Papers
Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classification
Yancheng Wang, Ping Li, Yingzhen Yang
Bandit Learning for Online Scheduling with Immediate Decision
Zilong Wang, Yuhao Zhang, Zhewei Wei et al.
On the Effect of Sampling Diversity in Scaling LLM Inference
Tianchun Wang, Yuanzhou Chen, Zichuan Liu et al.
From Moves to Paths: A Hierarchical Framework for Trajectory Representation Learning
Chundong Wang, Xiangtian Zheng, Qingbo Hao et al.
Recursive Fréchet Mean Estimation
Cheng Wang, Carlos J Soto
Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study
Kaizheng Wang, Yunjia Wang, Fabio Cuzzolin et al.
Learning Informative Attention Weights for Person Re-Identification
Yancheng Wang, Nebojsa Jojic, Yingzhen Yang
Upper entropy for 2-monotone lower probabilities
Tuan-Anh Vu, Sebastien Destercke, Frédéric Pichon
Collapse-Aware Regularization for Reliable Reasoning Under Distribution Shift
Quynh Vo, Cong-Duy T Nguyen
Markovian Compression: Looking to the Past Helps Accelerate the Future
Andrey Veprikov, Vladimir Solodkin, Mikhail Rudakov et al.
Structured Credal Learning
Varun Venkatesh, Eyke Hüllermeier, Bernd Bischl et al.
APIC: Amortized Physics-Informed Calibration using Neural Processes
Aishwarya Venkataramanan, Sai Karthikeya Vemuri, Joachim Denzler
Fisher8: Stabilizing Neural Heteroscedastic Regression via Output-Layer Fisher Geometry
Sumedh Vemuganti, Nickvash Kani
A Causal Markov Condition for Value
Olav Benjamin Vassend
Online Learning for Project Selection in Hedonic Project Games
Jaber Valizadeh, Dongmo Zhang, Omar Mubin et al.
Improving TabPFN’s Synthetic Data Generation by Integrating Causal Structure
Davide Tugnoli, Andrea De Lorenzo, Marco Virgolin et al.
Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions
Mame Diarra Toure, David A. Stephens
Benign Overfitting with Quantum Kernels
Joachim Tomasi, Sandrine Anthoine, Hachem Kadri
On Pairwise Quantile Regression - Statistical Guarantees and Applications
Romain Therezien, Stephan Clémençon, Fantin Girard et al.
EVIA: Entropic Variational Inference Auto-encoding
Yunfei Teng, Zhichao Chen, Xinyu Chen et al.
Positive-Unlabeled Regression: learning from partially labeled quantitative outcomes
Paweł Teisseyre, Jan Mielniczuk
Particle Dynamics for Latent-Variable Energy-Based Models
Shiqin Tang, Shuxin Zhuang, Runsheng Yu et al.
Deep Spectral Learning of Embedded Latent Transfer Operators for Stochastic Dynamical Systems
Ryogo Tanaka, Yoshinobu Kawahara