Papers
Differentiable Kernel Evolution
Yu Liu, Jihao Liu, Ailing Zeng et al.
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
Zhaoyang Zhang, Jingyu Li, Wenqi Shao et al.
Differentiable Learning-to-Normalize via Switchable Normalization
Ping Luo, Jiamin Ren, Zhanglin Peng et al.
Differentiable Linearized ADMM
Xingyu Xie, Jianlong Wu, Guangcan Liu et al.
Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning - Extended Abtract
Marc Toussaint, Kelsey R. Allen, Kevin A. Smith et al.
Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem
Karen Ullrich, Rianne van den Berg, Marcus Brubaker et al.
Differentiable Ranking and Sorting using Optimal Transport
Marco Cuturi, Olivier Teboul, Jean-Philippe Vert
Differentiable reservoir computing
Lyudmila Grigoryeva, Juan-Pablo Ortega
Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation
Weijia Xu, Xing Niu, Marine Carpuat
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
Ruihao Gong, Xianglong Liu, Shenghu Jiang et al.
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory
Huizhuo Yuan, Yuren Zhou, Chris Junchi Li et al.
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal et al.
Differentially Private Anonymized Histograms
Ananda Theertha Suresh
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate
James Jordon, Jinsung Yoon, Mihaela van der Schaar
Differentially Private Bayesian Linear Regression
Garrett Bernstein, Daniel R. Sheldon
Differentially Private Community Detection in Attributed Social Networks
Tianxi Ji, Changqing Luo, Yifan Guo et al.
Differentially Private Covariance Estimation
Kareem Amin, Travis Dick, Alex Kulesza et al.
Differentially Private Distributed Data Summarization under Covariate Shift
Kanthi Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu et al.
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
Di Wang, Changyou Chen, Jinhui Xu
Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao et al.
Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning
Lingxiao Wang, Quanquan Gu
Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias et al.
Differentially Private Markov Chain Monte Carlo
Mikko Heikkilä, Joonas Jälkö, Onur Dikmen et al.