default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDGábor Lugosi
Person information
- affiliation: Universitat Pompeu Fabra, Barcelona, Spain
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j59]Roger Garriga, Vicenç Gómez, Gábor Lugosi:
Individualized post-crisis monitoring of psychiatric patients via Hidden Markov models. Frontiers Digit. Health 6 (2024)- [j58]Gábor Lugosi
, Mihalis G. Markakis, Gergely Neu:
On the Hardness of Learning from Censored and Nonstationary Demand. INFORMS J. Optim. 6(2): 63-83 (2024) - [i38]Simon Briend, Christophe Giraud, Gábor Lugosi, Déborah Sulem:
Estimating the history of a random recursive tree. CoRR abs/2403.09755 (2024) - [i37]Gábor Lugosi, Eulalia Nualart:
Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks. CoRR abs/2409.07401 (2024) - 2023
- [j57]Simon Briend, Francisco Calvillo, Gábor Lugosi:
Archaeology of random recursive dags and Cooper-Frieze random networks. Comb. Probab. Comput. 32(6): 859-873 (2023) - [j56]Gábor Lugosi, Ciara Pike-Burke, Pierre-André Savalle:
Bandit problems with fidelity rewards. J. Mach. Learn. Res. 24: 328:1-328:44 (2023) - [j55]Azadeh Khaleghi, Gábor Lugosi:
Inferring the Mixing Properties of a Stationary Ergodic Process From a Single Sample-Path. IEEE Trans. Inf. Theory 69(6): 4014-4026 (2023) - [i36]Gábor Lugosi, Gergely Neu:
Online-to-PAC Conversions: Generalization Bounds via Regret Analysis. CoRR abs/2305.19674 (2023) - [i35]Simon Briend, Luc Devroye, Gábor Lugosi:
Broadcasting in random recursive dags. CoRR abs/2306.01727 (2023) - [i34]Nicolas Broutin, Nina Kamcev, Gábor Lugosi:
Increasing paths in random temporal graphs. CoRR abs/2306.11401 (2023) - [i33]Simon Briend, Gábor Lugosi, Roberto Imbuzeiro Oliveira:
On the quality of randomized approximations of Tukey's depth. CoRR abs/2309.05657 (2023) - [i32]Caelan Atamanchuk, Luc Devroye, Gábor Lugosi:
A note on estimating the dimension from a random geometric graph. CoRR abs/2311.13059 (2023) - 2022
- [j54]Gábor Lugosi, Abbas Mehrabian:
Multiplayer Bandits Without Observing Collision Information. Math. Oper. Res. 47(2): 1247-1265 (2022) - [c42]Gábor Lugosi, Gergely Neu:
Generalization Bounds via Convex Analysis. COLT 2022: 3524-3546 - [i31]Gergely Neu, Gábor Lugosi:
Generalization Bounds via Convex Analysis. CoRR abs/2202.04985 (2022) - [i30]Simon Briend, Francisco Calvillo, Gábor Lugosi:
Archaeology of random recursive dags and Cooper-Frieze random networks. CoRR abs/2207.14601 (2022) - [i29]Nicolas Broutin, Luc Devroye, Gábor Lugosi, Roberto Imbuzeiro Oliveira:
Subtractive random forests. CoRR abs/2210.10544 (2022) - 2021
- [j53]Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik:
Learning partial correlation graphs and graphical models by covariance queries. J. Mach. Learn. Res. 22: 203:1-203:41 (2021) - [i28]Gábor Lugosi, Gergely Neu, Julia Olkhovskaya:
Learning to maximize global influence from local observations. CoRR abs/2109.11909 (2021) - [i27]Gábor Lugosi, Ciara Pike-Burke, Pierre-André Savalle:
Bandit problems with fidelity rewards. CoRR abs/2111.13026 (2021) - 2020
- [i26]Luc Devroye, Silvio Lattanzi, Gábor Lugosi, Nikita Zhivotovskiy:
On Mean Estimation for Heteroscedastic Random Variables. CoRR abs/2010.11537 (2020)
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) - [c41]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) - [c40]Yevgeny Seldin, Gábor Lugosi:
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits. COLT 2017: 1743-1759 - [c39]Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao:
Algorithmic Stability and Hypothesis Complexity. ICML 2017: 2159-2167 - [c38]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) - [c37]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) - [c36]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) - [c35]András György, Tamás Linder, Gábor Lugosi:
Efficient tracking of large classes of experts. ISIT 2012: 885-889 - [c34]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) - [c33]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) - [c32]Nicolò Cesa-Bianchi, Gábor Lugosi:
Combinatorial Bandits. COLT 2009 - [c31]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) - [c30]Gábor Lugosi:
Concentration Inequalities. COLT 2008: 7-8 - [c29]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) - [c28]Gábor Lugosi:
Sequential prediction under incomplete feedback. CCIA 2007: 3-5 - [c27]Gábor Lugosi, Shie Mannor, Gilles Stoltz:
Strategies for Prediction Under Imperfect Monitoring. COLT 2007: 248-262 - [c26]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) - [c25]Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz:
Regret Minimization Under Partial Monitoring. ITW 2006: 72-76 - [c24]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) - [c23]András György, Tamás Linder, Gábor Lugosi:
Limited-Delay Coding of Individual Sequences with Piecewise Different Behavior. CDC/ECC 2005: 8185-8190 - [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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-15 00:26 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
