Papers
Achieving the time of 1-NN, but the accuracy of k-NN
Lirong Xue, Samory Kpotufe
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Scott Linderman, Gonzalo Mena, Hal Cooper et al.
Learning Generative Models with Sinkhorn Divergences
Aude Genevay, Gabriel Peyre, Marco Cuturi
Online Regression with Partial Information: Generalization and Linear Projection
Shinji Ito, Daisuke Hatano, Hanna Sumita et al.
Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems
Jason Ge, Zhaoran Wang, Mengdi Wang et al.
On Statistical Optimality of Variational Bayes
Debdeep Pati, Anirban Bhattacharya, Yun Yang
Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth
Jussi Viinikka, Ralf Eggeling, Mikko Koivisto
Generalized Binary Search For Split-Neighborly Problems
Stephen Mussmann, Percy Liang
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.