Learning with an unreliable teacher. - Gábor Lugosi (Pattern Recognit., 1992)

Universality and Rates of Convergence in Lossy Source Coding. - Tamás Linder; Gábor Lugosi; Kenneth Zeger (Data Compression Conference, 1993)

Fast Nearest-Neighbor Search in Dissimilarity Spaces. - András Faragó; Tamás Linder; Gábor Lugosi (IEEE Trans. Pattern Anal. Mach. Intell., 1993)

Strong universal consistency of neural network classifiers. - András Faragó; Gábor Lugosi (IEEE Trans. Inf. Theory, 1993)

Nonparametric classification using radial basis function nets and empirical risk minimization. - Adam Krzyzak; Tamás Linder; Gábor Lugosi (ICPR (2), 1994)

Rates of convergence in the source coding theorem, in empirical quantizer design, and in universal lossy source coding. - Tamás Linder; Gábor Lugosi; Kenneth Zeger (IEEE Trans. Inf. Theory, 1994)

On the posterior-probability estimate of the error rate of nonparametric classification rules. - Gábor Lugosi; Miroslaw Pawlak (IEEE Trans. Inf. Theory, 1994)

Lower bounds in pattern recognition and learning. - Luc Devroye; Gábor Lugosi (Pattern Recognit., 1995)

Fixed-rate universal lossy source coding and rates of convergence for memoryless sources. - Tamás Linder; Gábor Lugosi; Kenneth Zeger (IEEE Trans. Inf. Theory, 1995)

Nonparametric estimation via empirical risk minimization. - Gábor Lugosi; Kenneth Zeger (IEEE Trans. Inf. Theory, 1995)

A Probabilistic Theory of Pattern Recognition - Luc Devroye; László Györfi; Gábor Lugosi (Springer, 1996)

A Data-Dependent Skeleton Estimate for Learning. - Gábor Lugosi; Márta Pintér (COLT, 1996)

Designing Vector Quantizers in the Presence of Source Noise or Channel Noise. - Tamás Linder; Gábor Lugosi; Kenneth Zeger (Data Compression Conference, 1996)

Concept learning using complexity regularization. - Gábor Lugosi; Kenneth Zeger (IEEE Trans. Inf. Theory, 1996)

Nonparametric estimation and classification using radial basis function nets and empirical risk minimization. - Adam Krzyzak; Tamás Linder; Gábor Lugosi (IEEE Trans. Neural Networks, 1996)

A Minimax Lower Bound for Empirical Quantizer Design. - Peter L. Bartlett; Tamás Linder; Gábor Lugosi (EuroCOLT, 1997)

Empirical quantizer design in the presence of source noise or channel noise. - Tamás Linder; Gábor Lugosi; Kenneth Zeger (IEEE Trans. Inf. Theory, 1997)

On Sequential Prediction of Individual Sequences Relative to a Set of Experts. - Nicolò Cesa-Bianchi; Gábor Lugosi (COLT, 1998)

Scale-sensitive Dimensions and Skeleton Estimates for Classification. - Márta Horváth; Gábor Lugosi (Discret. Appl. Math., 1998)

Strong Minimax Lower Bounds for Learning. - András Antos; Gábor Lugosi (Mach. Learn., 1998)

The Minimax Distortion Redundancy in Empirical Quantizer Design. - Peter L. Bartlett; Tamás Linder; Gábor Lugosi (IEEE Trans. Inf. Theory, 1998)

Learning Pattern Classification - A Survey. - Sanjeev R. Kulkarni; Gábor Lugosi; Santosh S. Venkatesh (IEEE Trans. Inf. Theory, 1998)

Minimax Regret Under log Loss for General Classes of Experts. - Nicolò Cesa-Bianchi; Gábor Lugosi (COLT, 1999)

A simple randomized algorithm for sequential prediction of ergodic time series. - László Györfi; Gábor Lugosi; Gusztáv Morvai (IEEE Trans. Inf. Theory, 1999)

A sharp concentration inequality with applications. - Stéphane Boucheron; Gábor Lugosi; Pascal Massart (Random Struct. Algorithms, 2000)

Finite-time lower bounds for the two-armed bandit problem. - Sanjeev R. Kulkarni; Gábor Lugosi (IEEE Trans. Autom. Control., 2000)

Combinatorial methods in density estimation. - Luc Devroye; Gábor Lugosi (Springer, 2001)

Potential-Based Algorithms in Online Prediction and Game Theory. - Nicolò Cesa-Bianchi; Gábor Lugosi (COLT/EuroCOLT, 2001)

Data-Dependent Margin-Based Generalization Bounds for Classification. - Balázs Kégl; Tamás Linder; Gábor Lugosi (COLT/EuroCOLT, 2001)

Worst-Case Bounds for the Logarithmic Loss of Predictors. - Nicolò Cesa-Bianchi; Gábor Lugosi (Mach. Learn., 2001)

A zero-delay sequential scheme for lossy coding of individual sequences. - Tamás Linder; Gábor Lugosi (IEEE Trans. Inf. Theory, 2001)

A Consistent Strategy for Boosting Algorithms. - Gábor Lugosi; Nicolas Vayatis (COLT, 2002)

Data-dependent margin-based generalization bounds for classification. - András Antos; Balázs Kégl; Tamás Linder; Gábor Lugosi (J. Mach. Learn. Res., 2002)

Model Selection and Error Estimation. - Peter L. Bartlett; Stéphane Boucheron; Gábor Lugosi (Mach. Learn., 2002)

A note on robust hypothesis testing. - Luc Devroye; László Györfi; Gábor Lugosi (IEEE Trans. Inf. Theory, 2002)

Concentration Inequalities. - Stéphane Boucheron; Gábor Lugosi; Olivier Bousquet (Advanced Lectures on Machine Learning, 2003)

Introduction to Statistical Learning Theory. - Olivier Bousquet; Stéphane Boucheron; Gábor Lugosi (Advanced Lectures on Machine Learning, 2003)

On the Rate of Convergence of Regularized Boosting Classifiers. - Gilles Blanchard; Gábor Lugosi; Nicolas Vayatis (J. Mach. Learn. Res., 2003)

Potential-Based Algorithms in On-Line Prediction and Game Theory. - Nicolò Cesa-Bianchi; Gábor Lugosi (Mach. Learn., 2003)

A "Follow the Perturbed Leader"-type Algorithm for Zero-Delay Quantization of Individual Sequence. - András György 001; Tamás Linder; Gábor Lugosi (Data Compression Conference, 2004)

Efficient algorithms and minimax bounds for zero-delay lossy source coding. - András György 001; Tamás Linder; Gábor Lugosi (ISIT, 2004)

Efficient adaptive algorithms and minimax bounds for zero-delay lossy source coding. - András György 001; Tamás Linder; Gábor Lugosi (IEEE Trans. Signal Process., 2004)

Limited-Delay Coding of Individual Sequences with Piecewise Different Behavior. - András György 001; Tamás Linder; Gábor Lugosi (CDC/ECC, 2005)

Ranking and Scoring Using Empirical Risk Minimization. - Stéphan Clémençon; Gábor Lugosi; Nicolas Vayatis (COLT, 2005)

Tracking the Best of Many Experts. - András György 001; Tamás Linder; Gábor Lugosi (COLT, 2005)

From Ranking to Classification: A Statistical View. - Stéphan Clémençon; Gábor Lugosi; Nicolas Vayatis (GfKl, 2005)

Internal Regret in On-Line Portfolio Selection. - Gilles Stoltz; Gábor Lugosi (Mach. Learn., 2005)

Minimizing regret with label efficient prediction. - Nicolò Cesa-Bianchi; Gábor Lugosi; Gilles Stoltz (IEEE Trans. Inf. Theory, 2005)

Prediction, learning, and games. - Nicolò Cesa-Bianchi; Gábor Lugosi (Cambridge University Press, 2006)

The Shortest Path Problem in the Bandit Setting. - András György 001; Tamás Linder; Gábor Lugosi (ITW, 2006)

Regret Minimization Under Partial Monitoring. - Nicolò Cesa-Bianchi; Gábor Lugosi; Gilles Stoltz (Math. Oper. Res., 2006)

Sequential prediction under incomplete feedback. - Gábor Lugosi (CCIA, 2007)

Global Nash convergence of Foster and Young's regret testing. - Fabrizio Germano; Gábor Lugosi (Games Econ. Behav., 2007)

Learning correlated equilibria in games with compact sets of strategies. - Gilles Stoltz; Gábor Lugosi (Games Econ. Behav., 2007)

The On-Line Shortest Path Problem Under Partial Monitoring. - András György 001; Tamás Linder; Gábor Lugosi; György Ottucsák (J. Mach. Learn. Res., 2007)

Introduction to the special issue on COLT 2006. - Avrim Blum; Gábor Lugosi; Hans Ulrich Simon (Mach. Learn., 2007)

Concentration Inequalities. - Gábor Lugosi (COLT, 2008)

Consistency of Random Forests and Other Averaging Classifiers. - Gérard Biau; Luc Devroye; Gábor Lugosi (J. Mach. Learn. Res., 2008)

Strategies for Prediction Under Imperfect Monitoring. - Gábor Lugosi; Shie Mannor; Gilles Stoltz (Math. Oper. Res., 2008)

On the Performance of Clustering in Hilbert Spaces. - Gérard Biau; Luc Devroye; Gábor Lugosi (IEEE Trans. Inf. Theory, 2008)

Tracking the Best Quantizer. - András György 001; Tamás Linder; Gábor Lugosi (IEEE Trans. Inf. Theory, 2008)

Online Multi-task Learning with Hard Constraints. - Gábor Lugosi; Omiros Papaspiliopoulos; Gilles Stoltz (COLT, 2009)

Multiple choice tries and distributed hash tables. - Luc Devroye; Gábor Lugosi; GaHyun Park; Wojciech Szpankowski (Random Struct. Algorithms, 2009)

The Longest Minimum-Weight Path in a Complete Graph. - Louigi Addario-Berry; Nicolas Broutin; Gábor Lugosi (Comb. Probab. Comput., 2010)

On-Line Sequential Bin Packing. - András György 001; Gábor Lugosi; György Ottucsák (J. Mach. Learn. Res., 2010)

Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291). - Matthias Hein 001; Gábor Lugosi; Lorenzo Rosasco; Steve Smale (Dagstuhl Reports, 2011)

Minimax Policies for Combinatorial Prediction Games. - Jean-Yves Audibert; Sébastien Bubeck; Gábor Lugosi (COLT, 2011)

Preface. - Gábor Lugosi; Sandra Zilles (Theor. Comput. Sci., 2011)

Mirror Descent Meets Fixed Share (and feels no regret). - Nicolò Cesa-Bianchi; Pierre Gaillard; Gábor Lugosi; Gilles Stoltz (NIPS, 2012)

A new look at shifting regret - Nicolò Cesa-Bianchi; Pierre Gaillard; Gábor Lugosi; Gilles Stoltz (CoRR, 2012)

Combinatorial bandits. - Nicolò Cesa-Bianchi; Gábor Lugosi (J. Comput. Syst. Sci., 2012)

Efficient Tracking of Large Classes of Experts. - András György 001; Tamás Linder; Gábor Lugosi (IEEE Trans. Inf. Theory, 2012)

Concentration Inequalities - A Nonasymptotic Theory of Independence. - Stéphane Boucheron; Gábor Lugosi; Pascal Massart (Concentration Inequalities, 2013)

Prediction by random-walk perturbation. - Luc Devroye; Gábor Lugosi; Gergely Neu (COLT, 2013)

Bandits With Heavy Tail. - Sébastien Bubeck; Nicolò Cesa-Bianchi; Gábor Lugosi (IEEE Trans. Inf. Theory, 2013)

Density-preserving quantization with application to graph downsampling. - Morteza Alamgir; Gábor Lugosi; Ulrike von Luxburg (COLT, 2014)

Connectivity of sparse Bluetooth networks. - Nicolas Broutin; Luc Devroye; Gábor Lugosi (CoRR, 2014)

Almost optimal sparsification of random geometric graphs. - Nicolas Broutin; Luc Devroye; Gábor Lugosi (CoRR, 2014)

Regret in Online Combinatorial Optimization. - Jean-Yves Audibert; Sébastien Bubeck; Gábor Lugosi (Math. Oper. Res., 2014)

Connectivity threshold of Bluetooth graphs. - Nicolas Broutin; Luc Devroye; Nicolas Fraiman; Gábor Lugosi (Random Struct. Algorithms, 2014)

Detection of Correlations With Adaptive Sensing. - Rui M. Castro; Gábor Lugosi; Pierre-André Savalle (IEEE Trans. Inf. Theory, 2014)

Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361). - Matthias Hein 001; Gábor Lugosi; Lorenzo Rosasco (Dagstuhl Reports, 2015)

Exceptional rotations of random graphs: a VC theory. - Louigi Addario-Berry; Shankar Bhamidi; Sébastien Bubeck; Luc Devroye; Gábor Lugosi; Roberto Imbuzeiro Oliveira (J. Mach. Learn. Res., 2015)

Random-Walk Perturbations for Online Combinatorial Optimization. - Luc Devroye; Gábor Lugosi; Gergely Neu (IEEE Trans. Inf. Theory, 2015)

An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits. - Yevgeny Seldin; Gábor Lugosi (COLT, 2017)

Algorithmic Stability and Hypothesis Complexity. - Tongliang Liu; Gábor Lugosi; Gergely Neu; Dacheng Tao (ICML, 2017)

Boltzmann Exploration Done Right. - Nicolò Cesa-Bianchi; Claudio Gentile; Gergely Neu; Gábor Lugosi (NIPS, 2017)

On the Hardness of Inventory Management with Censored Demand Data. - Gábor Lugosi; Mihalis G. Markakis; Gergely Neu (CoRR, 2017)

Local optima of the Sherrington-Kirkpatrick Hamiltonian. - Louigi Addario-Berry; Luc Devroye; Gábor Lugosi; Roberto Imbuzeiro Oliveira (CoRR, 2017)

On the measure of Voronoi cells. - Luc Devroye; László Györfi; Gábor Lugosi; Harro Walk (J. Appl. Probab., 2017)

Finding Adam in random growing trees. - Sébastien Bubeck; Luc Devroye; Gábor Lugosi (Random Struct. Algorithms, 2017)

Online Influence Maximization with Local Observations. - Gábor Lugosi; Gergely Neu; Julia Olkhovskaya (ALT, 2019)

Structure learning in graphical models by covariance queries. - Gábor Lugosi; Jakub Truszkowski; Vasiliki Velona; Piotr Zwiernik (CoRR, 2019)

Benign Overfitting in Linear Regression. - Peter L. Bartlett; Philip M. Long; Gábor Lugosi; Alexander Tsigler (CoRR, 2019)

Mean Estimation and Regression Under Heavy-Tailed Distributions: A Survey. - Gábor Lugosi; Shahar Mendelson (Found. Comput. Math., 2019)

On Mean Estimation for Heteroscedastic Random Variables. - Luc Devroye; Silvio Lattanzi; Gábor Lugosi; Nikita Zhivotovskiy (CoRR, 2020)

Learning to maximize global influence from local observations. - Gábor Lugosi; Gergely Neu; Julia Olkhovskaya (CoRR, 2021)

Learning partial correlation graphs and graphical models by covariance queries. - Gábor Lugosi; Jakub Truszkowski; Vasiliki Velona; Piotr Zwiernik (J. Mach. Learn. Res., 2021)

Generalization Bounds via Convex Analysis. - Gábor Lugosi; Gergely Neu (COLT, 2022)

Subtractive random forests. - Nicolas Broutin; Luc Devroye; Gábor Lugosi; Roberto Imbuzeiro Oliveira (CoRR, 2022)

Multiplayer Bandits Without Observing Collision Information. - Gábor Lugosi; Abbas Mehrabian (Math. Oper. Res., 2022)

Online-to-PAC Conversions: Generalization Bounds via Regret Analysis. - Gábor Lugosi; Gergely Neu (CoRR, 2023)

Broadcasting in random recursive dags. - Simon Briend; Luc Devroye; Gábor Lugosi (CoRR, 2023)

Increasing paths in random temporal graphs. - Nicolas Broutin; Nina Kamcev; Gábor Lugosi (CoRR, 2023)

On the quality of randomized approximations of Tukey's depth. - Simon Briend; Gábor Lugosi; Roberto Imbuzeiro Oliveira (CoRR, 2023)

A note on estimating the dimension from a random geometric graph. - Caelan Atamanchuk; Luc Devroye; Gábor Lugosi (CoRR, 2023)

Bandit problems with fidelity rewards. - Gábor Lugosi; Ciara Pike-Burke; Pierre-André Savalle (J. Mach. Learn. Res., 2023)

Inferring the Mixing Properties of a Stationary Ergodic Process From a Single Sample-Path. - Azadeh Khaleghi; Gábor Lugosi (IEEE Trans. Inf. Theory, 2023)

Archaeology of random recursive dags and Cooper-Frieze random networks. - Simon Briend; Francisco Calvillo; Gábor Lugosi (Comb. Probab. Comput., 2023)

Estimating the history of a random recursive tree. - Simon Briend; Christophe Giraud 002; Gábor Lugosi; Déborah Sulem (CoRR, 2024)

Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks. - Gábor Lugosi; Eulalia Nualart (CoRR, 2024)

Leaf Stripping on Uniform Attachment Trees. - Louigi Addario-Berry; Anna M. Brandenberger; Simon Briend; Nicolas Broutin; Gábor Lugosi (CoRR, 2024)

Individualized post-crisis monitoring of psychiatric patients via Hidden Markov models. - Roger Garriga; Vicenç Gómez; Gábor Lugosi (Frontiers Digit. Health, 2024)

On the Hardness of Learning from Censored and Nonstationary Demand. - Gábor Lugosi; Mihalis G. Markakis; Gergely Neu (INFORMS J. Optim., 2024)

Detecting weighted hidden cliques. - Urmisha Chatterjee; Karissa Huang; Ritabrata Karmakar; B. R. Vinay Kumar; Gábor Lugosi; Nandan Malhotra; Anirban Mandal; Maruf Alam Tarafdar (CoRR, 2025)

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces. - Gábor Lugosi; Marcos Matabuena (CoRR, 2025)