Data Poisoning Attacks on Factorization-Based Collaborative Filtering Authors Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik Abstract Recommendation and collaborative filtering systems are important in modern information and e-commerce applications. As these systems are becoming increasingly popular in industry, their outputs could affect business decision making, introducing incentives for
Advances in Neural Information Processing Systems 28 (NIPS 2015) The papers below appear in Advances in Neural Information Processing Systems 28 edited by C. Cortes and N.D. Lawrence and D.D. Lee and M. Sugiyama and R. Garnett. They are proceedings from the conference, "Neural Information Processing Systems 2015." Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar Bhadr
Authors Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, Max Welling Abstract We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities p(y|x, D), e.g., for applications involving bandits or active learning. One simple approach to this
Advances in Neural Information Processing Systems 26 (NIPS 2013) The papers below appear in Advances in Neural Information Processing Systems 26 edited by C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger. They are proceedings from the conference, "Neural Information Processing Systems 2013." The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Bern
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