Papers
4,025 papers found
Differentially Private Synthetic Control
Saeyoung Rho, Rachel Cummings, Vishal Misra
Diffusion Generative Models in Infinite Dimensions
Gavin Kerrigan, Justin Ley, Padhraic Smyth
Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon Forecasting
Helmuth Naumer, Farzad Kamalabadi
Direct Inference of Effect of Treatment (DIET) for a Cookieless World
Shiv Shankar, Ritwik Sinha, Saayan Mitra et al.
Discovering Many Diverse Solutions with Bayesian Optimization
Natalie Maus, Kaiwen Wu, David Eriksson et al.
Discrete Distribution Estimation under User-level Local Differential Privacy
Jayadev Acharya, Yuhan Liu, Ziteng Sun
Discrete Langevin Samplers via Wasserstein Gradient Flow
Haoran Sun, Hanjun Dai, Bo Dai et al.
Distance-to-Set Priors and Constrained Bayesian Inference
Rick Presman, Jason Xu
Distill n’ Explain: explaining graph neural networks using simple surrogates
Tamara Pereira, Erik Nascimento, Lucas E. Resck et al.
Distributed Offline Policy Optimization Over Batch Data
Han Shen, Songtao Lu, Xiaodong Cui et al.
Distributionally Robust Policy Gradient for Offline Contextual Bandits
Zhouhao Yang, Yihong Guo, Pan Xu et al.
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick et al.
Does Label Differential Privacy Prevent Label Inference Attacks?
Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger et al.
Domain Adaptation under Missingness Shift
Helen Zhou, Sivaraman Balakrishnan, Zachary Lipton
Don’t be fooled: label leakage in explanation methods and the importance of their quantitative evaluation
Neil Jethani, Adriel Saporta, Rajesh Ranganath
Doubly Fair Dynamic Pricing
Jianyu Xu, Dan Qiao, Yu-Xiang Wang
Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity
Xingyu Lu, Hasin Us Sami, Başak Güler
Dueling RL: Reinforcement Learning with Trajectory Preferences
Aadirupa Saha, Aldo Pacchiano, Jonathan Lee
EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model
Yirui Liu, Xinghao Qiao, Liying Wang et al.
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
Chen Dun, Mirian Hipolito, Chris Jermaine et al.
Efficient fair PCA for fair representation learning
Matthäus Kleindessner, Michele Donini, Chris Russell et al.
Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation
Yue Xiang, Dongyao Zhu, Bowen Lei et al.
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection
Weilin Cong, Mehrdad Mahdavi
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner et al.
Efficient SAGE Estimation via Causal Structure Learning
Christoph Luther, Gunnar König, Moritz Grosse-Wentrup