Papers
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
Shixiang Chen, Alfredo Garcia, Mingyi Hong et al.
Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games
Hongyi Guo, Zuyue Fu, Zhuoran Yang et al.
Deciding What to Learn: A Rate-Distortion Approach
Dilip Arumugam, Benjamin Van Roy
Decomposable Submodular Function Minimization via Maximum Flow
Kyriakos Axiotis, Adam Karczmarz, Anish Mukherjee et al.
Decomposed Mutual Information Estimation for Contrastive Representation Learning
Alessandro Sordoni, Nouha Dziri, Hannes Schulz et al.
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Evan Z Liu, Aditi Raghunathan, Percy Liang et al.
Decoupling Representation Learning from Reinforcement Learning
Adam Stooke, Kimin Lee, Pieter Abbeel et al.
Decoupling Value and Policy for Generalization in Reinforcement Learning
Roberta Raileanu, Rob Fergus
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
Adam Foster, Desi R Ivanova, Ilyas Malik et al.
Deep Coherent Exploration for Continuous Control
Yijie Zhang, Herke Van Hoof
Deep Continuous Networks
Nergis Tomen, Silvia-Laura Pintea, Jan Van Gemert
Deep Generative Learning via Schrödinger Bridge
Gefei Wang, Yuling Jiao, Qian Xu et al.
Deep kernel processes
Laurence Aitchison, Adam Yang, Sebastian W. Ober
Deep Latent Graph Matching
Tianshu Yu, Runzhong Wang, Junchi Yan et al.
Deep Learning for Functional Data Analysis with Adaptive Basis Layers
Junwen Yao, Jonas Mueller, Jane-Ling Wang
Deeply-Debiased Off-Policy Interval Estimation
Chengchun Shi, Runzhe Wan, Victor Chernozhukov et al.
DeepReDuce: ReLU Reduction for Fast Private Inference
Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg et al.
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity
Annie Xie, James Harrison, Chelsea Finn
DeepWalking Backwards: From Embeddings Back to Graphs
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos et al.
Delving into Deep Imbalanced Regression
Yuzhe Yang, Kaiwen Zha, Yingcong Chen et al.
Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation
Christopher R. Dance, Julien Perez, Théo Cachet
Demystifying Inductive Biases for (Beta-)VAE Based Architectures
Dominik Zietlow, Michal Rolinek, Georg Martius
Density Constrained Reinforcement Learning
Zengyi Qin, Yuxiao Chen, Chuchu Fan