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Daniil Ryabko
2010 – today
- 2013
[i27]Ronald Ortner, Daniil Ryabko: Online Regret Bounds for Undiscounted Continuous Reinforcement Learning. CoRR abs/1302.2550 (2013)- [i26]Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko: Selecting the State-Representation in Reinforcement Learning. CoRR abs/1302.2552 (2013)
- [i25]Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko: Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. CoRR abs/1302.2553 (2013)
- [i24]Azadeh Khaleghi, Daniil Ryabko: A consistent clustering-based approach to estimating the number of change-points in highly dependent time-series. CoRR abs/1302.3407 (2013)
- [i23]
- 2012
- [j10]Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary, Philippe Preux: Online Clustering of Processes. Journal of Machine Learning Research - Proceedings Track 22: 601-609 (2012)
- [c20]Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos: Regret Bounds for Restless Markov Bandits. ALT 2012: 214-228
- [c19]Ronald Ortner, Daniil Ryabko: Online Regret Bounds for Undiscounted Continuous Reinforcement Learning. NIPS 2012: 1772-1780
- [c18]Daniil Ryabko, Jérémie Mary: Reducing statistical time-series problems to binary classification. NIPS 2012: 2069-2077
- [c17]Azadeh Khaleghi, Daniil Ryabko: Locating Changes in Highly Dependent Data with Unknown Number of Change Points. NIPS 2012: 3095-3103
- [i22]Azadeh Khaleghi, Daniil Ryabko: Multiple Change-Point Estimation in Stationary Ergodic Time-Series. CoRR abs/1203.1515 (2012)
- [i21]Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos: Regret Bounds for Restless Markov Bandits. CoRR abs/1209.2693 (2012)
- [i20]Daniil Ryabko, Jérémie Mary: Reducing statistical time-series problems to binary classification. CoRR abs/1210.6001 (2012)
- 2011
- [j9]Boris Ryabko, Daniil Ryabko: Constructing perfect steganographic systems. Inf. Comput. 209(9): 1223-1230 (2011)
- [j8]Daniil Ryabko: On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem. Journal of Machine Learning Research 12: 2161-2180 (2011)
- [c16]
- [c15]Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko: Selecting the State-Representation in Reinforcement Learning. NIPS 2011: 2627-2635
- 2010
- [j7]Daniil Ryabko: On Finding Predictors for Arbitrary Families of Processes. Journal of Machine Learning Research 11: 581-602 (2010)
- [j6]Daniil Ryabko, Boris Ryabko: Nonparametric statistical inference for ergodic processes. IEEE Transactions on Information Theory 56(3): 1430-1435 (2010)
- [c14]
- [c13]
- [i19]
- [i18]
- [i17]Daniil Ryabko: Sequence prediction in realizable and non-realizable cases. CoRR abs/1005.5603 (2010)
- [i16]
2000 – 2009
- 2009
- [j5]Daniil Ryabko, Jürgen Schmidhuber: Using data compressors to construct order tests for homogeneity and component independence. Appl. Math. Lett. 22(7): 1029-1032 (2009)
- [c12]Jean-Yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko, Csaba Szepesvári: Workshop summary: On-line learning with limited feedback. ICML 2009: 168
- [c11]
- [c10]Boris Ryabko, Daniil Ryabko: Using Kolmogorov complexity for understanding some limitations on steganography. ISIT 2009: 2733-2736
- [c9]
- [i15]Boris Ryabko, Daniil Ryabko: Using Kolmogorov Complexity for Understanding Some Limitations on Steganography. CoRR abs/0901.4023 (2009)
- [i14]
- [i13]Daniil Ryabko: A criterion for hypothesis testing for stationary processes. CoRR abs/0905.4937 (2009)
- [i12]
- 2008
- [j4]Daniil Ryabko, Marcus Hutter: Predicting non-stationary processes. Appl. Math. Lett. 21(5): 477-482 (2008)
- [j3]Daniil Ryabko, Marcus Hutter: On the possibility of learning in reactive environments with arbitrary dependence. Theor. Comput. Sci. 405(3): 274-284 (2008)
- [c8]Daniil Ryabko: Some Sufficient Conditions on an Arbitrary Class of Stochastic Processes for the Existence of a Predictor. ALT 2008: 169-182
- [e1]Sertan Girgin, Manuel Loth, Rémi Munos, Philippe Preux, Daniil Ryabko (Eds.): Recent Advances in Reinforcement Learning, 8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30 - July 3, 2008, Revised and Selected Papers. Lecture Notes in Computer Science 5323, Springer 2008, ISBN 978-3-540-89721-7
- [i11]Daniil Ryabko, Boris Ryabko: Testing Statistical Hypotheses About Ergodic Processes. CoRR abs/0804.0510 (2008)
- [i10]
- [i9]
- [i8]Daniil Ryabko, Marcus Hutter: On the Possibility of Learning in Reactive Environments with Arbitrary Dependence. CoRR abs/0810.5636 (2008)
- 2007
- [j2]Daniil Ryabko: Sample Complexity for Computational Classification Problems. Algorithmica 49(1): 69-77 (2007)
- [c7]
- [i7]Daniil Ryabko, Jürgen Schmidhuber: Using Data Compressors to Construct Rank Tests. CoRR abs/0709.0670 (2007)
- 2006
- [j1]Daniil Ryabko: Pattern Recognition for Conditionally Independent Data. Journal of Machine Learning Research 7: 645-664 (2006)
- [c6]Daniil Ryabko, Marcus Hutter: Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence. ALT 2006: 334-347
- [c5]Daniil Ryabko, Marcus Hutter: Sequence prediction for non-stationary processes. Combinatorial and Algorithmic Foundations of Pattern and Association Discovery 2006
- [c4]Daniil Ryabko, Marcus Hutter: Learning in Reactive Environments with Arbitrary Dependence. Kolmogorov Complexity and Applications 2006
- [i6]Daniil Ryabko, Marcus Hutter: Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence. CoRR abs/cs/0603110 (2006)
- [i5]Daniil Ryabko, Marcus Hutter: On Sequence Prediction for Arbitrary Measures. CoRR abs/cs/0606077 (2006)
- [i4]Boris Ryabko, Daniil Ryabko: Provably Secure Universal Steganographic Systems. CoRR abs/cs/0606085 (2006)
- [i3]Boris Ryabko, Daniil Ryabko: Provably Secure Universal Steganographic Systems. IACR Cryptology ePrint Archive 2006: 63 (2006)
- 2005
- [c3]
- [i2]Daniil Ryabko: On sample complexity for computational pattern recognition. CoRR abs/cs/0502074 (2005)
- [i1]
- 2004
- [c2]Daniil Ryabko: Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data. ALT 2004: 171-180
- [c1]
Coauthor Index
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last updated on 2013-05-03 21:41 CEST by the dblp team