Gábor Lugosi - researchr alias

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2011

PrefaceGábor Lugosi, Sandra Zilles. TCS, 412(19):1755, 2011. [doi]

2010

On-Line Sequential Bin PackingAndrás György, Gábor Lugosi, György Ottucsák. jmlr, 11:89-109, 2010. [doi]

2009

Combinatorial BanditsNicolò Cesa-Bianchi, Gábor Lugosi. colt 2009: [doi]

Multiple choice tries and distributed hash tablesLuc Devroye, Gábor Lugosi, GaHyun Park, Wojciech Szpankowski. rsa, 34(3):337-367, 2009. [doi]

Online Multi-task Learning with Hard ConstraintsGábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz. colt 2009: [doi]

2008

On the Performance of Clustering in Hilbert SpacesGérard Biau, Luc Devroye, Gábor Lugosi. TIT, 54(2):781-790, 2008. [doi]

Consistency of Random Forests and Other Averaging ClassifiersGérard Biau, Luc Devroye, Gábor Lugosi. jmlr, 9:2015-2033, 2008. [doi]

On-line Sequential Bin PackingAndrás György, Gábor Lugosi, György Ottucsák. colt 2008: 447-454 [doi]

Tracking the Best QuantizerAndrás György, Tamás Linder, Gábor Lugosi. TIT, 54(4):1604-1625, 2008. [doi]

Concentration InequalitiesGábor Lugosi. colt 2008: 7-8 [doi]

2007

2006

Prediction, learning, and gamesNicolò Cesa-Bianchi, Gábor Lugosi. Cambridge University Press, 2006.

Learning Theory, 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, ProceedingsGábor Lugosi, Hans-Ulrich Simon, editors, Volume 4005 of Lecture Notes in Computer Science, Springer, 2006.

2005

2004

Minimizing Regret with Label Efficient PredictionNicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz. colt 2004: 77-92 [doi]

A Follow the Perturbed Leader -type Algorithm for Zero-Delay Quantization of Individual SequenceAndrás György, Tamás Linder, Gábor Lugosi. dcc 2004: 342-351 [doi]

2003

2002

Data-dependent margin-based generalization bounds for classificationAndrás Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi. jmlr, 3:73-98, 2002. [doi]

Model Selection and Error EstimationPeter L. Bartlett, Stéphane Boucheron, Gábor Lugosi. ml, 48(1-3):85-113, 2002.

A note on robust hypothesis testingLuc Devroye, László Györfi, Gábor Lugosi. TIT, 48(7):2111-2114, 2002.

A Consistent Strategy for Boosting AlgorithmsGábor Lugosi, Nicolas Vayatis. colt 2002: 303-318 [doi]

2001

Worst-Case Bounds for the Logarithmic Loss of PredictorsNicolò Cesa-Bianchi, Gábor Lugosi. ml, 43(3):247-264, 2001.

Potential-Based Algorithms in Online Prediction and Game TheoryNicolò Cesa-Bianchi, Gábor Lugosi. colt 2001: 48-64 [doi]

Data-Dependent Margin-Based Generalization Bounds for ClassificationBalázs Kégl, Tamás Linder, Gábor Lugosi. colt 2001: 368-384 [doi]

A zero-delay sequential scheme for lossy coding of individual sequencesTamás Linder, Gábor Lugosi. TIT, 47(6):2533-2538, 2001.

2000

Model Selection and Error EstimationPeter L. Bartlett, Stéphane Boucheron, Gábor Lugosi. colt 2000: 286-297

A sharp concentration inequality with applicationsStéphane Boucheron, Gábor Lugosi, Pascal Massart. rsa, 16(3):277-292, 2000.

1999

Minimax Regret Under log Loss for General Classes of ExpertsNicolò Cesa-Bianchi, Gábor Lugosi. colt 1999: 12-18 [doi]

A simple randomized algorithm for sequential prediction of ergodic time seriesLászló Györfi, Gábor Lugosi, Gusztáv Morvai. TIT, 45(7):2642-2650, 1999.

1998

1997

A Minimax Lower Bound for Empirical Quantizer DesignPeter L. Bartlett, Tamás Linder, Gábor Lugosi. eurocolt 1997: 210-222

Empirical quantizer design in the presence of source noise or channel noiseTamás Linder, Gábor Lugosi, Kenneth Zeger. TIT, 43(2):612-623, 1997.

1996

Strong Minimax Lower Bounds for LearningAndrás Antos, Gábor Lugosi. colt 1996: 303-309 [doi]

Designing Vector Quantizers in the Presence of Source Noise or Channel NoiseTamás Linder, Gábor Lugosi, Kenneth Zeger. dcc 1996: 33-42

Concept learning using complexity regularizationGábor Lugosi, Kenneth Zeger. TIT, 42(1):48-54, 1996.

A Data-Dependent Skeleton Estimate for LearningGábor Lugosi, Márta Pintér. colt 1996: 51-56 [doi]

1995

Lower bounds in pattern recognition and learningLuc Devroye, Gábor Lugosi. PR, 28(7):1011-1018, 1995. [doi]

Fixed-rate universal lossy source coding and rates of convergence for memoryless sourcesTamás Linder, Gábor Lugosi, Kenneth Zeger. TIT, 41(3):665-676, 1995.

Nonparametric estimation via empirical risk minimizationGábor Lugosi, Kenneth Zeger. TIT, 41(3):677-687, 1995.

1994

Rates of convergence in the source coding theorem, in empirical quantizer design, and in universal lossy source codingTamás Linder, Gábor Lugosi, Kenneth Zeger. TIT, 40(6):1728-1740, 1994.

On the posterior-probability estimate of the error rate of nonparametric classification rulesGábor Lugosi, Miroslaw Pawlak. TIT, 40(2):475-481, 1994.

1993

Fast Nearest-Neighbor Search in Dissimilarity SpacesAndrás Faragó, Tamás Linder, Gábor Lugosi. pami, 15(9):957-962, 1993. [doi]

Strong universal consistency of neural network classifiersAndrás Faragó, Gábor Lugosi. TIT, 39(4):1146-1151, 1993.

Universality and Rates of Convergence in Lossy Source CodingTamás Linder, Gábor Lugosi, Kenneth Zeger. dcc 1993: 89-97

1992

Learning with an unreliable teacherGábor Lugosi. PR, 25(1):79-87, 1992. [doi]