2015.

• N. Cesa-Bianchi, C. Gentile, and G. Zappella
  A gang of bandits
  Advances in Neural Information Processing Systems 26 (NIPS). Pages 737-745, 2013.

• S. Bubeck, N. Cesa-Bianchi, and G. Lugosi
  Bandits with heavy tail
  IEEE Transactions on Information Theory, 59(11):7711-7717, 2013.

• S. Bubeck and N. Cesa-Bianchi
  Regret analysis of stochastic and nonstochastic multi-armed bandit problems
  Foundations and Trends in Machine Learning, 5(1)1-122, 2012.

• S. Bubeck, N. Cesa-Bianchi, and S. Kakade
  Towards minimax policies for online linear optimization with bandit feedback
  Proceedings of the 25th Annual Conference on Learning Theory (COLT). JMLR Workshop and Conference Proceedings, 23:41.1-41.14, 2012.

• N. Cesa-Bianchi and G. Lugosi
  Combinatorial bandits
  Journal of Computer and Systems Sciences, 78:1404-1422, 2012.

• P. Auer, N. Cesa-Bianchi, and P. Fischer
  Finite-time analysis of the multiarmed bandit problem
  Machine Learning, 47(2/3):235-256, 2002.

• P. Auer, N. Cesa-Bianchi, Y. Freund, and R.E. Schapire
  The nonstochastic multiarmed bandit problem
  SIAM Journal on Computing, 32(1):48-77, 2002.

Learning on Graphs

• G. Le Falher, N. Cesa-Bianchi, C. Gentile, F. Vitale
  On the troll-trust model for edge sign prediction in social networks
  Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). JMLR Workshop and Conference Proceedings, 54:402-411, 2017.

• N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella
  A linear time active learning algorithm for link classification
  Advances in Neural Information Processing Systems 25 (NIPS). Pages 1619-1627, 2012.

• N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella
  A correlation clustering approach to link classification in signed networks
  Proceedings of the 25th Annual Conference on Learning Theory (COLT). JMLR Workshop and Conference Proceedings, 23:34.1-34.20, 2012.

• F. Vitale, N. Cesa-Bianchi, C. Gentile, and G. Zappella
  See the tree through the lines: the Shazoo algorithm
  Advances in Neural Information Processing Systems 24 (NIPS). Pages 1584-1592, 2011.
  Full paper.

• N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella
  Active learning on trees and graphs
  Proceedings of the 23rd Conference on Learning Theory (COLT). Omnipress, 2010.

• N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella
  Random spanning trees and the prediction of weighted graphs
  Journal of Machine Learning Research, 14:1251-1284, 2013.

• N. Cesa-Bianchi, C. Gentile, and F. Vitale
  Predicting the labels of an unknown graph via adaptive exploration
  Theoretical Computer Science, 412(19):1791-1804, 2011.

• N. Cesa-Bianchi, C. Gentile, and F. Vitale
  Fast and optimal prediction on a labeled tree
  Proceedings of the 22nd Conference on Learning Theory (COLT). Omnipress, 2009.

• D. van der Hoeven, N. Zhivotovskiy, and N. Cesa-Bianchi
  A regret-variance trade-off in online learning
  Advances in Neural Information Processing Systems 35 (NeurIPS), 2022.

• N. Cesa-Bianchi and F. Orabona
  Online learning algorithms
  Annual Review of Statistics and Its Application, 8:165-190, 2021.

• I. Kuzborskij and N. Cesa-Bianchi
  Locally-adaptive nonparametric online learning
  Advances in Neural Information Processing Systems 33 (NeurIPS), 2020.

• I. Kuzborskij and N. Cesa-Bianchi
  Nonparametric online regression while learning the metric
  Advances in Neural Information Processing Systems 30 (NIPS). Pages 667-676, 2017.

• R. De Rosa and N. Cesa-Bianchi
  Confidence decision trees via online and active learning for streaming data
  Journal of Artificial Intelligence Research, 60:1031-1055, 2017.

• R. De Rosa, I. Gori, F. Cuzzolin, and N. Cesa-Bianchi
  Active incremental recognition of human activities in a streaming context
  Pattern Recognition Letters. 99:48-56, 2017.

• H. Luo, A. Agarwal, N. Cesa-Bianchi, and J. Langford
  Efficient second order online learning by sketching
  Advances in Neural Information Processing Systems 29 (NIPS). Pages 902-910, 2016.

• R. De Rosa, F. Orabona, and N. Cesa-Bianchi
  The ABACOC algorithm: A novel approach for nonparametric classification of data streams
  Proceedings of the IEEE International Conference on Data Mining (ICDM). Pages 733-738, 2015.
  Long version.

• F. Orabona, K. Crammer, and N. Cesa-Bianchi
  A generalized online mirror descent with applications to classification and regression
  Machine Learning, 99(3):411-435, 2015.

• N. Cesa-Bianchi and O. Shamir
  Efficient transductive online learning via randomized rounding
  In: Empirical Inference, Festschrift in Honor of Vladimir N. Vapnik. Springer, 2013.
  (A preliminary version Efficient online learning via randomized rounding appeared in: Advances in Neural Information Processing Systems 24 (NIPS 2011). Pages 343-351.)

• F. Orabona, N. Cesa-Bianchi, and C. Gentile
  Beyond logarithmic bounds in online learning
  Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS). JMLR Workshop and Conference Proceedings, 22:823-831, 2012.

• F. Orabona and N. Cesa-Bianchi
  Better algorithms for selective sampling
  Proceedings of the 28th International Conference on Machine Learning (ICML). Omnipress, 2011.

• N. Cesa-Bianchi, S. Shalev-Shwartz, and O. Shamir
  Quantity makes quality: learning with partial views
  Proceedings of the 25th AAAI Conference on Artificial Intelligence (Nectar Program). AAAI Press, 2011.

• N. Cesa-Bianchi, S. Shalev-Shwartz, and O. Shamir
  Online learning of noisy data
  IEEE Transaction on Information Theory, 57(12):7907-7931, 2011.

• L. Jie, F. Orabona, M. Fornoni, B. Caputo, and N. Cesa-Bianchi
  OM-2: An online multi-class multi-kernel learning algorithm
  Proceedings of the 4th IEEE Online Learning for Computer Vision Workshop. IEEE Press, 2010.

• N. Cesa-Bianchi, S. Shalev-Shwartz, and O. Shamir
  Efficient learning with partially observed attributes
  Journal of Machine Learning Research, 12:2857-2878, 2011.

• G. Cavallanti, N. Cesa-Bianchi, and C. Gentile
  Linear algorithms for online multitask classification
  Journal of Machine Learning Research, 11:2901-2934, 2010.

• N. Cesa-Bianchi, C. Gentile, and F. Orabona
  Robust bounds for classification via selective sampling
  Proceedings of the 26th International Conference on Machine Learning (ICML). Omnipress, 2009.

• G. Cavallanti, N. Cesa-Bianchi, and C. Gentile
  Tracking the best hyperplane with a simple budget Perceptron
  Machine Learning, 69(2/3):143-167. Springer, 2007.

• N. Cesa-Bianchi
  Applications of regularized least squares to pattern classification
  Theoretical Computer Science, 382(3):221-231. Elsevier, 2007.

• N. Cesa-Bianchi, C. Gentile, and L. Zaniboni
  Worst-case analysis of selective sampling for linear classification
  Journal of Machine Learning Research, 7:1205-1230, 2006.

• N. Cesa-Bianchi, C. Gentile, and L. Zaniboni
  Hierarchical classification: combining Bayes with SVM
  Proceedings of the 23rd International Conference on Machine Learning (ICML), 2006.

• N. Cesa-Bianchi, C. Gentile, and L. Zaniboni
  Incremental algorithms for hierarchical classification
  Journal of Machine Learning Research, 7:31--54, 2006.

• N. Cesa-Bianchi, A. Conconi, and C. Gentile
  A second-order Perceptron algorithm
  SIAM Journal on Computing, 34(3):640-668. SIAM, 2005.

• P. Auer, N. Cesa-Bianchi, and C. Gentile
  Adaptive and self-confident on-line learning algorithms
  Journal of Computer and System Sciences, 64(1):48-75, 2002.

• N. Cesa-Bianchi
  Analysis of two gradient-based algorithms for on-line regression
  Journal of Computer and System Sciences, 59(3):392-411, 1999.

• P. Auer and N. Cesa-Bianchi
  On-line learning with malicious noise and the closure algorithm
  Annals of AI and Mathematics, 23(1/2):83-99, 1998.

• N. Cesa-Bianchi, P.M. Long, and M.K. Warmuth
  Worst-case quadratic loss bounds for prediction using linear functions and gradient descent
  IEEE Transactions on Neural Networks, 7(3):604-619, 1996.

• A. Bertoni, N. Cesa-Bianchi e G. Fiorino
  Efficient learning with equivalence queries of conjunctions of modulo functions
  Information Processing Letters, 56:15--17, 1995.

• M. Bressan, N. Cesa-Bianchi, E. Esposito, Y. Mansour, S. Moran, and M. Thiessen.
  A theory of interpretable approximations
  Proceedings of the 37th Annual Conference on Learning Theory (COLT). PMLR 247:648-668, 2024.

• I. Kuzborskij, N. Cesa-Bianchi, and C. Szepesvari
  Distribution-Dependent Analysis of Gibbs-ERM Principle
  Proceedings of the 32nd Annual Conference on Learning Theory (COLT). PMLR 99:2028-2054, 2019.

• N. Cesa-Bianchi, Y. Mansour, and O. Shamir
  On the complexity of learning with kernels
  Proceedings of the 28th Annual Conference on Learning Theory (COLT). JMLR Workshop and Conference Proceedings, 40:297-325, 2015.

• G. Cavallanti, N. Cesa-Bianchi, and C. Gentile
  Learning noisy linear classifiers via adaptive and selective sampling
  Machine Learning, 83:71-102, 2011.

• N. Cesa-Bianchi and C. Gentile
  Improved risk tail bounds for on-line algorithms
  IEEE Transactions on Information Theory, 54(1):386-390, 2008.

• N. Cesa-Bianchi, A. Conconi, and C. Gentile
  On the generalization ability of on-line learning algorithms
  IEEE Transactions on Information Theory, 50(9):2050-2057, 2004.

• N. Cesa-Bianchi, E. Dichterman, P. Fischer, E. Shamir, and H.U. Simon
  Sample-efficient strategies for learning in the presence of noise
  Journal of the ACM, 46(5):684-719, 1999.

• N. Alon, S. Ben-David, N. Cesa-Bianchi, and D. Haussler
  Scale-sensitive dimensions, uniform convergence, and learnability
  Journal of the ACM, 44(4):615-631, 1997.

• S. Ben-David, N. Cesa-Bianchi, D. Haussler, and P.M. Long
  Characterizations of learnability for classes of {0,..,n}-valued functions
  Journal of Computer and System Sciences, 50(1):74-86, 1995.

• N. Cesa-Bianchi, O. Dekel, O Shamir
  Online learning with switching costs and other adaptive adversaries
  Advances in Neural Information Processing Systems 26. Pages 1160-1168, 2013.

• E. Gofer, N. Cesa-Bianchi, C. Gentile, and Y. Mansour
  Regret minimization for branching experts
  Proceedings of the 26th Conference on Learning Theory. JMLR Workshop and Conference Proceedings, 30:618-638, 2013.

• N. Cesa-Bianchi, P. Gaillard, G. Lugosi, and G. Stoltz
  Mirror Descent meets Fixed Share (and feels no regret)
  Advances in Neural Information Processing Systems 25. Pages 989-997, 2012.

• N. Cesa-Bianchi, Y. Mansour, and G. Stoltz
  Improved second-order bounds for prediction with expert advice
  Machine Learning, 66(2/3):321-352, 2007.

• N. Cesa-Bianchi, G. Lugosi, and G. Stoltz
  Regret minimization under partial monitoring
  Mathematics of Operations Research, 31(3):562-580, 2006.

• N. Cesa-Bianchi, G. Lugosi, and G. Stoltz
  Minimizing regret with label efficient prediction
  IEEE Transactions on Information Theory, 51(6):2152-2162, 2005.

• N. Cesa-Bianchi and G. Lugosi
  Potential-based algorithms in on-line prediction and game theory
  Machine Learning, 51(3):239-261, 2003.

• N. Cesa-Bianchi
  Universal prediction
  In: Principles of Nonparametric Learning
  Springer (CISM courses and lectures no. 434), 2002.

• N. Cesa-Bianchi and G. Lugosi
  Worst-case bounds for the logarithmic loss of predictors
  Machine Learning, 43(3):247-264, 2001.
  

• N. Cesa-Bianchi and G. Lugosi
  On prediction of individual sequences
  Annals of Statistics, 27(6):1865-1895, 1999.

• N. Cesa-Bianchi, D.P. Helmbold, and S. Panizza
  On Bayes methods for on-line boolean prediction
  Algorithmica, 22(1/2):112-137, 1998.

• N. Cesa-Bianchi, Y. Freund, D.P. Helmbold, D. Haussler, R. Schapire, and M.K. Warmuth
  How to use expert advice
  Journal of the ACM, 44(3):427-485, 1997.

• N. Cesa-Bianchi, Y. Freund, D.P. Helmbold, and M.K. Warmuth
  On-line prediction and conversion strategies
  Machine Learning, 25:71-110, 1996.

• M. Frasca and N. Cesa-Bianchi
  Multitask protein function prediction through task dissimilarity
  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16:1550-1560, 2019.

• N. Cesa-Bianchi, M. Re, and G. Valentini
  Synergy of multilabel hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference
  Machine Learning, 88:209-241, 2012.

• N. Cesa-Bianchi and G. Valentini
  Hierarchical cost-sensitive algorithms for genome-wide gene function prediction
  Proceedings of the 3rd International Workshop on Machine Learning in Systems Biology, JMLR Workshop and Conference Proceedings, 8:14-29, 2010.

• G. Valentini and N. Cesa-Bianchi
  HCGene: a software tool to support the hierarchical classification of genes
  Bioinformatics, 24(5):729-731, 2008.

• M. Bressan, N. Cesa-Bianchi, E. Esposito, Y. Mansour, S. Moran, and M. Thiessen.
  A theory of interpretable approximations.
  Proceedings of the 37th Conference on Learning Theory (COLT), PMLR 247:648-668, 2024.

• Y. Seldin, F. Laviolette, N. Cesa-Bianchi, J. Shawe-Taylor, and P. Auer
  PAC-Bayesian Inequalities for Martingales
  IEEE Transactions on Information Theory, 58(12):7086-7093, 2012.

• P. Auer and N. Cesa-Bianchi
  A distributed voting scheme to maximize preferences
  RAIRO - Theoretical Informatics and Applications, 40:389-403, 2006.

• N. Cesa-Bianchi and G. Lugosi
  Minimax values and metric entropy bounds for portfolio selection problems
  Communicated at the First World Congress of the Game Theory Society (Games 2000).

• N. Cesa-Bianchi and D. Haussler,
  A graph-theoretic generalization of the Sauer-Shelah lemma
  Discrete Applied Mathematics, 86(1):27-35, 1998.

• N. Cesa-Bianchi, A. Krogh and M.K. Warmuth
  Bounds on approximate steepest descent for likelihood maximization in exponential families
  IEEE Transactions on Information Theory, 40(4):1215-1218, 1994.

• B. Apolloni, N. Cesa-Bianchi, and D. De Falco
  A numerical implementation of quantum annealing
  Stochastic Processes, Physics and Geometry: Proceedings of the Ascona-Locarno Conference, pages 97-111, 1988.

• G. Cattaneo and N. Cesa-Bianchi
  Microcanonical annealing on neural networks
  Proceedings of the First Annual Meeting of the International Neural Network Society, page 81, 1988.