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ORCIDAndrás Antos
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2020 – today
- 2024
[c9]András Millinghoffer, Mátyás Antal, Márk Marosi, András Formanek
, András Antos, Péter Antal:
Boosting Multitask Decomposition: Directness, Sequentiality, Subsampling, Cross-Gradients. AIME (1) 2024: 17-35
2010 – 2019
- 2015
- [j12]Alexandra Carpentier, Rémi Munos, András Antos:
Adaptive strategy for stratified Monte Carlo sampling. J. Mach. Learn. Res. 16: 2231-2271 (2015) - [i3]Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Peter Auer, András Antos:
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits. CoRR abs/1507.04523 (2015) - 2014
- [j11]Gergely Neu, András György, Csaba Szepesvári, András Antos:
Online Markov Decision Processes Under Bandit Feedback. IEEE Trans. Autom. Control. 59(3): 676-691 (2014) - 2013
- [j10]András Antos, Gábor Bartók, Dávid Pál, Csaba Szepesvári:
Toward a classification of finite partial-monitoring games. Theor. Comput. Sci. 473: 77-99 (2013) - 2012
- [j9]András Antos:
On Codecell Convexity of Optimal Multiresolution Scalar Quantizers for Continuous Sources. IEEE Trans. Inf. Theory 58(2): 1147-1157 (2012) - 2011
- [i2]András Antos, Gábor Bartók, Dávid Pál, Csaba Szepesvári:
Toward a Classification of Finite Partial-Monitoring Games. CoRR abs/1102.2041 (2011) - [i1]András Antos, Gábor Bartók, Csaba Szepesvári:
Non-trivial two-armed partial-monitoring games are bandits. CoRR abs/1108.4961 (2011) - 2010
- [j8]András Antos, Varun Grover, Csaba Szepesvári:
Active learning in heteroscedastic noise. Theor. Comput. Sci. 411(29-30): 2712-2728 (2010) - [c8]Gergely Neu, András György, Csaba Szepesvári, András Antos:
Online Markov Decision Processes under Bandit Feedback. NIPS 2010: 1804-1812
2000 – 2009
- 2008
- [j7]András Antos, Csaba Szepesvári, Rémi Munos:
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path. Mach. Learn. 71(1): 89-129 (2008) - [c7]András Antos, Varun Grover, Csaba Szepesvári:
Active Learning in Multi-armed Bandits. ALT 2008: 287-302 - 2007
- [c6]András Antos, Rémi Munos, Csaba Szepesvári:
Fitted Q-iteration in continuous action-space MDPs. NIPS 2007: 9-16 - 2006
- [c5]András Antos, Csaba Szepesvári, Rémi Munos:
Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path. COLT 2006: 574-588 - 2005
- [j6]András Antos, László Györfi, András György:
Individual convergence rates in empirical vector quantizer design. IEEE Trans. Inf. Theory 51(11): 4013-4022 (2005) - [j5]András Antos:
Improved minimax bounds on the test and training distortion of empirically designed vector quantizers. IEEE Trans. Inf. Theory 51(11): 4022-4032 (2005) - [c4]András Antos:
Improved Minimax Bounds on the Test and Training Distortion of Empirically Designed Vector Quantizers. COLT 2005: 531-544 - 2004
- [c3]András Antos, László Györfi, András György:
Improved convergence rates in empirical vector quantizer design. ISIT 2004: 301 - 2002
- [j4]András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi:
Data-dependent margin-based generalization bounds for classification. J. Mach. Learn. Res. 3: 73-98 (2002) - [j3]András Antos:
Lower bounds for the rate of convergence in nonparametric pattern recognition. Theor. Comput. Sci. 284(1): 3-24 (2002) - 2000
- [b1]András Antos:
Performance limits of nonparametric estimators. Budapest University of Technology and Economics, Hungary, 2000
1990 – 1999
- 1999
- [j2]András Antos, Luc Devroye, László Györfi:
Lower Bounds for Bayes Error Estimation. IEEE Trans. Pattern Anal. Mach. Intell. 21(7): 643-645 (1999) - [c2]András Antos:
Lower Bounds on the Rate of Convergence of Nonparametric Pattern Recognition. EuroCOLT 1999: 241-252 - 1998
- [j1]András Antos, Gábor Lugosi:
Strong Minimax Lower Bounds for Learning. Mach. Learn. 30(1): 31-56 (1998) - 1996
- [c1]András Antos, Gábor Lugosi:
Strong Minimax Lower Bounds for Learning. COLT 1996: 303-309
Coauthor Index
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