Papers
Differentiable Top-k Classification Learning
Felix Petersen, Hilde Kuehne, Christian Borgelt et al.
Differentially Private Approximate Quantiles
Haim Kaplan, Shachar Schnapp, Uri Stemmer
Differentially Private Community Detection for Stochastic Block Models
Mohamed S Mohamed, Dung Nguyen, Anil Vullikanti et al.
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
Paul Mangold, Aurélien Bellet, Joseph Salmon et al.
Differentially Private Maximal Information Coefficients
John Lazarsfeld, Aaron Johnson, Emmanuel Adeniran
Diffusion bridges vector quantized variational autoencoders
Max Cohen, Guillaume Quispe, Sylvain Le Corff et al.
Diffusion Models for Adversarial Purification
Weili Nie, Brandon Guo, Yujia Huang et al.
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization
Dongruo Zhou, Quanquan Gu
Direct Behavior Specification via Constrained Reinforcement Learning
Julien Roy, Roger Girgis, Joshua Romoff et al.
Directed Acyclic Transformer for Non-Autoregressive Machine Translation
Fei Huang, Hao Zhou, Yang Liu et al.
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
Aviv Netanyahu, Tianmin Shu, Joshua Tenenbaum et al.
Discrete Probabilistic Inverse Optimal Transport
Wei-Ting Chiu, Pei Wang, Patrick Shafto
Discrete Tree Flows via Tree-Structured Permutations
Mai Elkady, Jim Lim, David I. Inouye
Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
Haoran Xu, Xianyuan Zhan, Honglei Yin et al.
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring
Zhengquan Luo, Yunlong Wang, Zilei Wang et al.
Disentangling Disease-related Representation from Obscure for Disease Prediction
Chu-Ran Wang, Fei Gao, Fandong Zhang et al.
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
Kyunghwan Son, Junsu Kim, Sungsoo Ahn et al.
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training
Rong Dai, Li Shen, Fengxiang He et al.
Distinguishing rule and exemplar-based generalization in learning systems
Ishita Dasgupta, Erin Grant, Tom Griffiths
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
Harley E Wiltzer, David Meger, Marc G. Bellemare
Distributionally Robust $Q$-Learning
Zijian Liu, Qinxun Bai, Jose Blanchet et al.
Distribution Regression with Sliced Wasserstein Kernels
Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto
Divergence-Regularized Multi-Agent Actor-Critic
Kefan Su, Zongqing Lu
Diversified Adversarial Attacks based on Conjugate Gradient Method
Keiichiro Yamamura, Haruki Sato, Nariaki Tateiwa et al.