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Notice
The paper versions given here are the most current, arxiv and other sources notwithstanding.
Errata may be found in the follow-up notes (when these are posted).
Please observe all copyright laws.

Preprints

Journals

  1. H. Pratt, A. Polyakov, A. Kontorovich. Evidence for Separate Processing in the Human Brainstem of Interaural Intensity and Temporal Disparities for Sound Lateralization. Hearing Research 108, 1-8, 1997.
  2. A. Kontorovich. Uniquely Decodable n-gram Embeddings. Theoretical Computer Science 329, 271-284, 2004.
  3. A. Kontorovich, K. Ramanan. Concentration Inequalities for Dependent Random Variables via the Martingale Method. Annals of Probability 36(6), 2126-2158, 2008.
  4. A. Kontorovich. Constructing processes with prescribed mixing coefficients. Statistics and Probability Letters 78, 2910-2915, 2008.
  5. A. Kontorovich, C. Cortes, M. Mohri. Kernel Methods for Learning Languages. Invited to Theoretical Computer Science 405, 223-236, 2008. [follow-up notes]
  6. A. Kontorovich, B. Nadler. Universal Kernel-Based Learning with Applications to Regular Languages. Journal of Machine Learning Research 10, 997-1031, 2009.
  7. B. Nadler, A. Kontorovich. Model Selection for Sinusoids in Noise: Statistical Analysis and a New Penalty Term. IEEE Transactions on Signal Processing 59(4), 1333-1345, 2010.
  8. A. Kontorovich. Statistical estimation with bounded memory. Statistics and Computing 22(5), 1155-1164, 2012. [follow-up notes]
  9. L. Gottlieb, A. Kontorovich, E. Mossel. VC bounds on the cardinality of nearly orthogonal function classes. Discrete Mathematics 312(10), 1766-1775, 2012.
  10. D. Berend, A. Kontorovich. The Missing Mass Problem. Statistics and Probability Letters 82(6), 1102-1110, 2012.
  11. A. Kontorovich. Obtaining Measure Concentration from Markov Contraction. Markov Processes and Related Fields 18, 613–638, 2012.
  12. A. Kontorovich, A. Brockwell. A Strong Law of Large Numbers for Strongly Mixing Processes. Communications in Statistics – Theory and Methods 43(18), pp. 3777-3796, 2014.
  13. L. Chekina, D. Gutfreund, A. Kontorovich, L. Rokach, B. Shapira. Exploiting Label Dependencies for Improved Sample Complexity. Machine Learning 1-42, 2012.
  14. A. Kontorovich. An Explicit Bound on the Transportation Cost Distance. Communications in Mathematical Analysis 14(1), 1-14, 2013.
  15. A. Kontorovich. An inequality involving the $ell_1$, $ell_2$ and $ell_infty$ norms. Analysis and Applications 11(6), 2013.
  16. D. Berend, A. Kontorovich. On the Concentration of the Missing Mass. Electronic Communications in Probability 18(3), 1-7, 2013.
  17. D. Berend, A. Kontorovich. A Sharp Estimate of the Binomial Mean Absolute Deviation with Applications. Statistics and Probability Letters 83(4), 1254-1259, 2013.
  18. T. Becker, A. Greaves-Tunnell, A. Kontorovich, S. J. Miller, K. Shen. Virus Dynamics on Starlike Graphs. Journal of Nonlinear Systems and Applications 4(1), 53-63, 2013.
  19. D. Angluin, J. Aspnes, S. Eisenstat, A. Kontorovich. On the Learnability of Shuffle Ideals. Journal of Machine Learning Research 14, 15131531, 2013.
  20. A. Kontorovich, A. Trachtenberg. Deciding unique decodability of bigram counts via finite automata. Journal of Computer and System Sciences 80(2), 450–456, 2014.
  21. D. Berend, P. Harremoës, A. Kontorovich. Minimum KL-divergence on complements of $L_1$ balls. IEEE Transactions on Information Theory 60(6), 3172-3177, 2014. [Former title: "A Reverse Pinsker Inequality"]
  22. A. Kontorovich, Roi Weiss. Uniform Chernoff and Dvoretzky-Kiefer-Wolfowitz-type inequalities for Markov chains and related processes. Journal of Applied Probability 51, 1-14, 2014. follow-up notes
  23. O. Asor, H. Duan, A. Kontorovich. On the Additive Properties of the Fat-Shattering Dimension. IEEE Transactions on Neural Networks and Learning Systems 25(12), 2309-2312, 2014.
  24. L. Gottlieb, A. Kontorovich, R. Krauthgamer. Efficient classification for metric data. IEEE Transactions on Information Theory 60(9), 5750-5759, 2014.
  25. D. Berend, A. Kontorovich. A finite sample analysis of the Naive Bayes classifier, Journal of Machine Learning Research 16, 1519-1545, 2015.
  26. D. Gordon, D. Hendler, A. Kontorovich, L. Rokach. Local-shapelets for fast classification of spectrographic measurements, Expert Systems with Applications 42, 3150-–3158, 2015.
  27. D. Gutfreund, A. Kontorovich, R. Levy, M. Rosen-Zvi. Boosting Conditional Probability Estimators. Annals of Mathematics and Artificial Intelligence 1-16, 2015.
  28. L. Gottlieb, A. Kontorovich, R. Krauthgamer. Adaptive Metric Dimensionality Reduction. Invited to Theoretical Computer Science, 105-118, 2016.
  29. D. Berend, A. Kontorovich. The state complexity of random DFAs, Theoretical Computer Science, 102-108, 2016.
  30. L. Gottlieb, A. Kontorovich, P. Nisnevitch. Nearly optimal classification for semimetrics. Journal of Machine Learning Research 18(37):1−22, 2017.
  31. L. Gottlieb, A. Kontorovich, R. Krauthgamer. Efficient Regression in Metric Spaces via Approximate Lipschitz Extension. Accepted to IEEE Transactions on Information Theory 2017+.

Refereed Conferences and Workshops

  1. A. Kontorovich, C. Cortes, M. Mohri. Learning Linearly Separable Languages. In ALT 2006. [follow-up notes]
  2. C. Cortes, A. Kontorovich, M. Mohri. Learning Languages with Rational Kernels. In COLT 2007.
  3. A. Kontorovich. A Universal Kernel for Learning Regular Languages. In MLG 2007 (distinguished contribution award). [watch video]
  4. D. Angluin, D. Eisenstat, A. Kontorovich, L. Reyzin. Lower Bounds on Learning Random Structures with Statistical Queries. In ALT 2010.
  5. L. Gottlieb, A. Kontorovich, R. Krauthgamer. Efficient classification for metric data. In COLT 2010.
  6. A. Kontorovich, D. Hendler, E. Menahem. Metric Anomaly Detection Via Asymmetric Risk Minimization. In SIMBAD 2011.
  7. A. Kontorovich, A. Trachtenberg. String reconciliation with unknown edit distance. In ISIT 2012.
  8. D. Angluin, J. Aspnes, A. Kontorovich. On the Learnability of Shuffle Ideals. In ALT 2012.
  9. L. Gottlieb, A. Kontorovich, R. Krauthgamer. Efficient Regression in Metric Spaces via Approximate Lipschitz Extension. In SIMBAD 2013.
  10. A. Filtser, J. Jin, A. Kontorovich, A. Trachtenberg. Efficient determination of the unique decodability of a string. In ISIT 2013.
  11. A. Kontorovich, B. Nadler, R. Weiss. On learning parametric-output HMMs. In ICML 2013.
  12. L. Gottlieb, A. Kontorovich, R. Krauthgamer. Adaptive Metric Dimensionality Reduction. In ALT 2013.
  13. C. R. Shalizi, A. Kontorovich. Predictive PAC Learning and Process Decompositions. In NIPS 2013.
  14. D. Gutfreund, A. Kontorovich, R. Levy, M. Rosen-Zvi. Boosting Conditional Probability Estimators. Invited to ISAIM Theory of Machine Learning Special Session, 2014.
  15. A. Kontorovich. Concentration in unbounded metric spaces and algorithmic stability, ICML 2014. follow-up notes
  16. A. Kontorovich, Roi Weiss. Maximum Margin Multiclass Nearest Neighbors, ICML 2014.
  17. D. Berend, A. Kontorovich. Consistency of weighted majority votes, NIPS 2014.
  18. L. Gottlieb, A. Kontorovich, P. Nisnevitch. Near-optimal sample compression for nearest neighbors, NIPS 2014.
  19. A. Kontorovich, Roi Weiss. A Bayes consistent 1-NN classifier. AISTATS 2015.
  20. D. Hsu, A. Kontorovich, C. Szepesvári. Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. NIPS 2015.
  21. L. Gottlieb, A. Kontorovich, P. Nisnevitch. Nearly optimal classification for semimetrics. AISTATS (Oral) 2016.
  22. A. Kontorovich, S. Sabato, R. Urner. Active Nearest-Neighbor Learning in Metric Spaces. NIPS 2016. Long version

Refereed chapters and proceedings

  1. A. Kontorovich, M. Raginsky. Concentration of measure without independence: a unified approach via the martingale method. Invited to The IMA volumes in mathematics and its applications, 2016.

Unrefereed