| * | 2009 |
|---|
| 60 | EE | Ricard Gavaldà,
Gábor Lugosi,
Thomas Zeugmann,
Sandra Zilles:
Algorithmic Learning Theory, 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009. Proceedings
Springer 2009 |
| 59 | EE | Gábor Lugosi,
Omiros Papaspiliopoulos,
Gilles Stoltz:
Online Multi-task Learning with Hard Constraints
CoRR abs/0902.3526: (2009) |
| 58 | EE | Luc Devroye,
Gábor Lugosi,
GaHyun Park,
Wojciech Szpankowski:
Multiple choice tries and distributed hash tables.
Random Struct. Algorithms 34(3): 337-367 (2009) |
| 2008 |
|---|
| 57 | EE | András György,
Gábor Lugosi,
György Ottucsák:
On-line Sequential Bin Packing.
COLT 2008: 447-454 |
| 56 | EE | Gábor Lugosi:
Concentration Inequalities.
COLT 2008: 7-8 |
| 55 | EE | Gérard Biau,
Luc Devroye,
Gábor Lugosi:
On the Performance of Clustering in Hilbert Spaces.
IEEE Transactions on Information Theory 54(2): 781-790 (2008) |
| 54 | EE | András György,
Tamás Linder,
Gábor Lugosi:
Tracking the Best Quantizer.
IEEE Transactions on Information Theory 54(4): 1604-1625 (2008) |
| 53 | EE | Gábor Lugosi,
Shie Mannor,
Gilles Stoltz:
Strategies for Prediction Under Imperfect Monitoring.
Math. Oper. Res. 33(3): 513-528 (2008) |
| 2007 |
|---|
| 52 | | Gábor Lugosi:
Sequential prediction under incomplete feedback.
CCIA 2007: 3-5 |
| 51 | EE | Gábor Lugosi,
Shie Mannor,
Gilles Stoltz:
Strategies for Prediction Under Imperfect Monitoring.
COLT 2007: 248-262 |
| 50 | EE | Luc Devroye,
Gábor Lugosi,
GaHyun Park,
Wojciech Szpankowski:
Multiple choice tries and distributed hash tables.
SODA 2007: 891-899 |
| 49 | EE | András György,
Tamás Linder,
Gábor Lugosi,
György Ottucsák:
The on-line shortest path problem under partial monitoring
CoRR abs/0704.1020: (2007) |
| 48 | EE | Gábor Lugosi,
Shie Mannor,
Gilles Stoltz:
Strategies for prediction under imperfect monitoring
CoRR abs/math/0701419: (2007) |
| 47 | EE | Avrim Blum,
Gábor Lugosi,
Hans-Ulrich Simon:
Introduction to the special issue on COLT 2006.
Machine Learning 69(2-3): 75-77 (2007) |
| 2006 |
|---|
| 46 | | Gábor Lugosi,
Hans-Ulrich Simon:
Learning Theory, 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings
Springer 2006 |
| 45 | EE | Nicolò Cesa-Bianchi,
Gábor Lugosi,
Gilles Stoltz:
Regret Minimization Under Partial Monitoring.
Math. Oper. Res. 31(3): 562-580 (2006) |
| 2005 |
|---|
| 44 | EE | Stéphan Clémençon,
Gábor Lugosi,
Nicolas Vayatis:
Ranking and Scoring Using Empirical Risk Minimization.
COLT 2005: 1-15 |
| 43 | EE | András György,
Tamás Linder,
Gábor Lugosi:
Tracking the Best of Many Experts.
COLT 2005: 204-216 |
| 42 | EE | Stéphan Clémençon,
Gábor Lugosi,
Nicolas Vayatis:
From Ranking to Classification: A Statistical View.
GfKl 2005: 214-221 |
| 41 | EE | Nicolò Cesa-Bianchi,
Gábor Lugosi,
Gilles Stoltz:
Minimizing regret with label efficient prediction.
IEEE Transactions on Information Theory 51(6): 2152-2162 (2005) |
| 40 | EE | Gilles Stoltz,
Gábor Lugosi:
Internal Regret in On-Line Portfolio Selection.
Machine Learning 59(1-2): 125-159 (2005) |
| 2004 |
|---|
| 39 | EE | Nicolò Cesa-Bianchi,
Gábor Lugosi,
Gilles Stoltz:
Minimizing Regret with Label Efficient Prediction.
COLT 2004: 77-92 |
| 38 | EE | 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 |
| 2003 |
|---|
| 37 | EE | Olivier Bousquet,
Stéphane Boucheron,
Gábor Lugosi:
Introduction to Statistical Learning Theory.
Advanced Lectures on Machine Learning 2003: 169-207 |
| 36 | EE | Stéphane Boucheron,
Gábor Lugosi,
Olivier Bousquet:
Concentration Inequalities.
Advanced Lectures on Machine Learning 2003: 208-240 |
| 35 | EE | Gilles Stoltz,
Gábor Lugosi:
Internal Regret in On-Line Portfolio Selection.
COLT 2003: 403-417 |
| 34 | EE | Gilles Blanchard,
Gábor Lugosi,
Nicolas Vayatis:
On the Rate of Convergence of Regularized Boosting Classifiers.
Journal of Machine Learning Research 4: 861-894 (2003) |
| 33 | EE | Nicolò Cesa-Bianchi,
Gábor Lugosi:
Potential-Based Algorithms in On-Line Prediction and Game Theory.
Machine Learning 51(3): 239-261 (2003) |
| 2002 |
|---|
| 32 | EE | Gábor Lugosi,
Nicolas Vayatis:
A Consistent Strategy for Boosting Algorithms.
COLT 2002: 303-318 |
| 31 | | Luc Devroye,
László Györfi,
Gábor Lugosi:
A note on robust hypothesis testing.
IEEE Transactions on Information Theory 48(7): 2111-2114 (2002) |
| 30 | EE | András Antos,
Balázs Kégl,
Tamás Linder,
Gábor Lugosi:
Data-dependent margin-based generalization bounds for classification.
Journal of Machine Learning Research 3: 73-98 (2002) |
| 29 | | Peter L. Bartlett,
Stéphane Boucheron,
Gábor Lugosi:
Model Selection and Error Estimation.
Machine Learning 48(1-3): 85-113 (2002) |
| 2001 |
|---|
| 28 | EE | Balázs Kégl,
Tamás Linder,
Gábor Lugosi:
Data-Dependent Margin-Based Generalization Bounds for Classification.
COLT/EuroCOLT 2001: 368-384 |
| 27 | EE | Nicolò Cesa-Bianchi,
Gábor Lugosi:
Potential-Based Algorithms in Online Prediction and Game Theory.
COLT/EuroCOLT 2001: 48-64 |
| 26 | | Tamás Linder,
Gábor Lugosi:
A zero-delay sequential scheme for lossy coding of individual sequences.
IEEE Transactions on Information Theory 47(6): 2533-2538 (2001) |
| 25 | | Nicolò Cesa-Bianchi,
Gábor Lugosi:
Worst-Case Bounds for the Logarithmic Loss of Predictors.
Machine Learning 43(3): 247-264 (2001) |
| 2000 |
|---|
| 24 | | Peter L. Bartlett,
Stéphane Boucheron,
Gábor Lugosi:
Model Selection and Error Estimation.
COLT 2000: 286-297 |
| 23 | | Stéphane Boucheron,
Gábor Lugosi,
Pascal Massart:
A sharp concentration inequality with applications.
Random Struct. Algorithms 16(3): 277-292 (2000) |
| 1999 |
|---|
| 22 | EE | Nicolò Cesa-Bianchi,
Gábor Lugosi:
Minimax Regret Under log Loss for General Classes of Experts.
COLT 1999: 12-18 |
| 21 | | László Györfi,
Gábor Lugosi,
Gusztáv Morvai:
A simple randomized algorithm for sequential prediction of ergodic time series.
IEEE Transactions on Information Theory 45(7): 2642-2650 (1999) |
| 1998 |
|---|
| 20 | EE | Nicolò Cesa-Bianchi,
Gábor Lugosi:
On Sequential Prediction of Individual Sequences Relative to a Set of Experts.
COLT 1998: 1-11 |
| 19 | EE | Márta Horváth,
Gábor Lugosi:
Scale-sensitive Dimensions and Skeleton Estimates for Classification.
Discrete Applied Mathematics 86(1): 37-61 (1998) |
| 18 | | Peter L. Bartlett,
Tamás Linder,
Gábor Lugosi:
The Minimax Distortion Redundancy in Empirical Quantizer Design.
IEEE Transactions on Information Theory 44(5): 1802-1813 (1998) |
| 17 | | Sanjeev R. Kulkarni,
Gábor Lugosi,
Santosh S. Venkatesh:
Learning Pattern Classification - A Survey.
IEEE Transactions on Information Theory 44(6): 2178-2206 (1998) |
| 16 | | András Antos,
Gábor Lugosi:
Strong Minimax Lower Bounds for Learning.
Machine Learning 30(1): 31-56 (1998) |
| 1997 |
|---|
| 15 | | Peter L. Bartlett,
Tamás Linder,
Gábor Lugosi:
A Minimax Lower Bound for Empirical Quantizer Design.
EuroCOLT 1997: 210-222 |
| 14 | | Tamás Linder,
Gábor Lugosi,
Kenneth Zeger:
Empirical quantizer design in the presence of source noise or channel noise.
IEEE Transactions on Information Theory 43(2): 612-623 (1997) |
| 1996 |
|---|
| 13 | EE | András Antos,
Gábor Lugosi:
Strong Minimax Lower Bounds for Learning.
COLT 1996: 303-309 |
| 12 | EE | Gábor Lugosi,
Márta Pintér:
A Data-Dependent Skeleton Estimate for Learning.
COLT 1996: 51-56 |
| 11 | | Tamás Linder,
Gábor Lugosi,
Kenneth Zeger:
Designing Vector Quantizers in the Presence of Source Noise or Channel Noise.
Data Compression Conference 1996: 33-42 |
| 10 | | Gábor Lugosi,
Kenneth Zeger:
Concept learning using complexity regularization.
IEEE Transactions on Information Theory 42(1): 48-54 (1996) |
| 1995 |
|---|
| 9 | | Tamás Linder,
Gábor Lugosi,
Kenneth Zeger:
Fixed-rate universal lossy source coding and rates of convergence for memoryless sources.
IEEE Transactions on Information Theory 41(3): 665-676 (1995) |
| 8 | | Gábor Lugosi,
Kenneth Zeger:
Nonparametric estimation via empirical risk minimization.
IEEE Transactions on Information Theory 41(3): 677-687 (1995) |
| 7 | EE | Luc Devroye,
Gábor Lugosi:
Lower bounds in pattern recognition and learning.
Pattern Recognition 28(7): 1011-1018 (1995) |
| 1994 |
|---|
| 6 | | Gábor Lugosi,
Miroslaw Pawlak:
On the posterior-probability estimate of the error rate of nonparametric classification rules.
IEEE Transactions on Information Theory 40(2): 475-481 (1994) |
| 5 | | 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 Transactions on Information Theory 40(6): 1728-1740 (1994) |
| 1993 |
|---|
| 4 | | Tamás Linder,
Gábor Lugosi,
Kenneth Zeger:
Universality and Rates of Convergence in Lossy Source Coding.
Data Compression Conference 1993: 89-97 |
| 3 | EE | András Faragó,
Tamás Linder,
Gábor Lugosi:
Fast Nearest-Neighbor Search in Dissimilarity Spaces.
IEEE Trans. Pattern Anal. Mach. Intell. 15(9): 957-962 (1993) |
| 2 | | András Faragó,
Gábor Lugosi:
Strong universal consistency of neural network classifiers.
IEEE Transactions on Information Theory 39(4): 1146-1151 (1993) |
| 1992 |
|---|
| 1 | EE | Gábor Lugosi:
Learning with an unreliable teacher.
Pattern Recognition 25(1): 79-87 (1992) |
