Papers
Combinatorial Penalties: Which structures are preserved by convex relaxations?
Marwa El Halabi, Francis Bach, Volkan Cevher
Metrics for Deep Generative Models
Nutan Chen, Alexej Klushyn, Richard Kurle et al.
On the Statistical Efficiency of Compositional Nonparametric Prediction
Yixi Xu, Jean Honorio, Xiao Wang
Factorized Recurrent Neural Architectures for Longer Range Dependence
Francois Belletti, Alex Beutel, Sagar Jain et al.
Plug-in Estimators for Conditional Expectations and Probabilities
Steffen Grunewalder
Stochastic algorithms for entropy-regularized optimal transport problems
Brahim Khalil Abid, Robert Gower
Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization
Seung-Jean Kim, Johan Lim, Joong-Ho Won
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity
Asish Ghoshal, Jean Honorio
Convergence diagnostics for stochastic gradient descent with constant learning rate
Jerry Chee, Panos Toulis
Learning linear structural equation models in polynomial time and sample complexity
Asish Ghoshal, Jean Honorio
Frank-Wolfe Splitting via Augmented Lagrangian Method
Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro
Bayesian Structure Learning for Dynamic Brain Connectivity
Michael Andersen, Ole Winther, Lars Kai Hansen et al.
Scalable Generalized Dynamic Topic Models
Patrick Jähnichen, Florian Wenzel, Marius Kloft et al.
Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation
Mohammadreza Soltani, Chinmay Hegde
Product Kernel Interpolation for Scalable Gaussian Processes
Jacob Gardner, Geoff Pleiss, Ruihan Wu et al.
Robust Vertex Enumeration for Convex Hulls in High Dimensions
Pranjal Awasthi, Bahman Kalantari, Yikai Zhang
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Penporn Koanantakool, Alnur Ali, Ariful Azad et al.
Teacher Improves Learning by Selecting a Training Subset
Yuzhe Ma, Robert Nowak, Philippe Rigollet et al.
Stochastic Zeroth-order Optimization in High Dimensions
Yining Wang, Simon Du, Sivaraman Balakrishnan et al.
Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go?
Chandrashekar Lakshminarayanan, Csaba Szepesvari
Kernel Conditional Exponential Family
Michael Arbel, Arthur Gretton
Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning
Shashank Singh, Barnabas Poczos, Jian Ma
Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams
Chris Hickey, Graham Cormode