# Daniil Ryabko

According to our database

Collaborative distances:

^{1}, Daniil Ryabko authored at least 53 papers between 2004 and 2019.Collaborative distances:

## Timeline

#### Legend:

Book In proceedings Article PhD thesis Other## Links

#### On csauthors.net:

## Bibliography

2019

Asymptotic Nonparametric Statistical Analysis of Stationary Time Series

Springer Briefs in Computer Science, Springer, ISBN: 978-3-030-12563-9, 2019

2017

Independence clustering (without a matrix).

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Hypotheses testing on infinite random graphs.

Proceedings of the International Conference on Algorithmic Learning Theory, 2017

Universality of Bayesian mixture predictors.

Proceedings of the International Conference on Algorithmic Learning Theory, 2017

2016

Consistent Algorithms for Clustering Time Series.

Journal of Machine Learning Research, 2016

Things Bayes Can't Do.

Proceedings of the Algorithmic Learning Theory - 27th International Conference, 2016

2015

The replacement bootstrap for dependent data.

Proceedings of the IEEE International Symposium on Information Theory, 2015

Predicting the outcomes of every process for which an asymptotically accurate stationary predictor exists is impossible.

Proceedings of the IEEE International Symposium on Information Theory, 2015

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning.

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Asymptotically consistent estimation of the number of change points in highly dependent time series.

Proceedings of the 31th International Conference on Machine Learning, 2014

Selecting Near-Optimal Approximate State Representations in Reinforcement Learning.

Proceedings of the Algorithmic Learning Theory - 25th International Conference, 2014

2013

A binary-classification-based metric between time-series distributions and its use in statistical and learning problems.

Journal of Machine Learning Research, 2013

Time-series information and learning.

Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning.

Proceedings of the 30th International Conference on Machine Learning, 2013

Unsupervised Model-Free Representation Learning.

Proceedings of the Algorithmic Learning Theory - 24th International Conference, 2013

Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series.

Proceedings of the Algorithmic Learning Theory - 24th International Conference, 2013

Competing with an Infinite Set of Models in Reinforcement Learning.

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Online Clustering of Processes.

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Reducing statistical time-series problems to binary classification.

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Online Regret Bounds for Undiscounted Continuous Reinforcement Learning.

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Locating Changes in Highly Dependent Data with Unknown Number of Change Points.

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Regret Bounds for Restless Markov Bandits.

Proceedings of the Algorithmic Learning Theory - 23rd International Conference, 2012

2011

On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem.

Journal of Machine Learning Research, 2011

Constructing perfect steganographic systems.

Inf. Comput., 2011

Selecting the State-Representation in Reinforcement Learning.

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Confidence sets in time-series filtering.

Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

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

, 2011

2010

Nonparametric statistical inference for ergodic processes.

IEEE Trans. Information Theory, 2010

On Finding Predictors for Arbitrary Families of Processes.

Journal of Machine Learning Research, 2010

Clustering processes.

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Sequence Prediction in Realizable and Non-realizable Cases.

Proceedings of the COLT 2010, 2010

2009

Asymptotically optimal perfect steganographic systems.

Probl. Inf. Transm., 2009

Using data compressors to construct order tests for homogeneity and component independence.

Appl. Math. Lett., 2009

Characterizing predictable classes of processes.

Proceedings of the UAI 2009, 2009

Using Kolmogorov complexity for understanding some limitations on steganography.

Proceedings of the IEEE International Symposium on Information Theory, 2009

An impossibility result for process discrimination.

Proceedings of the IEEE International Symposium on Information Theory, 2009

Workshop summary: On-line learning with limited feedback.

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

On the possibility of learning in reactive environments with arbitrary dependence.

Theor. Comput. Sci., 2008

Predicting non-stationary processes.

Appl. Math. Lett., 2008

On hypotheses testing for ergodic processes.

Proceedings of the 2008 IEEE Information Theory Workshop, 2008

Some Sufficient Conditions on an Arbitrary Class of Stochastic Processes for the Existence of a Predictor.

Proceedings of the Algorithmic Learning Theory, 19th International Conference, 2008

2007

Sample Complexity for Computational Classification Problems.

Algorithmica, 2007

Information-Theoretic Approach to Steganographic Systems.

Proceedings of the IEEE International Symposium on Information Theory, 2007

On Sequence Prediction for Arbitrary Measures.

Proceedings of the IEEE International Symposium on Information Theory, 2007

Testing Component Independence Using Data Compressors.

Proceedings of the Artificial Neural Networks, 2007

2006

Pattern Recognition for Conditionally Independent Data.

Journal of Machine Learning Research, 2006

Provably Secure Universal Steganographic Systems.

IACR Cryptology ePrint Archive, 2006

Learning in Reactive Environments with Arbitrary Dependence.

Proceedings of the Kolmogorov Complexity and Applications, 29.01. - 03.02.2006, 2006

Sequence prediction for non-stationary processes.

Proceedings of the Combinatorial and Algorithmic Foundations of Pattern and Association Discovery, 14.05., 2006

Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence.

Proceedings of the Algorithmic Learning Theory, 17th International Conference, 2006

2005

On Computability of Pattern Recognition Problems.

Proceedings of the Algorithmic Learning Theory, 16th International Conference, 2005

2004

Online learning of conditionally I.I.D. data.

Proceedings of the Machine Learning, 2004

Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data.

Proceedings of the Algorithmic Learning Theory, 15th International Conference, 2004