Papers
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables
Bryant Chen, Daniel Kumor, Elias Bareinboim
Learning to Learn without Gradient Descent by Gradient Descent
Yutian Chen, Matthew W. Hoffman, Sergio Gómez Colmenarejo et al.
Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions
Yichen Chen, Dongdong Ge, Mengdi Wang et al.
Dueling Bandits with Weak Regret
Bangrui Chen, Peter I. Frazier
Adaptive Multiple-Arm Identification
Jiecao Chen, Xi Chen, Qin Zhang et al.
Robust Structured Estimation with Single-Index Models
Sheng Chen, Arindam Banerjee
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
Yevgen Chebotar, Karol Hausman, Marvin Zhang et al.
Active Heteroscedastic Regression
Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference
Aditya Chaudhry, Pan Xu, Quanquan Gu
Multiple Clustering Views from Multiple Uncertain Experts
Yale Chang, Junxiang Chen, Michael H. Cho et al.
Sliced Wasserstein Kernel for Persistence Diagrams
Mathieu Carrière, Marco Cuturi, Steve Oudot
“Convex Until Proven Guilty”: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions
Yair Carmon, John C. Duchi, Oliver Hinder et al.
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Priv’IT: Private and Sample Efficient Identity Testing
Bryan Cai, Constantinos Daskalakis, Gautam Kamath
Multi-objective Bandits: Optimizing the Generalized Gini Index
Róbert Busa-Fekete, Balázs Szörényi, Paul Weng et al.
Deep Tensor Convolution on Multicores
David Budden, Alexander Matveev, Shibani Santurkar et al.
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs
Alon Brutzkus, Amir Globerson
Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning
Noam Brown, Tuomas Sandholm
On the Sampling Problem for Kernel Quadrature
François-Xavier Briol, Chris J. Oates, Jon Cockayne et al.
Clustering High Dimensional Dynamic Data Streams
Vladimir Braverman, Gereon Frahling, Harry Lang et al.
Lazifying Conditional Gradient Algorithms
Gábor Braun, Sebastian Pokutta, Daniel Zink
Practical Gauss-Newton Optimisation for Deep Learning
Aleksandar Botev, Hippolyt Ritter, David Barber
Programming with a Differentiable Forth Interpreter
Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky et al.
Compressed Sensing using Generative Models
Ashish Bora, Ajil Jalal, Eric Price et al.
Adaptive Neural Networks for Efficient Inference
Tolga Bolukbasi, Joseph Wang, Ofer Dekel et al.