Papers
Deep Asymmetric Multi-task Feature Learning
Hae Beom Lee, Eunho Yang, Sung Ju Hwang
Deep Bayesian Nonparametric Tracking
Aonan Zhang, John Paisley
Deep Density Destructors
David Inouye, Pradeep Ravikumar
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
Junru Wu, Yue Wang, Zhenyu Wu et al.
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Thomas Laurent, James Brecht
Deep Models of Interactions Across Sets
Jason Hartford, Devon Graham, Kevin Leyton-Brown et al.
Deep One-Class Classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz et al.
Deep Predictive Coding Network for Object Recognition
Haiguang Wen, Kuan Han, Junxing Shi et al.
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
Kyowoon Lee, Sol-A Kim, Jaesik Choi et al.
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le et al.
Delayed Impact of Fair Machine Learning
Lydia T. Liu, Sarah Dean, Esther Rolf et al.
Dependent Relational Gamma Process Models for Longitudinal Networks
Sikun Yang, Heinz Koeppl
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Simon Olofsson, Marc Deisenroth, Ruth Misener
Detecting and Correcting for Label Shift with Black Box Predictors
Zachary Lipton, Yu-Xiang Wang, Alexander Smola
Detecting non-causal artifacts in multivariate linear regression models
Dominik Janzing, Bernhard Schölkopf
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat et al.
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
Thomas Moreau, Laurent Oudre, Nicolas Vayatis
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Matthew Mirman, Timon Gehr, Martin Vechev
Differentiable Compositional Kernel Learning for Gaussian Processes
Shengyang Sun, Guodong Zhang, Chaoqi Wang et al.
Differentiable Dynamic Programming for Structured Prediction and Attention
Arthur Mensch, Mathieu Blondel
Differentiable plasticity: training plastic neural networks with backpropagation
Thomas Miconi, Kenneth Stanley, Jeff Clune
Differentially Private Database Release via Kernel Mean Embeddings
Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf
Differentially Private Identity and Equivalence Testing of Discrete Distributions
Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld
Differentially Private Matrix Completion Revisited
Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta
Dimensionality-Driven Learning with Noisy Labels
Xingjun Ma, Yisen Wang, Michael E. Houle et al.