Book Chapter
 Y.Kalnishkan. Predictive Complexity for Games with Finite Outcome
Spaces. In Measures of Complexity:
Festschrift for Alexey Chervonenkis, pp. 117139, Springer,
2015. DOI,
Pure.
In Journals
 Y.Kalnishkan. Prediction with Expert Advice for a Finite Number of Experts: A Practical Introduction. Pattern Recognition (2022): 108557. DOI, Pure
 R.Dzhamtyrova and Y.Kalnishkan. Universal algorithms for
multinomial logistic regression under Kullback–Leibler
game. Neurocomputing, 397 (2020): 369380. DOI, Pure
 D.Adamskiy, A.Bellotti, R.Dzhamtyrova, and
Y.Kalnishkan. Aggregating Algorithm for prediction of
packs. Machine Learning, 108, 12311260
(2019). DOI, Pure
 T.Scarfe, W.Koolen, and Y.Kalnishkan. Segmentation of electronic
dance music. International Journal of Engineering Intelligent
Systems for Electrical Engineering and Communications. 22, 3/4
(2014).
Pure.
 Y.Kalnishkan, V.Vyugin, and V.Vovk. Generalised Entropies and Asymptotic Complexities of Languages. Information and Computation, 237, 101141 (2014). DOI, Pure.
 F.Zhdanov and Y.Kalnishkan. An Identity for Kernel Ridge
Regression. Theoretical Computer Science, 473, 157178
(2013). DOI, Pure.
 F.Zhdanov and Y.Kalnishkan. Universal Algorithms for Probability
Forecasting. International Journal on Artificial Intelligence
Tools, 21(4)
(2012). DOI, Pure.
 A.Chernov, Y.Kalnishkan, F.Zhdanov, and V.Vovk. Supermartingales
in Prediction with Expert Advice. Theoretical Computer Science,
411(2930):
26472669 (2010).
DOI,
Pure. See also the arXiv.org version.
 Y.Kalnishkan and M.V.Vyugin. The weak
aggregating algorithm and weak mixability. Journal of Computer
and System Sciences, 74(8): 12281244 (2008).
DOI,
Pure.
 Y.Kalnishkan, V.Vovk, and M.V.Vyugin. How
many strings are easy to predict? Information and
Computation, 201: 5571
(2005). DOI.
 Y.Kalnishkan, V.Vovk, and M.V.Vyugin. Loss
functions, complexities, and the Legendre transformation.
Theoretical Computer Science, 313(2): 195207,
(2004). DOI.
 Y.Kalnishkan. General linear relations
among different types of predictive complexity. Theoretical
Computer Science, 271(12): 181200, (2002).
DOI.

In Refereed Conference Proceedings
 R.Dzhamtyrova and Y.Kalnishkan. A lower bound for a prediction algorithm under the KullbackLeibler game. In Conformal and Probabilistic Prediction and Applications, pp. 3951. PMLR, 2021. Pure, PMLR.
 R.Dzhamtyrova and Y.Kalnishkan. Prediction with Expert Advice
for Value at Risk. In Proceedings of The 2020 International Joint
Conference on Neural Networks (IJCNN 2020.), IEEE, 2020. Pure
 R.Dzhamtyrova and Y.Kalnishkan. Competitive online quantile
regression. In Information Processing and Management of Uncertainty in KnowledgeBased Systems, Proceedings of the 18th International Conference IPMU 2020, Springer, 2020. Pure
 N. AlBaghdadi, W.Wisniewski, D.Lindsay, S.Lindsay, Y.Kalnishkan, and
C.Watkins, Structuring Time Series Data to Gain Insight into Agent
Behaviour, Proceedings of the 3rd International Workshop on Big Data
for Financial News and Data, IEEE,
2019. DOI, Pure
 R.Dzhamtyrova and Y.Kalnishkan. Competitive Online Regression under
Continuous Ranked Probability Score, In Proceedings of Machine Learning
Research, Vol.105., p. 178195, Conformal and Probabilistic Prediction
and Applications, Golden Sands,
Bulgaria, 2019. PMLR, Pure
 R.Dzhamtyrova and Y.Kalnishkan. Competitive Online Generalised
Linear Regression with Multidimensional Outputs. In 2019
International Joint Conference on Neural Networks (IJCNN),
Budapest, Hungary,
2019. Pure, DOI
 M.Bijelic, C.Muench, W.Ritter, Y.Kalnishkan, and
K.Dietmayer, Robustness Against Unknown Noise for Raw Data Fusing
Neural Networks. In Proceedings of 21st IEEE International Conference
on Intelligent Transportation Systems (IEEE ITSC 2018), IEEE Xplore.
Pure, DOI
 Y.Kalnishkan. An Upper Bound for Aggregating Algorithm for
Regression with Changing Dependencies. In Proceedings of the
International Conference on Algorithmic Learning Theory
(pp. 238252). Springer,
2016. Pure, DOI
 Y.Kalnishkan, D.Adamskiy, A.Chernov, and T.Scarfe,
Specialist Experts for Prediction with Side Information. In
2015 IEEE International Conference on Data Mining Workshop
(ICDMW), pp.14701477, IEEE,
2015. Pure, DOI
 T.Scarfe, W.Koolen, and Y.Kalnishkan. A longrange selfsimilarity approach to segmenting DJ mixed music streams. In Artificial Intelligence Applications and Innovations: Proceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, Springer, 2013. p. 235244.
DOI, Pure.
 F.Zhdanov and Y.Kalnishkan. An
Identity for Kernel Ridge Regression. In Algorithmic Learning
Theory 21st International Conference, ALT 2010, Proceedings,
volume 6331 of Lecture Notes in Computer Science, pages
405419. Springer, 2010.
DOI.
 F.Zhdanov and Y.Kalnishkan. Linear Probability
Forecasting. In Artificial Intelligence Applications and
Innovations, AIAI 2010, Proceedings, volume 339 of IFIP
Advances in Information and Communication Technology, pages
411. Springer, 2010.
DOI.
 A.Chernov, Y.Kalnishkan, F.Zhdanov, and V.Vovk. Supermartingales
in Prediction with Expert Advice. In
Algorithmic Learning Theory, 19th International
Conference, ALT 2008, Proceedings, volume 5254 of Lecture
Notes in Computer Science, pages 199213. Springer, 2008.
DOI.
 S.Busuttil and Y.Kalnishkan. Online
Regression Competitive with Changing Predictors. In Algorithmic
Learning Theory, 18th International Conference, ALT 2007,
Proceedings, volume 4754 of
Lecture Notes in Computer Science, pages 181195. Springer,
2007. DOI.
 S.Busuttil and Y.Kalnishkan. Weighted Kernel
Regression for Predicting Changing Dependencies. In Machine
Learning: ECML 2007, 18th European Conference on Machine Learning,
volume 4701 of Lecture Notes in Computer Science, pages
535542. Springer,
2007. DOI.
 Y.Kalnishkan, V.Vovk and
M.V.Vyugin. Generalised Entropy and Asymptotic
Complexities of Languages. In Learning Theory, 20th Annual
Conference on Learning Theory, COLT 2007, volume 4539
of Lecture Notes in Computer Science, pages 293307, Springer
2007. DOI.
The statement of the main theorem in this version of the paper is
inaccurate. See the journal version from Information and Computation,
2014 for the correct theorem.
 Y.Kalnishkan and M.V.Vyugin. The Weak
Aggregating Algorithm and Weak Mixability. In
Learning Theory, Proceedings of the 18th Annual Conference (COLT
2005), volume 3559 of Lecture Notes in Artificial
Intelligence, Springer, 2005.
DOI.
 Y.Kalnishkan, V.Vovk, and M.V. Vyugin. A
Criterion for the Existence of Predictive Complexity for Binary
Games. In Algorithmic Learning Theory, 15th International
Conference, ALT 2004, Proceedings, volume 3244 of Lecture Notes
in Artificial Intelligence, pages 249263. Springer, 2004.
DOI.
 A.Gammerman, Y.Kalnishkan, and
V.Vovk. Online Prediction with Kernels and the
Complexity Approximation Principle. In Uncertainty in
Artificial Intelligence, Proceedings of the Twentieth Conference,
pages 170176. AUAI Press, 2004.
ACM
Digital Library.
 Y.Kalnishkan, V.Vovk and M.V.Vyugin. How Many Strings Are Easy to
Predict? In 16th Annual Conference on Learning Theory (COLT) and
7th Annual Workshop on Kernel Machines, Proceedings, volume 2777
of Lecture Notes in Artificial Intelligence, SpringerVerlag,
2003. DOI.
 Y.Kalnishkan and M.V.Vyugin. On the Absence
of Predictive Complexity for Some Games. In Algorithmic
Learning Theory 13th International Conference, ALT 2002,
Proceedings,, volume 2533 of Lecture Notes in Artificial
Intelligence, SpringerVerlag, 2002.
DOI.
 Y.Kalnishkan and M.V.Vyugin. Mixability
and the Existence of Weak Complexities. In Computational
Learning Theory, 15th Annual Conference on Computational Learning
Theory, COLT 2002, Proceedings, volume 2375 of Lecture Notes in
Artificial Intelligence, pages 105120. Springer, 2002.
DOI.
 Y.Kalnishkan, M.V.Vyugin and V.Vovk. Loss Functions, Complexities,
and the Legendre Transformation. In Algorithmic Learning Theory
12th International Conference, ALT 2001, Proceedings, volume 2225
of Lecture Notes in Artificial Intelligence, pages
181189. SpringerVerlag, 2001.
DOI.
 Y.Kalnishkan. Complexity Approximation Principle and
Rissanen's Approach to RealValued Parameters. In Machine
Learning: ECML 2000, 11th European Conference on Machine Learning,
Proceedings, volume 1810 of Lecture Notes in Artificial
Intelligence, pages 203210, SpringerVerlag, 2000.
DOI.
 Y.Kalnishkan. General Linear Relations among Different Types of
Predictive Complexity. In Algorithmic Learning Theory, 10th
International Conference, ALT'99, Proceedings pages 323334,
volume 1720 of Lecture Notes in Artificial Intelligence,
SpringerVerlag,
1999. DOI.
 Y.Kalnishkan. Linear Relations between SquareLoss and Kolmogorov
Complexity. In Proceedings of the Twelfth Annual Conference
on Computation Learning Theory, pages 226232. Association
for Computing Machinery, 1999.
