Papers
Towards Testing Monotonicity of Distributions Over General Posets
Maryam Aliakbarpour, Themis Gouleakis, John Peebles et al.
Learning to Prune: Speeding up Repeated Computations
Daniel Alabi, Adam Tauman Kalai, Katrina Liggett et al.
Learning in Non-convex Games with an Optimization Oracle
Naman Agarwal, Alon Gonen, Elad Hazan
Inference under Information Constraints: Lower Bounds from Chi-Square Contraction
Jayadev Acharya, Clément L Canonne, Himanshu Tyagi
Conference on Learning Theory 2019: Preface
Alina Beygelzimer, Daniel Hsu
Minimax Learning of Ergodic Markov Chains
Geoffrey Wolfer, Aryeh Kontorovich
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations
Di Wang, Adam Smith, Jinhui Xu
Online Linear Optimization with Sparsity Constraints
Jun-Kun Wang, Chi-Jen Lu, Shou-De Lin
Stochastic Nonconvex Optimization with Large Minibatches
Weiran Wang, Nathan Srebro
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
Ivan Stelmakh, Nihar B. Shah, Aarti Singh
General parallel optimization a without metric
Xuedong Shang, Emilie Kaufmann, Michal Valko
PAC Battling Bandits in the Plackett-Luce Model
Aadirupa Saha, Aditya Gopalan
Ising Models with Latent Conditional Gaussian Variables
Frank Nussbaum, Joachim Giesen
Average-Case Information Complexity of Learning
Ido Nachum, Amir Yehudayoff
Can Adversarially Robust Learning LeverageComputational Hardness?
Saeed Mahloujifar, Mohammad Mahmoody
Online Influence Maximization with Local Observations
Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
Cleaning up the neighborhood: A full classification for adversarial partial monitoring
Tor Lattimore, Csaba Szepesvári
Optimal Collusion-Free Teaching
David Kirkpatrick, Hans U. Simon, Sandra Zilles
A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes
Steve Hanneke, Aryeh Kontorovich
Sample Compression for Real-Valued Learners
Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi