Papers
8,340 papers found
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum
Jigang Kim, Daesol Cho, H. Jin Kim
Demystifying Disagreement-on-the-Line in High Dimensions
Donghwan Lee, Behrad Moniri, Xinmeng Huang et al.
Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
He Zhang, Bang Wu, Shuo Wang et al.
Denoising MCMC for Accelerating Diffusion-Based Generative Models
Beomsu Kim, Jong Chul Ye
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
Liangbin Xie, Xintao Wang, Xiangyu Chen et al.
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky et al.
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score
Shuhai Zhang, Feng Liu, Jiahao Yang et al.
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
Ezgi Korkmaz, Jonah Brown-Cohen
Detecting Out-of-distribution Data through In-distribution Class Prior
Xue Jiang, Feng Liu, Zhen Fang et al.
Deterministic equivalent and error universality of deep random features learning
Dominik Schröder, Hugo Cui, Daniil Dmitriev et al.
DevFormer: A Symmetric Transformer for Context-Aware Device Placement
Haeyeon Kim, Minsu Kim, Federico Berto et al.
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation
Andi Peng, Aviv Netanyahu, Mark K Ho et al.
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
Tomoya Murata, Taiji Suzuki
Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding
Caizhi Tang, Huiyuan Wang, Xinyu Li et al.
Difference of submodular minimization via DC programming
Marwa El Halabi, George Orfanides, Tim Hoheisel
Differentiable and Transportable Structure Learning
Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie et al.
Differentiable Multi-Target Causal Bayesian Experimental Design
Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova et al.
Differentiable Simulations for Enhanced Sampling of Rare Events
Martin Sipka, Johannes C. B. Dietschreit, Lukáš Grajciar et al.
Differentiable Tree Operations Promote Compositional Generalization
Paul Soulos, Edward J Hu, Kate Mccurdy et al.
Differentially Private Distributed Bayesian Linear Regression with MCMC
Baris Alparslan, Sinan Yıldırım, Ilker Birbil
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury et al.
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees
Jacob Imola, Alessandro Epasto, Mohammad Mahdian et al.
Differentially Private Optimization on Large Model at Small Cost
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha et al.
Differentially Private Sharpness-Aware Training
Jinseong Park, Hoki Kim, Yujin Choi et al.
Differentially Private Stochastic Convex Optimization under a Quantile Loss Function
Du Chen, Geoffrey A. Chua