Papers
DISCO: Adversarial Defense with Local Implicit Functions
Chih-Hui Ho, Nuno Vasconcelos
Discovered Policy Optimisation
Chris Lu, Jakub Kuba, Alistair Letcher et al.
Discovering and Overcoming Limitations of Noise-engineered Data-free Knowledge Distillation
Piyush Raikwar, Deepak Mishra
Discovering Design Concepts for CAD Sketches
Yuezhi Yang, Hao Pan
Discovery of Single Independent Latent Variable
Uri Shaham, Jonathan Svirsky, Ori Katz et al.
Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Riashat Islam, Hongyu Zang, Anirudh Goyal et al.
Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions
Shinsaku Sakaue, Taihei Oki
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
Olivier Jeunen, Ciarán Gilligan-Lee, Rishabh Mehrotra et al.
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald
Disentangling Transfer in Continual Reinforcement Learning
Maciej Wolczyk, Michał Zając, Razvan Pascanu et al.
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network
Giyoung Jeon, Haedong Jeong, Jaesik Choi
Distilling Representations from GAN Generator via Squeeze and Span
Yu Yang, Xiaotian Cheng, Chang Liu et al.
Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs
Julia Costacurta, Lea Duncker, Blue Sheffer et al.
Distinguishing Learning Rules with Brain Machine Interfaces
Jacob Portes, Christian Schmid, James M Murray
Distributed Distributionally Robust Optimization with Non-Convex Objectives
Yang Jiao, Kai Yang, Dongjin Song
Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems
Miguel Suau, Jinke He, Mustafa Mert Çelikok et al.
Distributed Inverse Constrained Reinforcement Learning for Multi-agent Systems
Shicheng Liu, Minghui Zhu
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Aleksandr Beznosikov, Peter Richtarik, Michael Diskin et al.
Distributed Online Convex Optimization with Compressed Communication
Zhipeng Tu, Xi Wang, Yiguang Hong et al.
Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity
Bingqing Song, Ioannis Tsaknakis, Chung-Yiu Yau et al.
Distributional Convergence of the Sliced Wasserstein Process
Jiaqi Xi, Jonathan Niles-Weed
Distributionally Adaptive Meta Reinforcement Learning
Anurag Ajay, Abhishek Gupta, Dibya Ghosh et al.
Distributionally Robust Optimization via Ball Oracle Acceleration
Yair Carmon, Danielle Hausler
Distributionally Robust Optimization with Data Geometry
Jiashuo Liu, Jiayun Wu, Bo Li et al.