Papers
Type Information-Assisted Self-Supervised Knowledge Graph Denoising
Jiaqi Sun, Yujia Zheng, Xinshuai Dong et al.
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs
Enea Monzio Compagnoni, Rustem Islamov, Frank Norbert Proske et al.
Unbiased Quantization of the $L_1$ Ball for Communication-Efficient Distributed Mean Estimation
Nithish Suresh Babu, Ritesh Kumar, Shashank Vatedka
Unconditionally Calibrated Priors for Beta Mixture Density Networks
Alix Lhéritier, Maurizio Filippone
Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts
Fanqi Yan, Huy Nguyen, Le Quang Dung et al.
Understanding GNNs and Homophily in Dynamic Node Classification
Michael Ito, Danai Koutra, Jenna Wiens
Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality
Ruijia Zhang, Siliang Zeng, Chenliang Li et al.
Understanding the Effect of GCN Convolutions in Regression Tasks
Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat et al.
Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization
Ziqing Xu, Hancheng Min, Lachlan Ewen MacDonald et al.
UNHaP: Unmixing Noise from Hawkes Processes
Virginie Loison, Guillaume Staerman, Thomas Moreau
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier et al.
Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory
Lucas Gnecco Heredia, Matteo Sammut, Muni Sreenivas Pydi et al.
Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits
Ha Manh Bui, Enrique Mallada, Anqi Liu
Variance-Dependent Regret Bounds for Nonstationary Linear Bandits
Zhiyong Wang, Jize Xie, Yi Chen et al.
Variational Adversarial Training Towards Policies with Improved Robustness
Juncheng Dong, Hao-Lun Hsu, Qitong Gao et al.
Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space
Alex Chen, Philippe Chlenski, Kenneth Munyuza et al.
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix
Charles Margossian, Lawrence K. Saul
Variational Inference on the Boolean Hypercube with the Quantum Entropy
Eliot Beyler, Francis Bach
Variational Schrödinger Momentum Diffusion
Kevin Rojas, Yixin Tan, Molei Tao et al.
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning Uncertainty
James McInerney, Nathan Kallus
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
Felix Jimenez, Matthias Katzfuss
Visualizing token importance for black-box language models
Paulius Rauba, Qiyao Wei, Mihaela van der Schaar
Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context
Francesco Micheli, Efe C. Balta, Anastasios Tsiamis et al.
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
Dai Hai Nguyen, Tetsuya Sakurai, Hiroshi Mamitsuka
Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models
Mingyu Pu, Wang Songhao, Haowei Wang et al.