Papers
Cross-Loss Influence Functions to Explain Deep Network Representations
Andrew Silva, Rohit Chopra, Matthew Gombolay
Crowdsourcing Regression: A Spectral Approach
Yaniv Tenzer, Omer Dror, Boaz Nadler et al.
Cycle Consistent Probability Divergences Across Different Spaces
Zhengxin Zhang, Youssef Mroueh, Ziv Goldfeld et al.
Data Appraisal Without Data Sharing
Xinlei Xu, Awni Hannun, Laurens Van Der Maaten
Data-splitting improves statistical performance in overparameterized regimes
Nicole Muecke, Enrico Reiss, Jonas Rungenhagen et al.
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search
Matti Karppa, Martin Aumüller, Rasmus Pagh
Debiasing Samples from Online Learning Using Bootstrap
Ningyuan Chen, Xuefeng Gao, Yi Xiong
Decoupling Local and Global Representations of Time Series
Sana Tonekaboni, Chun-Liang Li, Sercan O. Arik et al.
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
Cristian I. Challu, Peihong Jiang, Ying Nian Wu et al.
Deep Layer-wise Networks Have Closed-Form Weights
Chieh Tzu Wu, Aria Masoomi, Arthur Gretton et al.
Deep Multi-Fidelity Active Learning of High-Dimensional Outputs
Shibo Li, Zheng Wang, Robert Kirby et al.
Deep Neyman-Scott Processes
Chengkuan Hong, Christian Shelton
Deep Non-crossing Quantiles through the Partial Derivative
Axel Brando, Barcelona Supercomputing Center, )*; and Joan Gimeno et al.
Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients
Fan Wang, Oscar Madrid, Yi Yu et al.
Density Ratio Estimation via Infinitesimal Classification
Kristy Choi, Chenlin Meng, Yang Song et al.
Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis
Dominic Danks, Christopher Yau
Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion
Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis
Differentially Private Densest Subgraph
Alireza Farhadi, MohammadTaghi Hajiaghayi, Elaine Shi
Differentially Private Federated Learning on Heterogeneous Data
Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut
Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown
Adrian Rivera Cardoso, Ryan Rogers
Differentially Private Regression with Unbounded Covariates
Jason Milionis, Alkis Kalavasis, Dimitris Fotakis et al.
Differential privacy for symmetric log-concave mechanisms
Staal A. Vinterbo
Dimensionality Reduction and Prioritized Exploration for Policy Search
Marius Memmel, Puze Liu, Davide Tateo et al.
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig et al.
Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data
Matthew Engelhard, Ricardo Henao