Papers
Discovery of Options via Meta-Learned Subgoals
Vivek Veeriah, Tom Zahavy, Matteo Hessel et al.
Discrete-Valued Neural Communication
Dianbo Liu, Alex M Lamb, Kenji Kawaguchi et al.
Disentangled Contrastive Learning on Graphs
Haoyang Li, Xin Wang, Ziwei Zhang et al.
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy et al.
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect
Lorenzo Noci, Kevin Roth, Gregor Bachmann et al.
Disrupting Deep Uncertainty Estimation Without Harming Accuracy
Ido Galil, Ran El-Yaniv
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Yifei Wang, Yisen Wang, Jiansheng Yang et al.
Distilling Image Classifiers in Object Detectors
Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
Yiyue Qian, Yiming Zhang, Yanfang (Fa Ye et al.
Distilling Object Detectors with Feature Richness
Du Zhixing, Rui Zhang, Ming Chang et al.
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
Junho Kim, Byung-Kwan Lee, Yong Man Ro
Distributed Deep Learning In Open Collaborations
Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin et al.
Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition
Jayadev Acharya, Clement Canonne, Yuhan Liu et al.
Distributed Machine Learning with Sparse Heterogeneous Data
Dominic Richards, Sahand Negahban, Patrick Rebeschini
Distributed Principal Component Analysis with Limited Communication
Foivos Alimisis, Peter Davies, Bart Vandereycken et al.
Distributed Saddle-Point Problems Under Data Similarity
Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin et al.
Distributed Zero-Order Optimization under Adversarial Noise
Arya Akhavan, Massimiliano Pontil, Alexandre Tsybakov
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models
Lenart Treven, Philippe Wenk, Florian Dorfler et al.
Distributionally Robust Imitation Learning
Mohammad Ali Bashiri, Brian Ziebart, Xinhua Zhang
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
Pushi Zhang, Xiaoyu Chen, Li Zhao et al.
Distribution-free inference for regression: discrete, continuous, and in between
Yonghoon Lee, Rina Barber
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals
Lang Liu, Krishna Pillutla, Sean Welleck et al.
Diverse Message Passing for Attribute with Heterophily
Liang Yang, Mengzhe Li, Liyang Liu et al.
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules
Wei Tan, Lan Du, Wray Buntine
Diversity Matters When Learning From Ensembles
Giung Nam, Jongmin Yoon, Yoonho Lee et al.