Papers
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy et al.
Choosing the Sample with Lowest Loss makes SGD Robust
Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
Jiacheng Zhuo, Qi Lei, Alex Dimakis et al.
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
Boyue Li, Shicong Cen, Yuxin Chen et al.
Competing Bandits in Matching Markets
Lydia T. Liu, Horia Mania, Michael Jordan
Computing Tight Differential Privacy Guarantees Using FFT
Antti Koskela, Joonas Jälkö, Antti Honkela
Conditional Importance Sampling for Off-Policy Learning
Mark Rowland, Anna Harutyunyan, Hado Hasselt et al.
Conditional Linear Regression
Diego Calderon, Brendan Juba, Sirui Li et al.
Conservative Exploration in Reinforcement Learning
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric et al.
Constructing a provably adversarially-robust classifier from a high accuracy one
Grzegorz Gluch, Rüdiger Urbanke
Context Mover’s Distance & Barycenters: Optimal Transport of Contexts for Building Representations
Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut et al.
Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization
Andi Nika, Sepehr Elahi, Cem Tekin
Contextual Constrained Learning for Dose-Finding Clinical Trials
Hyun-Suk Lee, Cong Shen, James Jordon et al.
Contextual Online False Discovery Rate Control
Shiyun Chen, Shiva Kasiviswanathan
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
Mojmir Mutny, Michal Derezinski, Andreas Krause
Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models
Milan Vojnovic, Se-Young Yun, Kaifang Zhou
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference
Jonathan Lee, Aldo Pacchiano, Michael Jordan
Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models
Tolga Ergen, Mert Pilanci
Coping With Simulators That Don’t Always Return
Andrew Warrington, Saeid Naderiparizi, Frank Wood
Corruption-Tolerant Gaussian Process Bandit Optimization
Ilija Bogunovic, Andreas Krause, Jonathan Scarlett
Data Generation for Neural Programming by Example
Judith Clymo, Haik Manukian, Nathanael Fijalkow et al.
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate
Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari et al.
Decentralized gradient methods: does topology matter?
Giovanni Neglia, Chuan Xu, Don Towsley et al.
Decentralized Multi-player Multi-armed Bandits with No Collision Information
Chengshuai Shi, Wei Xiong, Cong Shen et al.
Deep Active Learning: Unified and Principled Method for Query and Training
Changjian Shui, Fan Zhou, Christian Gagné et al.