Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data. - Daniil Ryabko (ALT, 2004)

Online learning of conditionally I.I.D. data. - Daniil Ryabko (ICML, 2004)

On Computability of Pattern Recognition Problems. - Daniil Ryabko (ALT, 2005)

On sample complexity for computational pattern recognition - Daniil Ryabko (CoRR, 2005)

Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence. - Daniil Ryabko; Marcus Hutter (ALT, 2006)

Sequence prediction for non-stationary processes. - Daniil Ryabko; Marcus Hutter (Combinatorial and Algorithmic Foundations of Pattern and Association Discovery, 2006)

Learning in Reactive Environments with Arbitrary Dependence. - Daniil Ryabko; Marcus Hutter (Kolmogorov Complexity and Applications, 2006)

Provably Secure Universal Steganographic Systems. - Boris Ryabko; Daniil Ryabko (IACR Cryptol. ePrint Arch., 2006)

Pattern Recognition for Conditionally Independent Data. - Daniil Ryabko (J. Mach. Learn. Res., 2006)

Testing Component Independence Using Data Compressors. - Daniil Ryabko (ICANN (2), 2007)

On Sequence Prediction for Arbitrary Measures. - Daniil Ryabko; Marcus Hutter (ISIT, 2007)

Information-Theoretic Approach to Steganographic Systems. - Boris Ryabko; Daniil Ryabko (ISIT, 2007)

Sample Complexity for Computational Classification Problems. - Daniil Ryabko (Algorithmica, 2007)

Using Data Compressors to Construct Rank Tests - Daniil Ryabko; Jürgen Schmidhuber (CoRR, 2007)

Some Sufficient Conditions on an Arbitrary Class of Stochastic Processes for the Existence of a Predictor. - Daniil Ryabko (ALT, 2008)

On hypotheses testing for ergodic processes. - Daniil Ryabko; Boris Ryabko (ITW, 2008)

Predicting non-stationary processes. - Daniil Ryabko; Marcus Hutter (Appl. Math. Lett., 2008)

Testing Statistical Hypotheses About Ergodic Processes - Daniil Ryabko; Boris Ryabko (CoRR, 2008)

On the possibility of learning in reactive environments with arbitrary dependence. - Daniil Ryabko; Marcus Hutter (Theor. Comput. Sci., 2008)

Workshop summary: On-line learning with limited feedback. - Jean-Yves Audibert; Peter Auer; Alessandro Lazaric; Rémi Munos; Daniil Ryabko; Csaba Szepesvári (ICML, 2009)

An impossibility result for process discrimination. - Daniil Ryabko (ISIT, 2009)

Using Kolmogorov complexity for understanding some limitations on steganography. - Boris Ryabko; Daniil Ryabko (ISIT, 2009)

Characterizing predictable classes of processes. - Daniil Ryabko (UAI, 2009)

Using data compressors to construct order tests for homogeneity and component independence. - Daniil Ryabko; Jürgen Schmidhuber (Appl. Math. Lett., 2009)

A criterion for hypothesis testing for stationary processes - Daniil Ryabko (CoRR, 2009)

Asymptotically optimal perfect steganographic systems. - B. Ya. Ryabko; Daniil Ryabko (Probl. Inf. Transm., 2009)

Sequence Prediction in Realizable and Non-realizable Cases. - Daniil Ryabko (COLT, 2010)

Clustering processes. - Daniil Ryabko (ICML, 2010)

On Finding Predictors for Arbitrary Families of Processes. - Daniil Ryabko (J. Mach. Learn. Res., 2010)

Nonparametric statistical inference for ergodic processes. - Daniil Ryabko; Boris Ryabko (IEEE Trans. Inf. Theory, 2010)

Learnability in Problems of Sequential Inference. (Apprenabilité dans les ProblèMES de l'inféRence SéQuentielle). - Daniil Ryabko (2011)

Confidence sets in time-series filtering. - Boris Ryabko; Daniil Ryabko (ISIT, 2011)

Selecting the State-Representation in Reinforcement Learning. - Odalric-Ambrym Maillard; Rémi Munos; Daniil Ryabko (NIPS, 2011)

Constructing perfect steganographic systems. - Boris Ryabko; Daniil Ryabko (Inf. Comput., 2011)

On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem. - Daniil Ryabko (J. Mach. Learn. Res., 2011)

Locating Changes in Highly Dependent Data with Unknown Number of Change Points. - Azadeh Khaleghi; Daniil Ryabko (NIPS, 2012)

Online Regret Bounds for Undiscounted Continuous Reinforcement Learning. - Ronald Ortner; Daniil Ryabko (NIPS, 2012)

Reducing statistical time-series problems to binary classification. - Daniil Ryabko; Jérémie Mary (NIPS, 2012)

Multiple Change-Point Estimation in Stationary Ergodic Time-Series - Azadeh Khaleghi; Daniil Ryabko (CoRR, 2012)

Online Clustering of Processes. - Azadeh Khaleghi; Daniil Ryabko; Jérémie Mary; Philippe Preux (AISTATS, 2012)

Competing with an Infinite Set of Models in Reinforcement Learning. - Phuong Nguyen; Odalric-Ambrym Maillard; Daniil Ryabko; Ronald Ortner (AISTATS, 2013)

Unsupervised Model-Free Representation Learning. - Daniil Ryabko (ALT, 2013)

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. - Odalric-Ambrym Maillard; Phuong Nguyen; Ronald Ortner; Daniil Ryabko (ICML (1), 2013)

Time-series information and learning. - Daniil Ryabko (ISIT, 2013)

A consistent clustering-based approach to estimating the number of change-points in highly dependent time-series - Azadeh Khaleghi; Daniil Ryabko (CoRR, 2013)

A binary-classification-based metric between time-series distributions and its use in statistical and learning problems. - Daniil Ryabko; Jérémie Mary (J. Mach. Learn. Res., 2013)

Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. - Ronald Ortner; Odalric-Ambrym Maillard; Daniil Ryabko (ALT, 2014)

Asymptotically consistent estimation of the number of change points in highly dependent time series. - Azadeh Khaleghi; Daniil Ryabko (ICML, 2014)

Regret bounds for restless Markov bandits. - Ronald Ortner; Daniil Ryabko; Peter Auer; Rémi Munos (Theor. Comput. Sci., 2014)

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning. - K. Lakshmanan 002; Ronald Ortner; Daniil Ryabko (ICML, 2015)

Predicting the outcomes of every process for which an asymptotically accurate stationary predictor exists is impossible. - Daniil Ryabko; Boris Ryabko (ISIT, 2015)

The replacement bootstrap for dependent data. - Amir Sani; Alessandro Lazaric; Daniil Ryabko (ISIT, 2015)

Things Bayes Can't Do. - Daniil Ryabko (ALT, 2016)

Universality of Baysian mixture predictors. - Daniil Ryabko (CoRR, 2016)

Consistent Algorithms for Clustering Time Series. - Azadeh Khaleghi; Daniil Ryabko; Jérémie Mary; Philippe Preux (J. Mach. Learn. Res., 2016)

Nonparametric multiple change point estimation in highly dependent time series. - Azadeh Khaleghi; Daniil Ryabko (Theor. Comput. Sci., 2016)

Universality of Bayesian mixture predictors. - Daniil Ryabko (ALT, 2017)

Hypotheses testing on infinite random graphs. - Daniil Ryabko (ALT, 2017)

Independence clustering (without a matrix). - Daniil Ryabko (NIPS, 2017)

Finite-time optimality of Bayesian predictors. - Daniil Ryabko (CoRR, 2018)

Asymptotic nonparametric statistical analysis of stationary time series. - Daniil Ryabko (CoRR, 2019)

On Asymptotic and Finite-Time Optimality of Bayesian Predictors. - Daniil Ryabko (J. Mach. Learn. Res., 2019)

Asymptotic Nonparametric Statistical Analysis of Stationary Time Series - Daniil Ryabko (Springer, 2019)

Clustering piecewise stationary processes. - Azadeh Khaleghi; Daniil Ryabko (ISIT, 2020)

Universal time-series forecasting with mixture predictors. - Daniil Ryabko (CoRR, 2020)

Time-Series Information and Unsupervised Learning of Representations. - Daniil Ryabko (IEEE Trans. Inf. Theory, 2020)

Universal Time-Series Forecasting with Mixture Predictors - Daniil Ryabko (Springer, 2020)