Gábor Lugosi - RankMe

151 papers A* 23B 10Journal 103Unranked 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Ratio Covers of Convex Sets and Optimal Mixture Density Estimation. CoRR Spencer Compton, Gábor Lugosi, Jaouad Mourtada, Jian Qian, Nikita Zhivotovskiy
2025	J	`jnl`	Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces. CoRR Gábor Lugosi, Marcos Matabuena
2025	J	`jnl`	Detecting weighted hidden cliques. CoRR Urmisha Chatterjee, Karissa Huang, Ritabrata Karmakar, B. R. Vinay Kumar, Gábor Lugosi, Nandan Malhotra, Anirban Mandal, Maruf Alam Tarafdar
2025	J	`jnl`	On the Quality of Randomized Approximations of Tukey's Depth. SIAM J. Math. Data Sci. Simon Briend, Gábor Lugosi, Roberto Imbuzeiro Oliveira
2025	J	`jnl`	On the size of temporal cliques in subcritical random temporal graphs. Comb. Probab. Comput. Caelan Atamanchuk, Luc Devroye, Gábor Lugosi
2024	J	`jnl`	Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks. CoRR Gábor Lugosi, Eulalia Nualart
2024	J	`jnl`	Estimating the history of a random recursive tree. CoRR Simon Briend, Christophe Giraud, Gábor Lugosi, Déborah Sulem
2024	J	`jnl`	Individualized post-crisis monitoring of psychiatric patients via Hidden Markov models. Frontiers Digit. Health Roger Garriga, Vicenç Gómez, Gábor Lugosi
2024	J	`jnl`	Leaf Stripping on Uniform Attachment Trees. CoRR Louigi Addario-Berry, Anna M. Brandenberger, Simon Briend, Nicolas Broutin, Gábor Lugosi
2024	J	`jnl`	On the Hardness of Learning from Censored and Nonstationary Demand. INFORMS J. Optim. Gábor Lugosi, Mihalis G. Markakis, Gergely Neu
2023	J	`jnl`	A note on estimating the dimension from a random geometric graph. CoRR Caelan Atamanchuk, Luc Devroye, Gábor Lugosi
2023	J	`jnl`	Archaeology of random recursive dags and Cooper-Frieze random networks. Comb. Probab. Comput. Simon Briend, Francisco Calvillo, Gábor Lugosi
2023	J	`jnl`	Bandit problems with fidelity rewards. J. Mach. Learn. Res. Gábor Lugosi, Ciara Pike-Burke, Pierre-André Savalle
2023	J	`jnl`	Broadcasting in random recursive dags. CoRR Simon Briend, Luc Devroye, Gábor Lugosi
2023	J	`jnl`	Increasing paths in random temporal graphs. CoRR Nicolas Broutin, Nina Kamcev, Gábor Lugosi
2023	J	`jnl`	Inferring the Mixing Properties of a Stationary Ergodic Process From a Single Sample-Path. IEEE Trans. Inf. Theory Azadeh Khaleghi, Gábor Lugosi
2023	J	`jnl`	On the quality of randomized approximations of Tukey's depth. CoRR Simon Briend, Gábor Lugosi, Roberto Imbuzeiro Oliveira
2023	J	`jnl`	Online-to-PAC Conversions: Generalization Bounds via Regret Analysis. CoRR Gábor Lugosi, Gergely Neu
2022	J	`jnl`	Archaeology of random recursive dags and Cooper-Frieze random networks. CoRR Simon Briend, Francisco Calvillo, Gábor Lugosi
2022	A*	`conf`	Generalization Bounds via Convex Analysis. COLT Gábor Lugosi, Gergely Neu
2022	J	`jnl`	Generalization Bounds via Convex Analysis. CoRR Gergely Neu, Gábor Lugosi
2022	J	`jnl`	Multiplayer Bandits Without Observing Collision Information. Math. Oper. Res. Gábor Lugosi, Abbas Mehrabian
2022	J	`jnl`	Subtractive random forests. CoRR Nicolas Broutin, Luc Devroye, Gábor Lugosi, Roberto Imbuzeiro Oliveira
2021	J	`jnl`	Bandit problems with fidelity rewards. CoRR Gábor Lugosi, Ciara Pike-Burke, Pierre-André Savalle
2021	J	`jnl`	Learning partial correlation graphs and graphical models by covariance queries. J. Mach. Learn. Res. Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik
2021	J	`jnl`	Learning to maximize global influence from local observations. CoRR Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
2020	J	`jnl`	On Mean Estimation for Heteroscedastic Random Variables. CoRR Luc Devroye, Silvio Lattanzi, Gábor Lugosi, Nikita Zhivotovskiy
2019	J	`jnl`	Benign Overfitting in Linear Regression. CoRR Peter L. Bartlett, Philip M. Long, Gábor Lugosi, Alexander Tsigler
2019	J	`jnl`	Mean Estimation and Regression Under Heavy-Tailed Distributions: A Survey. Found. Comput. Math. Gábor Lugosi, Shahar Mendelson
2019	J	`jnl`	Mean estimation and regression under heavy-tailed distributions-a survey. CoRR Gábor Lugosi, Shahar Mendelson
2019	B	`conf`	Online Influence Maximization with Local Observations. ALT Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
2019	J	`jnl`	Structure learning in graphical models by covariance queries. CoRR Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik
2018	J	`jnl`	Multiplayer bandits without observing collision information. CoRR Gábor Lugosi, Abbas Mehrabian
2018	J	`jnl`	Online Influence Maximization with Local Observations. CoRR Julia Olkhovskaya, Gergely Neu, Gábor Lugosi
2017	A*	`conf`	Algorithmic Stability and Hypothesis Complexity. ICML Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao
2017	J	`jnl`	Algorithmic stability and hypothesis complexity. CoRR Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao
2017	A*	`conf`	An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits. COLT Yevgeny Seldin, Gábor Lugosi
2017	J	`jnl`	An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits. CoRR Yevgeny Seldin, Gábor Lugosi
2017	—	`conf`	Boltzmann Exploration Done Right. NIPS Nicolò Cesa-Bianchi, Claudio Gentile, Gergely Neu, Gábor Lugosi
2017	J	`jnl`	Boltzmann Exploration Done Right. CoRR Nicolò Cesa-Bianchi, Claudio Gentile, Gábor Lugosi, Gergely Neu
2017	J	`jnl`	Finding Adam in random growing trees. Random Struct. Algorithms Sébastien Bubeck, Luc Devroye, Gábor Lugosi
2017	J	`jnl`	Local optima of the Sherrington-Kirkpatrick Hamiltonian. CoRR Louigi Addario-Berry, Luc Devroye, Gábor Lugosi, Roberto Imbuzeiro Oliveira
2017	J	`jnl`	On the Hardness of Inventory Management with Censored Demand Data. CoRR Gábor Lugosi, Mihalis G. Markakis, Gergely Neu
2017	J	`jnl`	On the measure of Voronoi cells. J. Appl. Probab. Luc Devroye, László Györfi, Gábor Lugosi, Harro Walk
2015	J	`jnl`	Exceptional rotations of random graphs: a VC theory. J. Mach. Learn. Res. Louigi Addario-Berry, Shankar Bhamidi, Sébastien Bubeck, Luc Devroye, Gábor Lugosi, Roberto Imbuzeiro Oliveira
2015	J	`jnl`	Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361). Dagstuhl Reports Matthias Hein, Gábor Lugosi, Lorenzo Rosasco
2015	J	`jnl`	Random-Walk Perturbations for Online Combinatorial Optimization. IEEE Trans. Inf. Theory Luc Devroye, Gábor Lugosi, Gergely Neu
2014	J	`jnl`	Almost optimal sparsification of random geometric graphs. CoRR Nicolas Broutin, Luc Devroye, Gábor Lugosi
2014	J	`jnl`	Connectivity of sparse Bluetooth networks. CoRR Nicolas Broutin, Luc Devroye, Gábor Lugosi
2014	J	`jnl`	Connectivity threshold of Bluetooth graphs. Random Struct. Algorithms Nicolas Broutin, Luc Devroye, Nicolas Fraiman, Gábor Lugosi
2014	A*	`conf`	Density-preserving quantization with application to graph downsampling. COLT Morteza Alamgir, Gábor Lugosi, Ulrike von Luxburg
2014	J	`jnl`	Detection of Correlations With Adaptive Sensing. IEEE Trans. Inf. Theory Rui M. Castro, Gábor Lugosi, Pierre-André Savalle
2014	J	`jnl`	Finding Adam in random growing trees. CoRR Sébastien Bubeck, Luc Devroye, Gábor Lugosi
2014	J	`jnl`	Regret in Online Combinatorial Optimization. Math. Oper. Res. Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi
2013	J	`jnl`	Bandits With Heavy Tail. IEEE Trans. Inf. Theory Sébastien Bubeck, Nicolò Cesa-Bianchi, Gábor Lugosi
2013	—	`book`	Concentration Inequalities - A Nonasymptotic Theory of Independence. Concentration Inequalities Stéphane Boucheron, Gábor Lugosi, Pascal Massart
2013	J	`jnl`	Prediction by Random-Walk Perturbation CoRR Luc Devroye, Gábor Lugosi, Gergely Neu
2013	A*	`conf`	Prediction by random-walk perturbation. COLT Luc Devroye, Gábor Lugosi, Gergely Neu
2012	J	`jnl`	A new look at shifting regret CoRR Nicolò Cesa-Bianchi, Pierre Gaillard, Gábor Lugosi, Gilles Stoltz
2012	J	`jnl`	Bandits with heavy tail CoRR Sébastien Bubeck, Nicolò Cesa-Bianchi, Gábor Lugosi
2012	J	`jnl`	Combinatorial bandits. J. Comput. Syst. Sci. Nicolò Cesa-Bianchi, Gábor Lugosi
2012	J	`jnl`	Efficient Tracking of Large Classes of Experts. IEEE Trans. Inf. Theory András György, Tamás Linder, Gábor Lugosi
2012	B	`conf`	Efficient tracking of large classes of experts. ISIT András György, Tamás Linder, Gábor Lugosi
2012	—	`conf`	Mirror Descent Meets Fixed Share (and feels no regret). NIPS Nicolò Cesa-Bianchi, Pierre Gaillard, Gábor Lugosi, Gilles Stoltz
2012	J	`jnl`	Regret in Online Combinatorial Optimization CoRR Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi
2011	J	`jnl`	Connectivity threshold for Bluetooth graphs CoRR Nicolas Broutin, Luc Devroye, Nicolas Fraiman, Gábor Lugosi
2011	J	`jnl`	Efficient Tracking of Large Classes of Experts CoRR András György, Tamás Linder, Gábor Lugosi
2011	J	`jnl`	Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291). Dagstuhl Reports Matthias Hein, Gábor Lugosi, Lorenzo Rosasco, Steve Smale
2011	A*	`conf`	Minimax Policies for Combinatorial Prediction Games. COLT Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi
2011	J	`jnl`	Preface. Theor. Comput. Sci. Gábor Lugosi, Sandra Zilles
2010	J	`jnl`	On-Line Sequential Bin Packing. J. Mach. Learn. Res. András György, Gábor Lugosi, György Ottucsák
2010	J	`jnl`	The Longest Minimum-Weight Path in a Complete Graph. Comb. Probab. Comput. Louigi Addario-Berry, Nicolas Broutin, Gábor Lugosi
2009	B	`ed.`	Algorithmic Learning Theory, 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009. Proceedings ALT Ricard Gavaldà, Gábor Lugosi, Thomas Zeugmann, Sandra Zilles
2009	A*	`conf`	Combinatorial Bandits. COLT Nicolò Cesa-Bianchi, Gábor Lugosi
2009	J	`jnl`	Multiple choice tries and distributed hash tables. Random Struct. Algorithms Luc Devroye, Gábor Lugosi, GaHyun Park, Wojciech Szpankowski
2009	J	`jnl`	Online Multi-task Learning with Hard Constraints CoRR Gábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz
2009	A*	`conf`	Online Multi-task Learning with Hard Constraints. COLT Gábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz
2008	J	`jnl`	A simple randomized algorithm for sequential prediction of ergodic time series. CoRR László Györfi, Gábor Lugosi, Gusztáv Morvai
2008	A*	`conf`	Concentration Inequalities. COLT Gábor Lugosi
2008	J	`jnl`	Consistency of Random Forests and Other Averaging Classifiers. J. Mach. Learn. Res. Gérard Biau, Luc Devroye, Gábor Lugosi
2008	J	`jnl`	On the Performance of Clustering in Hilbert Spaces. IEEE Trans. Inf. Theory Gérard Biau, Luc Devroye, Gábor Lugosi
2008	A*	`conf`	On-line Sequential Bin Packing. COLT András György, Gábor Lugosi, György Ottucsák
2008	J	`jnl`	Strategies for Prediction Under Imperfect Monitoring. Math. Oper. Res. Gábor Lugosi, Shie Mannor, Gilles Stoltz
2008	J	`jnl`	Tracking the Best Quantizer. IEEE Trans. Inf. Theory András György, Tamás Linder, Gábor Lugosi
2007	J	`jnl`	Global Nash convergence of Foster and Young's regret testing. Games Econ. Behav. Fabrizio Germano, Gábor Lugosi
2007	J	`jnl`	Introduction to the special issue on COLT 2006. Mach. Learn. Avrim Blum, Gábor Lugosi, Hans Ulrich Simon
2007	J	`jnl`	Learning correlated equilibria in games with compact sets of strategies. Games Econ. Behav. Gilles Stoltz, Gábor Lugosi
2007	A*	`conf`	Multiple choice tries and distributed hash tables. SODA Luc Devroye, Gábor Lugosi, GaHyun Park, Wojciech Szpankowski
2007	—	`conf`	Sequential prediction under incomplete feedback. CCIA Gábor Lugosi
2007	A*	`conf`	Strategies for Prediction Under Imperfect Monitoring. COLT Gábor Lugosi, Shie Mannor, Gilles Stoltz
2007	J	`jnl`	Strategies for prediction under imperfect monitoring CoRR Gábor Lugosi, Shie Mannor, Gilles Stoltz
2007	J	`jnl`	The On-Line Shortest Path Problem Under Partial Monitoring. J. Mach. Learn. Res. András György, Tamás Linder, Gábor Lugosi, György Ottucsák
2007	J	`jnl`	The on-line shortest path problem under partial monitoring CoRR András György, Tamás Linder, Gábor Lugosi, György Ottucsák
2006	A*	`ed.`	Learning Theory, 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings COLT Gábor Lugosi, Hans Ulrich Simon
2006	—	`book`	Prediction, learning, and games. Nicolò Cesa-Bianchi, Gábor Lugosi
2006	B	`conf`	Regret Minimization Under Partial Monitoring. ITW Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz
2006	J	`jnl`	Regret Minimization Under Partial Monitoring. Math. Oper. Res. Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz
2006	B	`conf`	The Shortest Path Problem in the Bandit Setting. ITW András György, Tamás Linder, Gábor Lugosi
2005	—	`conf`	From Ranking to Classification: A Statistical View. GfKl Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis
2005	J	`jnl`	Internal Regret in On-Line Portfolio Selection. Mach. Learn. Gilles Stoltz, Gábor Lugosi
2005	—	`conf`	Limited-Delay Coding of Individual Sequences with Piecewise Different Behavior. CDC/ECC András György, Tamás Linder, Gábor Lugosi
2005	J	`jnl`	Minimizing regret with label efficient prediction. IEEE Trans. Inf. Theory Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz
2005	A*	`conf`	Ranking and Scoring Using Empirical Risk Minimization. COLT Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis
2005	A*	`conf`	Tracking the Best of Many Experts. COLT András György, Tamás Linder, Gábor Lugosi
2005	B	`conf`	Tracking the best quantizer. ISIT András György, Tamás Linder, Gábor Lugosi
2004	B	`conf`	A "Follow the Perturbed Leader"-type Algorithm for Zero-Delay Quantization of Individual Sequence. Data Compression Conference András György, Tamás Linder, Gábor Lugosi
2004	J	`jnl`	Efficient adaptive algorithms and minimax bounds for zero-delay lossy source coding. IEEE Trans. Signal Process. András György, Tamás Linder, Gábor Lugosi
2004	B	`conf`	Efficient algorithms and minimax bounds for zero-delay lossy source coding. ISIT András György, Tamás Linder, Gábor Lugosi
2004	A*	`conf`	Minimizing Regret with Label Efficient Prediction. COLT Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz
2003	—	`conf`	Concentration Inequalities. Advanced Lectures on Machine Learning Stéphane Boucheron, Gábor Lugosi, Olivier Bousquet
2003	A*	`conf`	Internal Regret in On-Line Portfolio Selection. COLT Gilles Stoltz, Gábor Lugosi
2003	—	`conf`	Introduction to Statistical Learning Theory. Advanced Lectures on Machine Learning Olivier Bousquet, Stéphane Boucheron, Gábor Lugosi
2003	J	`jnl`	On the Rate of Convergence of Regularized Boosting Classifiers. J. Mach. Learn. Res. Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis
2003	J	`jnl`	Potential-Based Algorithms in On-Line Prediction and Game Theory. Mach. Learn. Nicolò Cesa-Bianchi, Gábor Lugosi
2002	A*	`conf`	A Consistent Strategy for Boosting Algorithms. COLT Gábor Lugosi, Nicolas Vayatis
2002	J	`jnl`	A note on robust hypothesis testing. IEEE Trans. Inf. Theory Luc Devroye, László Györfi, Gábor Lugosi
2002	J	`jnl`	Data-dependent margin-based generalization bounds for classification. J. Mach. Learn. Res. András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi
2002	J	`jnl`	Model Selection and Error Estimation. Mach. Learn. Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi
2001	J	`jnl`	A zero-delay sequential scheme for lossy coding of individual sequences. IEEE Trans. Inf. Theory Tamás Linder, Gábor Lugosi
2001	—	`book`	Combinatorial methods in density estimation. Luc Devroye, Gábor Lugosi
2001	—	`conf`	Data-Dependent Margin-Based Generalization Bounds for Classification. COLT/EuroCOLT Balázs Kégl, Tamás Linder, Gábor Lugosi
2001	—	`conf`	Potential-Based Algorithms in Online Prediction and Game Theory. COLT/EuroCOLT Nicolò Cesa-Bianchi, Gábor Lugosi
2001	J	`jnl`	Worst-Case Bounds for the Logarithmic Loss of Predictors. Mach. Learn. Nicolò Cesa-Bianchi, Gábor Lugosi
2000	J	`jnl`	A sharp concentration inequality with applications. Random Struct. Algorithms Stéphane Boucheron, Gábor Lugosi, Pascal Massart
2000	J	`jnl`	Finite-time lower bounds for the two-armed bandit problem. IEEE Trans. Autom. Control. Sanjeev R. Kulkarni, Gábor Lugosi
2000	A*	`conf`	Model Selection and Error Estimation. COLT Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi
1999	J	`jnl`	A simple randomized algorithm for sequential prediction of ergodic time series. IEEE Trans. Inf. Theory László Györfi, Gábor Lugosi, Gusztáv Morvai
1999	A*	`conf`	Minimax Regret Under log Loss for General Classes of Experts. COLT Nicolò Cesa-Bianchi, Gábor Lugosi
1998	J	`jnl`	Learning Pattern Classification - A Survey. IEEE Trans. Inf. Theory Sanjeev R. Kulkarni, Gábor Lugosi, Santosh S. Venkatesh
1998	A*	`conf`	On Sequential Prediction of Individual Sequences Relative to a Set of Experts. COLT Nicolò Cesa-Bianchi, Gábor Lugosi
1998	J	`jnl`	Scale-sensitive Dimensions and Skeleton Estimates for Classification. Discret. Appl. Math. Márta Horváth, Gábor Lugosi
1998	J	`jnl`	Strong Minimax Lower Bounds for Learning. Mach. Learn. András Antos, Gábor Lugosi
1998	J	`jnl`	The Minimax Distortion Redundancy in Empirical Quantizer Design. IEEE Trans. Inf. Theory Peter L. Bartlett, Tamás Linder, Gábor Lugosi
1997	—	`conf`	A Minimax Lower Bound for Empirical Quantizer Design. EuroCOLT Peter L. Bartlett, Tamás Linder, Gábor Lugosi
1997	J	`jnl`	Empirical quantizer design in the presence of source noise or channel noise. IEEE Trans. Inf. Theory Tamás Linder, Gábor Lugosi, Kenneth Zeger
1996	A*	`conf`	A Data-Dependent Skeleton Estimate for Learning. COLT Gábor Lugosi, Márta Pintér
1996	—	`book`	A Probabilistic Theory of Pattern Recognition Luc Devroye, László Györfi, Gábor Lugosi
1996	J	`jnl`	Concept learning using complexity regularization. IEEE Trans. Inf. Theory Gábor Lugosi, Kenneth Zeger
1996	B	`conf`	Designing Vector Quantizers in the Presence of Source Noise or Channel Noise. Data Compression Conference Tamás Linder, Gábor Lugosi, Kenneth Zeger
1996	J	`jnl`	Nonparametric estimation and classification using radial basis function nets and empirical risk minimization. IEEE Trans. Neural Networks Adam Krzyzak, Tamás Linder, Gábor Lugosi
1996	A*	`conf`	Strong Minimax Lower Bounds for Learning. COLT András Antos, Gábor Lugosi
1995	J	`jnl`	Fixed-rate universal lossy source coding and rates of convergence for memoryless sources. IEEE Trans. Inf. Theory Tamás Linder, Gábor Lugosi, Kenneth Zeger
1995	J	`jnl`	Lower bounds in pattern recognition and learning. Pattern Recognit. Luc Devroye, Gábor Lugosi
1995	J	`jnl`	Nonparametric estimation via empirical risk minimization. IEEE Trans. Inf. Theory Gábor Lugosi, Kenneth Zeger
1994	—	`conf`	Nonparametric classification using radial basis function nets and empirical risk minimization. ICPR (2) Adam Krzyzak, Tamás Linder, Gábor Lugosi
1994	J	`jnl`	On the posterior-probability estimate of the error rate of nonparametric classification rules. IEEE Trans. Inf. Theory Gábor Lugosi, Miroslaw Pawlak
1994	J	`jnl`	Rates of convergence in the source coding theorem, in empirical quantizer design, and in universal lossy source coding. IEEE Trans. Inf. Theory Tamás Linder, Gábor Lugosi, Kenneth Zeger
1993	J	`jnl`	Fast Nearest-Neighbor Search in Dissimilarity Spaces. IEEE Trans. Pattern Anal. Mach. Intell. András Faragó, Tamás Linder, Gábor Lugosi
1993	J	`jnl`	Strong universal consistency of neural network classifiers. IEEE Trans. Inf. Theory András Faragó, Gábor Lugosi
1993	B	`conf`	Universality and Rates of Convergence in Lossy Source Coding. Data Compression Conference Tamás Linder, Gábor Lugosi, Kenneth Zeger
1992	J	`jnl`	Learning with an unreliable teacher. Pattern Recognit. Gábor Lugosi