Papers
Distributionally robust weighted k-nearest neighbors
Shixiang Zhu, Liyan Xie, Minghe Zhang et al.
Distributional Reinforcement Learning for Risk-Sensitive Policies
Shiau Hong Lim, ILYAS MALIK
Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning
Jifeng Hu, Yanchao Sun, Hechang Chen et al.
Distribution-Informed Neural Networks for Domain Adaptation Regression
Jun Wu, Jingrui He, Sheng Wang et al.
DivBO: Diversity-aware CASH for Ensemble Learning
Yu Shen, Yupeng Lu, Yang Li et al.
Diverse Weight Averaging for Out-of-Distribution Generalization
Alexandre Rame, Matthieu Kirchmeyer, Thibaud Rahier et al.
Diversified Recommendations for Agents with Adaptive Preferences
William Brown, Arpit Agarwal
Diversity vs. Recognizability: Human-like generalization in one-shot generative models
Victor Boutin, Lakshya Singhal, Xavier Thomas et al.
Divert More Attention to Vision-Language Tracking
Mingzhe Guo, Zhipeng Zhang, Heng Fan et al.
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
Ziyi Zhang, Weikai Chen, Hui Cheng et al.
DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body
Alberto Silvio Chiappa, Alessandro Marin Vargas, Alexander Mathis
DNA: Proximal Policy Optimization with a Dual Network Architecture
Matthew Aitchison, Penny Sweetser
Do Current Multi-Task Optimization Methods in Deep Learning Even Help?
Derrick Xin, Behrooz Ghorbani, Justin Gilmer et al.
Does GNN Pretraining Help Molecular Representation?
Ruoxi Sun, Hanjun Dai, Adams Wei Yu
Does Momentum Change the Implicit Regularization on Separable Data?
Bohan Wang, Qi Meng, Huishuai Zhang et al.
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
Xiang Li, Jinghuan Shang, Srijan Das et al.
Domain Adaptation meets Individual Fairness. And they get along.
Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin et al.
Domain Adaptation under Open Set Label Shift
Saurabh Garg, Sivaraman Balakrishnan, Zachary Lipton
Domain Generalization by Learning and Removing Domain-specific Features
Yu Ding, Lei Wang, Bin Liang et al.
Domain Generalization without Excess Empirical Risk
Ozan Sener, Vladlen Koltun
DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
Yao Mu, Yuzheng Zhuang, Fei Ni et al.
Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation
Ziwei Xu, Yogesh Rawat, Yongkang Wong et al.
Don't Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond
Georgios Amanatidis, Georgios Birmpas, Aris Filos-Ratsikas et al.
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil et al.
Do Residual Neural Networks discretize Neural Ordinary Differential Equations?
Michael Sander, Pierre Ablin, Gabriel Peyré