Accepted Papers

  • Peter Bartlett, Victor Gabillon and Michal Valko. A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption
  • Di Wang, Adam Smith and Jinhui Xu. Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation
  • Chi-Jen Lu, Jun-Kun Wang and Shou-De Lin. Online Linear Optimization with Sparsity Constraints
  • Nader Bshouty and Catherine Haddad-Zaknoon. Adaptive Exact Learning of Decision Trees from Membership Queries
  • Or Sheffet. Old Techniques in Differentially Private Linear Regression
  • Pierre Gaillard, Sebastien Gerchinovitz, Malo Huard and Gilles Stoltz. Uniform regret bounds over R^d for the sequential linear regression problem with the square loss
  • Geoffrey Wolfer and Aryeh Kontorovich. Minimax Learning of Ergodic Markov Chains
  • Weiran Wang and Nathan Srebro. Stochastic Nonconvex Optimization with Large Minibatches
  • Ekaterina Fokina, Timo Kötzing and Luca San Mauro. Limit Learning Equivalence Structures
  • Clayton Scott. A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation
  • Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian and Manfred Warmuth. Online Non-Additive Path Learning under Full and Partial Information
  • Idan Attias, Aryeh Kontorovich and Yishay Mansour. Improved generalization bounds for robust learning
  • Brian Bullins, Elad Hazan, Adam Kalai and Roi Livni. Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning
  • Mastane Achab, Anna Korba and Stéphan Clémençon. Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
  • Peter Grünwald and Nishant Mehta. A Tight Excess Risk Bound via a Unified PAC-Bayesian–Rademacher–Shtarkov–MDL Complexity
  • Andrew Cotter, Heinrich Jiang and Karthik Sridharan. Two-Player Games for Efficient Non-Convex Constrained Optimization
  • Alexandr Andoni, Rishabh Dudeja, Daniel Hsu and Kiran Vodrahalli. Attribute-efficient learning of monomials over highly-correlated variables
  • Frank Nussbaum and Joachim Giesen. Ising Models with Latent Conditional Gaussian Variables
  • Amit Daniely and Yishay Mansour. Competitive ratio vs regret minimization: achieving the best of both worlds
  • Hasan Abasi and Nader Bshouty. On Learning Graphs with Edge-Detecting Queries
  • Xuedong Shang, Emilie Kaufmann and Michal Valko. General parallel optimization without metric
  • Tor Lattimore and Csaba Szepesvari. Cleaning up the neighborhood: A full classification for adversarial partial monitoring
  • Alexander Durgin and Brendan Juba. Hardness of Improper One-sided Learning of Conjunctions For All Uniformly Falsifiable CSPs
  • Ivan Stelmakh, Nihar Shah and Aarti Singh. PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
  • Nicolo Cesa-Bianchi, Tommaso Renato Cesari and Vianney Perchet. Dynamic Pricing with Finitely Many Unknown Valuations
  • David Kirkpatrick, Hans Simon and Sandra Zilles. Optimal Collusion-Free Teaching
  • Henry Reeve and Ata Kaban. Exploiting geometric structure in mixture proportion estimation with generalised Blanchard-Lee-Scott estimators
  • Julia Olkhovskaya, Gergely Neu and Gabor Lugosi. Online Influence Maximization with Local Observations
  • Juliette Achdou, Joseph Lam, Alexandra Carpentier and Gilles Blanchard. A minimax near-optimal algorithm for adaptive rejection sampling
  • Odalric-Ambrym Maillard. Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds.
  • Steve Hanneke and Aryeh Kontorovich. A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes
  • Karim Abou-Moustafa and Csaba Szepesvari. An Exponential Efron-Stein Inequality for Lq Stable Learning Rules. The Deleted Estimate Case
  • Ido Nachum and Amir Yehudayoff. Average-Case Information Complexity of Learning
  • Steve Hanneke, Aryeh Kontorovich and Menachem Sadigurschi. Sample Compression for Real-Valued Learners
  • Aadirupa Saha and Aditya Gopalan. PAC Battling Bandits in the Plackett-Luce Model
  • Saeed Mahloujifar and Mohammad Mahmoody. Can Adversarially Robust Learning Leverage Computational Hardness?
  • Mohamed Ndaoud. Interplay of minimax estimation and minimax support recovery under sparsity