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Semantic ScholarGábor Lugosi
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- affiliation: Universitat Pompeu Fabra, Barcelona, Spain
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2010 – 2019
- 2019
[j52]Gábor Lugosi, Shahar Mendelson:
Mean Estimation and Regression Under Heavy-Tailed Distributions: A Survey. Found. Comput. Math. 19(5): 1145-1190 (2019)- [c40]Gábor Lugosi, Gergely Neu, Julia Olkhovskaya:
Online Influence Maximization with Local Observations. ALT 2019: 557-580 - [i25]Gábor Lugosi, Shahar Mendelson:
Mean estimation and regression under heavy-tailed distributions-a survey. CoRR abs/1906.04280 (2019) - [i24]Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik:
Structure learning in graphical models by covariance queries. CoRR abs/1906.09501 (2019) - [i23]Peter L. Bartlett, Philip M. Long, Gábor Lugosi, Alexander Tsigler:
Benign Overfitting in Linear Regression. CoRR abs/1906.11300 (2019) - 2018
- [i22]Julia Olkhovskaya, Gergely Neu, Gábor Lugosi:
Online Influence Maximization with Local Observations. CoRR abs/1805.11022 (2018) - [i21]Gábor Lugosi, Abbas Mehrabian:
Multiplayer bandits without observing collision information. CoRR abs/1808.08416 (2018) - 2017
- [j51]Luc Devroye, László Györfi, Gábor Lugosi, Harro Walk:
On the measure of Voronoi cells. J. Appl. Probab. 54(2): 394-408 (2017) - [j50]Sébastien Bubeck, Luc Devroye, Gábor Lugosi:
Finding Adam in random growing trees. Random Struct. Algorithms 50(2): 158-172 (2017) - [c39]Yevgeny Seldin, Gábor Lugosi:
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits. COLT 2017: 1743-1759 - [c38]Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao:
Algorithmic Stability and Hypothesis Complexity. ICML 2017: 2159-2167 - [c37]Nicolò Cesa-Bianchi, Claudio Gentile, Gergely Neu, Gábor Lugosi:
Boltzmann Exploration Done Right. NIPS 2017: 6284-6293 - [i20]Yevgeny Seldin, Gábor Lugosi:
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits. CoRR abs/1702.06103 (2017) - [i19]Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao:
Algorithmic stability and hypothesis complexity. CoRR abs/1702.08712 (2017) - [i18]Nicolò Cesa-Bianchi, Claudio Gentile, Gábor Lugosi, Gergely Neu:
Boltzmann Exploration Done Right. CoRR abs/1705.10257 (2017) - [i17]Gábor Lugosi, Mihalis G. Markakis, Gergely Neu:
On the Hardness of Inventory Management with Censored Demand Data. CoRR abs/1710.05739 (2017) - [i16]Louigi Addario-Berry, Luc Devroye, Gábor Lugosi, Roberto Imbuzeiro Oliveira:
Local optima of the Sherrington-Kirkpatrick Hamiltonian. CoRR abs/1712.07775 (2017) - 2015
- [j49]Louigi Addario-Berry, Shankar Bhamidi, Sébastien Bubeck, Luc Devroye, Gábor Lugosi, Roberto Imbuzeiro Oliveira:
Exceptional rotations of random graphs: a VC theory. J. Mach. Learn. Res. 16: 1893-1922 (2015) - [j48]Luc Devroye, Gábor Lugosi, Gergely Neu:
Random-Walk Perturbations for Online Combinatorial Optimization. IEEE Trans. Inf. Theory 61(7): 4099-4106 (2015) - [i15]Matthias Hein, Gábor Lugosi, Lorenzo Rosasco:
Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361). Dagstuhl Reports 5(8): 54-0 (2015) - 2014
- [j47]Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi:
Regret in Online Combinatorial Optimization. Math. Oper. Res. 39(1): 31-45 (2014) - [j46]Nicolas Broutin, Luc Devroye, Nicolas Fraiman, Gábor Lugosi
:
Connectivity threshold of Bluetooth graphs. Random Struct. Algorithms 44(1): 45-66 (2014) - [j45]Rui M. Castro, Gábor Lugosi, Pierre-André Savalle:
Detection of Correlations With Adaptive Sensing. IEEE Trans. Inf. Theory 60(12): 7913-7927 (2014) - [c36]Morteza Alamgir, Gábor Lugosi, Ulrike von Luxburg:
Density-preserving quantization with application to graph downsampling. COLT 2014: 543-559 - [i14]Nicolas Broutin, Luc Devroye, Gábor Lugosi:
Connectivity of sparse Bluetooth networks. CoRR abs/1402.3696 (2014) - [i13]Nicolas Broutin, Luc Devroye, Gábor Lugosi:
Almost optimal sparsification of random geometric graphs. CoRR abs/1403.1274 (2014) - [i12]Sébastien Bubeck, Luc Devroye, Gábor Lugosi:
Finding Adam in random growing trees. CoRR abs/1411.3317 (2014) - 2013
- [b4]Stéphane Boucheron, Gábor Lugosi, Pascal Massart:
Concentration Inequalities - A Nonasymptotic Theory of Independence. Oxford University Press 2013, ISBN 978-0-19-953525-5, pp. 1-449 - [j44]Sébastien Bubeck, Nicolò Cesa-Bianchi, Gábor Lugosi:
Bandits With Heavy Tail. IEEE Trans. Inf. Theory 59(11): 7711-7717 (2013) - [c35]Luc Devroye, Gábor Lugosi, Gergely Neu:
Prediction by random-walk perturbation. COLT 2013: 460-473 - [i11]Luc Devroye, Gábor Lugosi, Gergely Neu:
Prediction by Random-Walk Perturbation. CoRR abs/1302.5797 (2013) - 2012
- [j43]Nicolò Cesa-Bianchi, Gábor Lugosi:
Combinatorial bandits. J. Comput. Syst. Sci. 78(5): 1404-1422 (2012) - [j42]András György, Tamás Linder, Gábor Lugosi:
Efficient Tracking of Large Classes of Experts. IEEE Trans. Inf. Theory 58(11): 6709-6725 (2012) - [c34]András György, Tamás Linder, Gábor Lugosi:
Efficient tracking of large classes of experts. ISIT 2012: 885-889 - [c33]Nicolò Cesa-Bianchi, Pierre Gaillard, Gábor Lugosi, Gilles Stoltz:
Mirror Descent Meets Fixed Share (and feels no regret). NIPS 2012: 989-997 - [i10]Nicolò Cesa-Bianchi, Pierre Gaillard, Gábor Lugosi, Gilles Stoltz:
A new look at shifting regret. CoRR abs/1202.3323 (2012) - [i9]Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi:
Regret in Online Combinatorial Optimization. CoRR abs/1204.4710 (2012) - [i8]Sébastien Bubeck, Nicolò Cesa-Bianchi, Gábor Lugosi:
Bandits with heavy tail. CoRR abs/1209.1727 (2012) - 2011
- [j41]Gábor Lugosi, Sandra Zilles:
Preface. Theor. Comput. Sci. 412(19): 1755 (2011) - [c32]Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi:
Minimax Policies for Combinatorial Prediction Games. COLT 2011: 107-132 - [i7]Nicolas Broutin, Luc Devroye, Nicolas Fraiman, Gábor Lugosi:
Connectivity threshold for Bluetooth graphs. CoRR abs/1103.0351 (2011) - [i6]András György, Tamás Linder, Gábor Lugosi:
Efficient Tracking of Large Classes of Experts. CoRR abs/1110.2755 (2011) - [i5]Matthias Hein, Gábor Lugosi, Lorenzo Rosasco, Steve Smale:
Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291). Dagstuhl Reports 1(7): 53-69 (2011) - 2010
- [j40]Louigi Addario-Berry, Nicolas Broutin, Gábor Lugosi:
The Longest Minimum-Weight Path in a Complete Graph. Comb. Probab. Comput. 19(1): 1-19 (2010) - [j39]András György, Gábor Lugosi, György Ottucsák:
On-Line Sequential Bin Packing. J. Mach. Learn. Res. 11: 89-109 (2010)
2000 – 2009
- 2009
- [j38]Luc Devroye, Gábor Lugosi, GaHyun Park, Wojciech Szpankowski:
Multiple choice tries and distributed hash tables. Random Struct. Algorithms 34(3): 337-367 (2009) - [c31]Nicolò Cesa-Bianchi, Gábor Lugosi:
Combinatorial Bandits. COLT 2009 - [c30]Gábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz:
Online Multi-task Learning with Hard Constraints. COLT 2009 - [e2]Ricard Gavaldà, Gábor Lugosi, Thomas Zeugmann, Sandra Zilles:
Algorithmic Learning Theory, 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009. Proceedings. Lecture Notes in Computer Science 5809, Springer 2009, ISBN 978-3-642-04413-7 [contents] - [i4]Gábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz:
Online Multi-task Learning with Hard Constraints. CoRR abs/0902.3526 (2009) - 2008
- [j37]Gérard Biau, Luc Devroye, Gábor Lugosi:
Consistency of Random Forests and Other Averaging Classifiers. J. Mach. Learn. Res. 9: 2015-2033 (2008) - [j36]Gábor Lugosi, Shie Mannor, Gilles Stoltz:
Strategies for Prediction Under Imperfect Monitoring. Math. Oper. Res. 33(3): 513-528 (2008) - [j35]Gérard Biau, Luc Devroye, Gábor Lugosi:
On the Performance of Clustering in Hilbert Spaces. IEEE Trans. Inf. Theory 54(2): 781-790 (2008) - [j34]András György, Tamás Linder, Gábor Lugosi:
Tracking the Best Quantizer. IEEE Trans. Inf. Theory 54(4): 1604-1625 (2008) - [c29]Gábor Lugosi:
Concentration Inequalities. COLT 2008: 7-8 - [c28]András György, Gábor Lugosi, György Ottucsák:
On-line Sequential Bin Packing. COLT 2008: 447-454 - [i3]László Györfi, Gábor Lugosi, Gusztáv Morvai:
A simple randomized algorithm for sequential prediction of ergodic time series. CoRR abs/0805.3091 (2008) - 2007
- [j33]Gilles Stoltz, Gábor Lugosi:
Learning correlated equilibria in games with compact sets of strategies. Games Econ. Behav. 59(1): 187-208 (2007) - [j32]Fabrizio Germano, Gábor Lugosi:
Global Nash convergence of Foster and Young's regret testing. Games Econ. Behav. 60(1): 135-154 (2007) - [j31]András György, Tamás Linder, Gábor Lugosi, György Ottucsák:
The On-Line Shortest Path Problem Under Partial Monitoring. J. Mach. Learn. Res. 8: 2369-2403 (2007) - [j30]Avrim Blum, Gábor Lugosi, Hans Ulrich Simon:
Introduction to the special issue on COLT 2006. Mach. Learn. 69(2-3): 75-77 (2007) - [c27]Gábor Lugosi:
Sequential prediction under incomplete feedback. CCIA 2007: 3-5 - [c26]Gábor Lugosi, Shie Mannor, Gilles Stoltz:
Strategies for Prediction Under Imperfect Monitoring. COLT 2007: 248-262 - [c25]Luc Devroye, Gábor Lugosi, GaHyun Park, Wojciech Szpankowski:
Multiple choice tries and distributed hash tables. SODA 2007: 891-899 - [i2]András György, Tamás Linder, Gábor Lugosi, György Ottucsák:
The on-line shortest path problem under partial monitoring. CoRR abs/0704.1020 (2007) - [i1]Gábor Lugosi, Shie Mannor, Gilles Stoltz:
Strategies for prediction under imperfect monitoring. CoRR abs/math/0701419 (2007) - 2006
- [b3]Nicolò Cesa-Bianchi, Gábor Lugosi:
Prediction, learning, and games. Cambridge University Press 2006, ISBN 978-0-521-84108-5, pp. I-XII, 1-394 - [j29]Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz:
Regret Minimization Under Partial Monitoring. Math. Oper. Res. 31(3): 562-580 (2006) - [c24]Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz:
Regret Minimization Under Partial Monitoring. ITW 2006: 72-76 - [c23]András György, Tamás Linder, Gábor Lugosi:
The Shortest Path Problem in the Bandit Setting. ITW 2006: 87-91 - [e1]Gábor Lugosi, Hans Ulrich Simon:
Learning Theory, 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings. Lecture Notes in Computer Science 4005, Springer 2006, ISBN 3-540-35294-5 [contents] - 2005
- [j28]Gilles Stoltz, Gábor Lugosi:
Internal Regret in On-Line Portfolio Selection. Mach. Learn. 59(1-2): 125-159 (2005) - [j27]Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz:
Minimizing regret with label efficient prediction. IEEE Trans. Inf. Theory 51(6): 2152-2162 (2005) - [c22]Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis:
Ranking and Scoring Using Empirical Risk Minimization. COLT 2005: 1-15 - [c21]András György, Tamás Linder, Gábor Lugosi:
Tracking the Best of Many Experts. COLT 2005: 204-216 - [c20]Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis:
From Ranking to Classification: A Statistical View. GfKl 2005: 214-221 - [c19]András György, Tamás Linder, Gábor Lugosi:
Tracking the best quantizer. ISIT 2005: 1163-1167 - 2004
- [j26]András György, Tamás Linder, Gábor Lugosi:
Efficient adaptive algorithms and minimax bounds for zero-delay lossy source coding. IEEE Trans. Signal Process. 52(8): 2337-2347 (2004) - [c18]Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz:
Minimizing Regret with Label Efficient Prediction. COLT 2004: 77-92 - [c17]András György, Tamás Linder, Gábor Lugosi:
A "Follow the Perturbed Leader"-type Algorithm for Zero-Delay Quantization of Individual Sequence. Data Compression Conference 2004: 342-351 - [c16]András György, Tamás Linder, Gábor Lugosi:
Efficient algorithms and minimax bounds for zero-delay lossy source coding. ISIT 2004: 463 - 2003
- [j25]Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis:
On the Rate of Convergence of Regularized Boosting Classifiers. J. Mach. Learn. Res. 4: 861-894 (2003) - [j24]Nicolò Cesa-Bianchi, Gábor Lugosi:
Potential-Based Algorithms in On-Line Prediction and Game Theory. Mach. Learn. 51(3): 239-261 (2003) - [c15]Olivier Bousquet, Stéphane Boucheron, Gábor Lugosi:
Introduction to Statistical Learning Theory. Advanced Lectures on Machine Learning 2003: 169-207 - [c14]Stéphane Boucheron, Gábor Lugosi, Olivier Bousquet:
Concentration Inequalities. Advanced Lectures on Machine Learning 2003: 208-240 - [c13]Gilles Stoltz, Gábor Lugosi:
Internal Regret in On-Line Portfolio Selection. COLT 2003: 403-417 - 2002
- [j23]András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi:
Data-dependent margin-based generalization bounds for classification. J. Mach. Learn. Res. 3: 73-98 (2002) - [j22]Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi:
Model Selection and Error Estimation. Mach. Learn. 48(1-3): 85-113 (2002) - [j21]Luc Devroye, László Györfi, Gábor Lugosi:
A note on robust hypothesis testing. IEEE Trans. Inf. Theory 48(7): 2111-2114 (2002) - [c12]Gábor Lugosi, Nicolas Vayatis:
A Consistent Strategy for Boosting Algorithms. COLT 2002: 303-318 - 2001
- [b2]Luc Devroye, Gábor Lugosi:
Combinatorial methods in density estimation. Springer series in statistics, Springer 2001, ISBN 978-0-387-95117-1, pp. I-XII, 1-208 - [j20]Nicolò Cesa-Bianchi, Gábor Lugosi:
Worst-Case Bounds for the Logarithmic Loss of Predictors. Mach. Learn. 43(3): 247-264 (2001) - [j19]Tamás Linder, Gábor Lugosi:
A zero-delay sequential scheme for lossy coding of individual sequences. IEEE Trans. Inf. Theory 47(6): 2533-2538 (2001) - [c11]Nicolò Cesa-Bianchi, Gábor Lugosi:
Potential-Based Algorithms in Online Prediction and Game Theory. COLT/EuroCOLT 2001: 48-64 - [c10]Balázs Kégl, Tamás Linder, Gábor Lugosi:
Data-Dependent Margin-Based Generalization Bounds for Classification. COLT/EuroCOLT 2001: 368-384 - 2000
- [j18]Stéphane Boucheron, Gábor Lugosi, Pascal Massart:
A sharp concentration inequality with applications. Random Struct. Algorithms 16(3): 277-292 (2000) - [j17]Sanjeev R. Kulkarni, Gábor Lugosi:
Finite-time lower bounds for the two-armed bandit problem. IEEE Trans. Autom. Control. 45(4): 711-714 (2000) - [c9]Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi:
Model Selection and Error Estimation. COLT 2000: 286-297
1990 – 1999
- 1999
- [j16]László Györfi, Gábor Lugosi, Gusztáv Morvai:
A simple randomized algorithm for sequential prediction of ergodic time series. IEEE Trans. Inf. Theory 45(7): 2642-2650 (1999) - [c8]Nicolò Cesa-Bianchi, Gábor Lugosi:
Minimax Regret Under log Loss for General Classes of Experts. COLT 1999: 12-18 - 1998
- [j15]Márta Horváth, Gábor Lugosi:
Scale-sensitive Dimensions and Skeleton Estimates for Classification. Discret. Appl. Math. 86(1): 37-61 (1998) - [j14]András Antos, Gábor Lugosi:
Strong Minimax Lower Bounds for Learning. Mach. Learn. 30(1): 31-56 (1998) - [j13]Peter L. Bartlett, Tamás Linder, Gábor Lugosi:
The Minimax Distortion Redundancy in Empirical Quantizer Design. IEEE Trans. Inf. Theory 44(5): 1802-1813 (1998) - [j12]Sanjeev R. Kulkarni, Gábor Lugosi, Santosh S. Venkatesh:
Learning Pattern Classification - A Survey. IEEE Trans. Inf. Theory 44(6): 2178-2206 (1998) - [c7]Nicolò Cesa-Bianchi, Gábor Lugosi:
On Sequential Prediction of Individual Sequences Relative to a Set of Experts. COLT 1998: 1-11 - 1997
- [j11]Tamás Linder, Gábor Lugosi, Kenneth Zeger:
Empirical quantizer design in the presence of source noise or channel noise. IEEE Trans. Inf. Theory 43(2): 612-623 (1997) - [c6]Peter L. Bartlett, Tamás Linder, Gábor Lugosi:
A Minimax Lower Bound for Empirical Quantizer Design. EuroCOLT 1997: 210-222 - 1996
- [b1]Luc Devroye, László Györfi, Gábor Lugosi:
A Probabilistic Theory of Pattern Recognition. Stochastic Modelling and Applied Probability 31, Springer 1996, ISBN 978-1-4612-6877-2, pp. 1-638 - [j10]Gábor Lugosi, Kenneth Zeger:
Concept learning using complexity regularization. IEEE Trans. Inf. Theory 42(1): 48-54 (1996) - [j9]Adam Krzyzak, Tamás Linder, Gábor Lugosi:
Nonparametric estimation and classification using radial basis function nets and empirical risk minimization. IEEE Trans. Neural Networks 7(2): 475-487 (1996) - [c5]Gábor Lugosi, Márta Pintér:
A Data-Dependent Skeleton Estimate for Learning. COLT 1996: 51-56 - [c4]András Antos, Gábor Lugosi:
Strong Minimax Lower Bounds for Learning. COLT 1996: 303-309 - [c3]Tamás Linder, Gábor Lugosi, Kenneth Zeger:
Designing Vector Quantizers in the Presence of Source Noise or Channel Noise. Data Compression Conference 1996: 33-42 - 1995
- [j8]Luc Devroye, Gábor Lugosi:
Lower bounds in pattern recognition and learning. Pattern Recognit. 28(7): 1011-1018 (1995) - [j7]Tamás Linder, Gábor Lugosi, Kenneth Zeger:
Fixed-rate universal lossy source coding and rates of convergence for memoryless sources. IEEE Trans. Inf. Theory 41(3): 665-676 (1995) - [j6]Gábor Lugosi, Kenneth Zeger:
Nonparametric estimation via empirical risk minimization. IEEE Trans. Inf. Theory 41(3): 677-687 (1995) - 1994
- [j5]Gábor Lugosi, Miroslaw Pawlak:
On the posterior-probability estimate of the error rate of nonparametric classification rules. IEEE Trans. Inf. Theory 40(2): 475-481 (1994) - [j4]Tamás Linder, Gábor Lugosi, Kenneth Zeger:
Rates of convergence in the source coding theorem, in empirical quantizer design, and in universal lossy source coding. IEEE Trans. Inf. Theory 40(6): 1728-1740 (1994) - [c2]Adam Krzyzak, Tamás Linder, Gábor Lugosi:
Nonparametric classification using radial basis function nets and empirical risk minimization. ICPR (2) 1994: 72-76 - 1993
- [j3]András Faragó, Tamás Linder, Gábor Lugosi:
Fast Nearest-Neighbor Search in Dissimilarity Spaces. IEEE Trans. Pattern Anal. Mach. Intell. 15(9): 957-962 (1993) - [j2]András Faragó, Gábor Lugosi:
Strong universal consistency of neural network classifiers. IEEE Trans. Inf. Theory 39(4): 1146-1151 (1993) - [c1]Tamás Linder, Gábor Lugosi, Kenneth Zeger:
Universality and Rates of Convergence in Lossy Source Coding. Data Compression Conference 1993: 89-97 - 1992
- [j1]Gábor Lugosi:
Learning with an unreliable teacher. Pattern Recognit. 25(1): 79-87 (1992)
Coauthor Index