Papers
The Gaussian Process Autoregressive Regression Model (GPAR)
James Requeima, William Tebbutt, Wessel Bruinsma et al.
The LORACs Prior for VAEs: Letting the Trees Speak for the Data
Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson
The non-parametric bootstrap and spectral analysis in moderate and high-dimension
Noureddine El Karoui, Elizabeth Purdom
Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning
Tadashi Kozuno, Eiji Uchibe, Kenji Doya
The Termination Critic
Anna Harutyunyan, Will Dabney, Diana Borsa et al.
Top Feasible Arm Identification
Julian Katz-Samuels, Clayton Scott
Tossing Coins Under Monotonicity
Matey Neykov
Towards a Theoretical Understanding of Hashing-Based Neural Nets
Yibo Lin, Zhao Song, Lin F. Yang
Towards Clustering High-dimensional Gaussian Mixture Clouds in Linear Running Time
Dan Kushnir, Shirin Jalali, Iraj Saniee
Towards Efficient Data Valuation Based on the Shapley Value
Ruoxi Jia, David Dao, Boxin Wang et al.
Towards Gradient Free and Projection Free Stochastic Optimization
Anit Kumar Sahu, Manzil Zaheer, Soummya Kar
Towards Optimal Transport with Global Invariances
David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent
Yifan Wu, Barnabas Poczos, Aarti Singh
Training a Spiking Neural Network with Equilibrium Propagation
Peter O’Connor, Efstratios Gavves, Max Welling
Training Variational Autoencoders with Buffered Stochastic Variational Inference
Rui Shu, Hung Bui, Jay Whang et al.
Truncated Back-propagation for Bilevel Optimization
Amirreza Shaban, Ching-An Cheng, Nathan Hatch et al.
Two-temperature logistic regression based on the Tsallis divergence
Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan
Unbiased Implicit Variational Inference
Michalis K. Titsias, Francisco Ruiz
Unbiased Smoothing using Particle Independent Metropolis-Hastings
Lawrece Middleton, George Deligiannidis, Arnaud Doucet et al.
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
Aditya Grover, Stefano Ermon
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit
Shengyu Zhu, Biao Chen, Pengfei Yang et al.
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach
Ryo Karakida, Shotaro Akaho, Shun-ichi Amari
Unsupervised Alignment of Embeddings with Wasserstein Procrustes
Edouard Grave, Armand Joulin, Quentin Berthet
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
Topi Paananen, Juho Piironen, Michael Riis Andersen et al.
Variance reduction properties of the reparameterization trick
Ming Xu, Matias Quiroz, Robert Kohn et al.