Papers
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.
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Thomas Dietterich, George Trimponias, Zhitang Chen
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Tameem Adel, Zoubin Ghahramani, Adrian Weller
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Yi Wu, Siddharth Srivastava, Nicholas Hay et al.
Disentangled Sequential Autoencoder
Li Yingzhen, Stephan Mandt
Disentangling by Factorising
Hyunjik Kim, Andriy Mnih
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Lukas Balles, Philipp Hennig
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Bin Hu, Stephen Wright, Laurent Lessard
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos et al.
Distributed Clustering via LSH Based Data Partitioning
Aditya Bhaskara, Maheshakya Wijewardena
Distributed Nonparametric Regression under Communication Constraints
Yuancheng Zhu, John Lafferty