Competing in the dark: An efficient algorithm for bandit linear optimization, 21st Annual Conference on Learning Theory-COLT, pp.263-274, 2008. ,

Optimal allocation strategies for the dark pool problem, AISTATS, vol.9, pp.9-16, 2010. ,

Learning user interaction models for predicting web search result preferences, Proceedings of the 29th annual international ACM conference on Research and development in information retrieval (SIGIR), pp.3-10, 2006. ,

Analysis of thompson sampling for the multiarmed bandit problem, Journal of Machine Learning Research-Proceedings Track, vol.23, pp.39-40, 2012. ,

Further optimal regret bounds for thompson sampling, Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, pp.99-107, 2013. ,

Reducing dueling bandits to cardinal bandits, ICML 2014, vol.32, pp.856-864, 2014. ,

Learning from noisy examples, Mach. Learn, vol.2, issue.4, pp.343-370, 1988. ,

DOI : 10.1007/bf00116829

Best arm identification in multi-armed bandits, COLT, Haïfa (Israël), 2010. ,

URL : https://hal.archives-ouvertes.fr/hal-00654404

Finite-time analysis of the multiarmed bandit problem, Mach. Learn, vol.47, issue.2-3, pp.235-256, 2002. ,

The nonstochastic multiarmed bandit problem, SIAM Journal on Computing, vol.32, issue.1, pp.48-77, 2002. ,

DOI : 10.1137/s0097539701398375

The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization, MIS Q, vol.30, issue.1, pp.13-28, 2006. ,

The Role of Information in Online Learning, 2012. ,

A near-optimal algorithm for finite partial-monitoring games against adversarial opponents, Proc. COLT, 2013. ,

Minimax regret of finite partialmonitoring games in stochastic environments, Conference on Learning Theory, 2011. ,

Partial monitoring-classification, regret bounds, and algorithms, Math. Oper. Res, vol.39, issue.4, pp.967-997, 2014. ,

A sequential multiple-decision procedure for selecting the best one of several normal populations with a common unknown variance, and its use with various experimental designs, Biometrics, vol.14, issue.3, pp.408-429, 1958. ,

The netflix prize, Proceedings of the KDD Cup Workshop, pp.3-6, 2007. ,

Regret analysis of stochastic and nonstochastic multi-armed bandit problems, Foundations and Trends in Machine Learning, vol.5, issue.1, pp.1-122, 2012. ,

Pure exploration in multi-armed bandits problems, ALT, pp.23-37, 2009. ,

DOI : 10.1007/978-3-642-04414-4_7

URL : http://arxiv.org/pdf/0802.2655v2.pdf

you might also like: " privacy risks of collaborative filtering, 32nd IEEE Symposium on Security and Privacy, pp.231-246, 2011. ,

DOI : 10.1109/sp.2011.40

URL : http://www.cs.utexas.edu/%7Eshmat/shmat_oak11ymal.pdf

Findings of the 2011 workshop on statistical machine translation, Proceedings of the Sixth Workshop on Statistical Machine Translation, WMT '11, pp.22-64, 2011. ,

Kullback-Leibler upper confidence bounds for optimal sequential allocation, Annals of Statistics, vol.41, issue.3, pp.1516-1541, 2013. ,

, Prediction, Learning, and Games, p.521841089, 2006.

Improved second-order bounds for prediction with expert advice, Mach. Learn, vol.66, issue.2-3, pp.321-352, 2007. ,

DOI : 10.1007/11503415_15

URL : https://hal.archives-ouvertes.fr/hal-00019799

Minimizing regret with label efficient prediction, IEEE Trans. Inform. Theory, vol.51, pp.77-92, 2005. ,

DOI : 10.1109/tit.2005.847729

URL : https://hal.archives-ouvertes.fr/hal-00007537

Private and continual release of statistics, ACM Trans. Inf. Syst. Secur, vol.14, issue.3, 2011. ,

Large-scale validation and analysis of interleaved search evaluation, ACM Trans. Inf. Syst, vol.30, issue.1, p.6, 2012. ,

An updated survey on the linear ordering problem for weighted or unweighted tournaments, Annals OR, vol.175, issue.1, pp.107-158, 2010. ,

Personalization versus privacy: An empirical examination of the online consumer's dilemma, Inf. Technol. and Management, vol.6, issue.2-3, pp.181-202, 2005. ,

Microdata Protection, pp.291-321, 2007. ,

Protecting Privacy Online: Is Self-Regulation Working, Journal of Public Policy & Marketing, vol.19, issue.1, pp.20-26, 2000. ,

Learning to classify with missing and corrupted features, Machine Learning, vol.81, pp.149-178, 2010. ,

, Algorithmic Learning Theory: 9th International Conference, ALT'98

, Proceedings, chapter PAC Learning from Positive Statistical Queries, pp.112-126, 1998.

Text Classification from Positive and Unlabeled Examples, Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU'02, pp.1927-1934, 2002. ,

URL : https://hal.archives-ouvertes.fr/inria-00538889

Text classification and co-training from positive and unlabeled examples, Proceedings of the ICML 2003 Workshop: The Continuum from Labeled to Unlabeled Data, pp.80-87, 2003. ,

Learning from positive and unlabeled examples, ALT 2000)11th International Conference, Algorithmic Learning Theory, vol.348, pp.70-83, 2000. ,

URL : https://hal.archives-ouvertes.fr/inria-00538887

Contextual dueling bandits, Proceedings of The 28th Conference on Learning Theory, vol.40, pp.3-06, 2015. ,

Differential privacy, 33rd International Colloquium on ,

, , vol.4052, pp.1-12, 2006.

The Differential Privacy Frontier (Extended Abstract) Theory of Cryptography, Theory of Cryptography, vol.29, pp.496-502 ,

, , pp.978-981, 2009.

Differential privacy in new settings, Symposium on Discrete Algorithms (SODA). Society for Industrial and Applied Mathematics, 2010. ,

The algorithmic foundations of differential privacy, Found. Trends Theor. Comput. Sci, vol.9, pp.211-407, 2014. ,

Calibrating noise to sensitivity in private data analysis, Proceedings of the 3rd Theory of Cryptography Conference, pp.265-284, 2006. ,

On the complexity of differentially private data release: Efficient algorithms and hardness results, Proceedings of the Forty-first Annual ACM Symposium on Theory of Computing, STOC '09, pp.381-390, 2009. ,

Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems, Journal of Machine Learning Research, vol.7, pp.1079-1105, 2006. ,

Learning and inference in the presence of corrupted inputs, Proceedings of The 28th Conference on Learning Theory, COLT 2015, pp.637-657, 2015. ,

Online convex optimization in the bandit setting: gradient descent without a gradient, 2004. ,

No internal regret via neighborhood watch. CoRR, abs/1108, vol.6088, 2011. ,

Classification in the presence of label noise: A survey, IEEE Trans. Neural Netw. Learning Syst, vol.25, issue.5, pp.845-869, 2014. ,

A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci, vol.55, issue.1, pp.119-139, 1997. ,

Adaptive game playing using multiplicative weights, Games and Economic Behavior, vol.29, issue.1-2, pp.79-103, 1999. ,

DOI : 10.1006/game.1999.0738

URL : http://www.cs.princeton.edu/~schapire/papers/FreundScYY.pdf

Adaptive game playing using multiplicative weights, Games and Economic Behavior, vol.29, issue.1, pp.79-103, 1999. ,

DOI : 10.1006/game.1999.0738

URL : http://www.cs.princeton.edu/~schapire/papers/FreundScYY.pdf

An efficient boosting algorithm for combining preferences, J. Mach. Learn. Res, vol.4, pp.933-969, 2003. ,

, Preference Learning, 2010.

A relative exponential weighing algorithm for adversarial utility-based dueling bandits, Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, pp.218-227, 2015. ,

URL : https://hal.archives-ouvertes.fr/hal-01225614

Explore first, exploit next: The true shape of regret in bandit problems, 2016. ,

URL : https://hal.archives-ouvertes.fr/hal-01276324

Preference learning in recommender systems, Preference Learning (PL-09) ECML/PKDD-09 Workshop, 2009. ,

Nightmare at test time: Robust learning by feature deletion, Proceedings of the 23rd International Conference on Machine Learning, ICML '06, pp.353-360, 2006. ,

DOI : 10.1145/1143844.1143889

It's modern trade: Web users get as much as they give, The Wall Street Journal, 2010. ,

Differentially private online learning, COLT 2012-The 25th Annual Conference on Learning Theory, vol.34, pp.24-25, 2012. ,

Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search, ACM Trans. Inf. Syst, vol.25, issue.2, 2007. ,

Efficient algorithms for online decision problems, J. Comput. Syst. Sci, vol.71, issue.3, pp.291-307, 2005. ,

DOI : 10.1007/978-3-540-45167-9_4

Almost optimal exploration in multi-armed bandits, Proceedings of the 30th International Conference on Machine Learning (ICML-13), vol.28, pp.1238-1246, 2013. ,

Noisy binary search and its applications, SODA 2007, SIAM Proceedings, pp.881-890, 2007. ,

On the complexity of bestarm identification in multi-armed bandit models, Journal of Machine Learning Research, vol.17, issue.1, pp.1-42, 2016. ,

Learning in the presence of malicious errors, SIAM J. Comput, vol.22, issue.4, pp.807-837, 1993. ,

A method for limiting disclosure in microdata based on random noise and transformation, Proceedings of the Survey Research Methods, pp.370-374, 1986. ,

Multiplicative noise for masking continuous data, 2003. ,

The value of knowing a demand curve: Bounds on regret for online posted-price auctions, FOCS, pp.594-605, 2003. ,

Privacy violations using microtargeted ads: A case study, The 10th IEEE International Conference on Data Mining Workshops, pp.474-482, 2010. ,

Private traits and attributes are predictable from digital records of human behavior, Proceedings of the National Academy of Sciences, vol.110, issue.15, pp.5802-5805, 2013. ,

Asymptotically efficient adaptive allocation rules, Advances in Applied Mathematics, vol.6, issue.1, pp.4-22, 1985. ,

Learning with positive and unlabeled examples using weighted logistic regression, Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), pp.448-455, 2003. ,

Learning with positive and unlabeled examples using weighted logistic regression, Proceedings of the Twentieth International Conference on Machine Learning (ICML, p.2003, 2003. ,

Learning to classify texts using positive and unlabeled data, Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI'03, pp.587-592, 2003. ,

A practical application of differential privacy to personalized online advertising, IACR Cryptology ePrint Archive, p.152, 2011. ,

The weighted majority algorithm, Inf. Comput, vol.108, issue.2, pp.212-261, 1994. ,

Algorithms for sequential decision making, 1996. ,

Partially supervised classification of text documents, Proceedings of the Nineteenth International Conference on Machine Learning, ICML '02, pp.387-394, 2002. ,

Building text classifiers using positive and unlabeled examples, Proceedings of the Third IEEE International Conference on Data Mining, ICDM '03, p.179, 2003. ,

Learning to rank for information retrieval, Found. Trends Inf. Retr, vol.3, issue.3, pp.225-331, 2009. ,

LETOR: Benchmark dataset for research on learning to rank for information retrieval, SIGIR 2007, 2007. ,

(nearly) optimal differentially private stochastic multi-arm bandits, Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, pp.592-601, 2015. ,

Utilizing noise addition for data privacy, an overview. CoRR, abs/1309, vol.3958, 2013. ,

Microsoft learning to rank dataset, 2012. ,

Learning with noisy labels, Advances in Neural Information Processing Systems 26, pp.1196-1204, 2013. ,

A sequential procedure for selecting the population with the largest mean from k normal populations, Ann. Math. Statist, vol.35, issue.1, pp.174-180, 1964. ,

Discrete prediction games with arbitrary feedback and loss, COLT/EuroCOLT, vol.2111, pp.208-223, 2001. ,

Active exploration for learning rankings from clickthrough data, KDD 2007, pp.570-579, 2007. ,

DOI : 10.1145/1281192.1281254

Some aspects of the sequential design of experiments, Bull. Amer. Math. Soc, vol.58, issue.5, pp.527-535, 1952. ,

A stochastic approximation method, Ann. Math. Statist, vol.22, issue.3, p.1951 ,

The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review, pp.65-386, 1958. ,

Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition, Knowledge and Information Systems, vol.38, issue.1, pp.179-206, 2014. ,

Evaluation and analysis of the performance of the exp3 Algorithm in stochastic environments, EWRL, vol.24, pp.103-116, 2012. ,

nearly) optimal algorithms for private online learning in full-information and bandit settings, Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held, pp.2733-2741, 2013. ,

On the likelihood that one unknown probability exceeds another in view of the evidence of two samples, Bulletin of the AMS, vol.25, pp.285-294, 1933. ,

Algorithms for differentially private multi-armed bandits, 13th International Conference on Artificial Intelligence (AAAI 2016), 2016. ,

Generic exploration and K-armed voting bandits, ICML 2013, vol.28, pp.91-99, 2013. ,

Generic exploration and k-armed voting bandits, Proceedings of the 30th International Conference on Machine Learning (ICML13), vol.28, pp.91-99, 2013. ,

On cumulative sums of random variables, Ann. Math. Statist, vol.15, issue.3, p.1944 ,

Using randomized response for differential privacy preserving data collection, Proceedings of the Workshops of the EDBT/ICDT 2016 Joint Conference, EDBT/ICDT Workshops 2016, 2016. ,

Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias, Journal of the American Statistical Association, vol.60, issue.309, p.63, 1965. ,

Double thompson sampling for dueling bandits, Advances in Neural Information Processing Systems, vol.29, p.649 ,

Pebl: Positive example based learning for web page classification using svm, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '02, pp.239-248, 2002. ,

Beat the mean bandit, ICML 2011, pp.241-248, 2011. ,

The k-armed dueling bandits problem, J. Comput. Syst. Sci, vol.78, issue.5, pp.1538-1556, 2012. ,

Interactively optimizing information retrieval systems as a dueling bandits problem, ICML 2009, pp.1201-1208, 2009. ,

DOI : 10.1145/1553374.1553527

Learning from Positive and Unlabeled Examples: A Survey, 2008 International Symposiums on Information Processing, vol.0, pp.650-654, 2008. ,

DOI : 10.1109/isip.2008.79

A simple probabilistic approach to learning from positive and unlabeled examples, Proc. of the 5th Annual UK Workshop on Computational Intelligence, 2005. ,

Class noise vs. attribute noise: A quantitative study, Artif. Intell. Rev, vol.22, issue.3, pp.177-210, 2003. ,

Online convex programming and generalized infinitesimal gradient ascent, Proceedings of the Twentieth International Conference (ICML 2003), pp.928-936, 2003. ,

Online convex programming and generalized infinitesimal gradient ascent, Proceedings of the Twentieth International Conference on International Conference on Machine Learning, ICML'03, pp.928-935, 2003. ,

Relative upper confidence bound for the k-armed dueling bandit problem, ICML 2014, vol.32, pp.10-18, 2014. ,

Relative confidence sampling for efficient on-line ranker evaluation, Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM '14, pp.73-82 ,

Relative confidence sampling for efficient on-line ranker evaluation, WSDM 2014, pp.73-82, 2014. ,

DOI : 10.1145/2556195.2556256

Copeland dueling bandits ,

, Advances in Neural Information Processing Systems, vol.28, pp.307-315, 2015.

Mergerucb: A method for large-scale online ranker evaluation, Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, WSDM '15, pp.17-26 ,

DOI : 10.3990/1.9789036542876

MergeRUCB: A method for large-scale online ranker evaluation, WSDM 2015, 2015. ,