ALT18 Accepted Papers

  1. Uriel Feige, Yishay Mansour and Robert Schapire. Robust Inference for Multiclass Classification
  2. Jialei Wang, Weiran Wang, Dan Garber and Nathan Srebro. Efficient coordinate-wise leading eigenvector computation
  3. Ziyuan Gao, Sanjay Jain, Frank Stephan and Thomas Zeugmann. On the Help of Bounded Shot Verifiers, Comparers, and Standarisers for Learnability in Inductive Inference
  4. Pratik Gajane, Tanguy Urvoy and Emilie Kaufmann. Corrupt Bandits for Preserving Local Privacy
  5. Mohammad Sadegh Talebi and Odalric-Ambrym Maillard. Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs
  6. Yanina Shkel, Maxim Raginsky and Sergio Verdu. Sequential prediction with coded side information under logarithmic loss
  7. Nathan Kallus. Instrument-Armed Bandits
  8. Holakou Rahmanian, David Helmbold and S.V.N. Vishwanathan. Online Learning of Combinatorial Objects via Extended Formulation
  9. Uri Stemmer and Kobbi Nissim. Clustering Algorithms for the Centralized and Local Models of Differential Privacy
  10. Anastasia Pentina and Shai Ben-David. Multi-task Kernel Learning Based on Probabilistic Lipschitzness
  11. Lilian Besson and Emilie Kaufmann. Multi-Player Bandits Models Revisited
  12. Ya-Ping Hsieh and Volkan Cevher. Dimension-free Information Concentration via Exp-Concavity
  13. Stephen Pasteris, Fabio Vitale, Claudio Gentile and Mark Herbster. On Similarity Prediction and Pairwise Clustering
  14. Yuval Dagan and Koby Crammer. A Better Resource Allocation Algorithm with Semi-Bandit Feedback
  15. Elias Jaasaari, Janne Leppa-Aho, Tomi Silander and Teemu Roos. Minimax Optimal Bayes Mixtures for Memoryless Sources over Large Alphabets
  16. Anna Korba, Stephan Clemencon and Eric Sibony. Ranking Median Regression: Learning to Order through Local Consensus
  17. Aditya Modi, Nan Jiang, Satinder Singh and Ambuj Tewari. Markov Decision Processes with Continuous Side Information
  18. Maryam Aziz, Jesse Anderton, Emilie Kaufmann and Javed Aslam. Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence
  19. Raef Bassily, Shay Moran, Ido Nachum, Jonathan Shafer and Amir Yehudayoff. Learners that Use Little Information
  20. Sebastien Bubeck, Michael Cohen and Yuanzhi Li. Sparsity, variance and curvature in multi-armed bandits
  21. Andrea Locatelli, Alexandra Carpentier and Samory Kpotufe. An Adaptive Strategy for Active Learning with Smooth Decision Boundary
  22. Henry Reeve, Gavin Brown and Joe Mellor. The k-Nearest Neighbour UCB Algorithm for Multi-Armed Bandits with Covariates
  23. Danielle Ensign, Sorelle Friedler, Scott Neville, Carlos Scheidegger and Suresh Venkatasubramanian. Decision making with limited feedback: Error bounds for predictive policing and recidivism prediction
  24. Saeed Mahloujifar, Dimitrios I. Diochnos and Mohammad Mahmoody. Learning under p-Tampering Attacks
  25. Alon Gonen and Ran Gilad-Bachrach. Smooth Sensitivity Based Approach for Differentially Private Principal Component Analysis
  26. Antonio Blanca, Zongchen Chen, Daniel Stefankovic and Eric Vigoda. Structure Learning of H-colorings
  27. Cheng Mao, Jonathan Weed and Philippe Rigollet. Minimax Rates and Efficient Algorithms for Noisy Sorting
  28. Tom Jurgenson and Yishay Mansour. Learning Decision Trees with Stochastic Linear Classifiers
  29. Nicolo Cesa-Bianchi and Ohad Shamir. Bandit Regret Scaling with the Effective Loss Range
  30. Dominik Csiba and Peter Richtarik. Coordinate Descent Faceoff: Primal or Dual?
  31. Nader Bshouty, Vivian Bshouty-Hurani, George Haddad, Thomas Hashem, Fadi Khoury and Omar Sharafy. Adaptive Group Testing Algorithms to Estimate the Number of Defectives
  32. Xiang Cheng and Peter Bartlett. Convergence of Langevin MCMC in KL-Divergence
  33. Justin Eldridge, Mikhail Belkin and Yusu Wang. Unperturbed: spectral analysis beyond Davis-Kahan