Papers
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
Differential Privacy has Bounded Impact on Fairness in Classification
Paul Mangold, Michaël Perrot, Aurélien Bellet et al.
Diffusion Based Representation Learning
Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer et al.
Diffusion Models are Minimax Optimal Distribution Estimators
Kazusato Oko, Shunta Akiyama, Taiji Suzuki
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
Victor Boutin, Thomas Fel, Lakshya Singhal et al.
Diffusion Models for Black-Box Optimization
Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover
Dimensionality Reduction for General KDE Mode Finding
Xinyu Luo, Christopher Musco, Cas Widdershoven
Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing
Jiaxiang Ren, Yang Zhou, Jiayin Jin et al.
Dink-Net: Neural Clustering on Large Graphs
Yue Liu, Ke Liang, Jun Xia et al.
Directed Chain Generative Adversarial Networks
Ming Min, Ruimeng Hu, Tomoyuki Ichiba