2025 Poster "uncertainty quantification" Papers
21 papers found
Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space
Mengjing Wu, Junyu Xuan, Jie Lu
CBMA: Improving Conformal Prediction through Bayesian Model Averaging
Pankaj Bhagwat, Linglong Kong, Bei Jiang
Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion
Minkyoung Cho, Yulong Cao, Jiachen Sun et al.
Conformal Linguistic Calibration: Trading-off between Factuality and Specificity
Zhengping Jiang, Anqi Liu, Ben Van Durme
Contextual Thompson Sampling via Generation of Missing Data
Kelly W Zhang, Tianhui Cai, Hongseok Namkoong et al.
Distribution-Free Data Uncertainty for Neural Network Regression
Domokos M. Kelen, Ádám Jung, Péter Kersch et al.
Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
Xu Wan, Chao Yang, Cheng Yang et al.
Gaussian Approximation and Concentration of Constant Learning-Rate Stochastic Gradient Descent
Ziyang Wei, Jiaqi Li, Zhipeng Lou et al.
Infinite Neural Operators: Gaussian processes on functions
Daniel Augusto de Souza, Yuchen Zhu, Jake Cunningham et al.
Knowledge Distillation of Uncertainty using Deep Latent Factor Model
Sehyun Park, Jongjin Lee, Yunseop Shin et al.
Neurosymbolic Diffusion Models
Emile van Krieken, Pasquale Minervini, Edoardo Maria Ponti et al.
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
Ting Wei, Biao Mei, Junliang Lyu et al.
Probabilistic Reasoning with LLMs for Privacy Risk Estimation
Jonathan Zheng, Alan Ritter, Sauvik Das et al.
ProDAG: Projected Variational Inference for Directed Acyclic Graphs
Ryan Thompson, Edwin Bonilla, Robert Kohn
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning
Yan Scholten, Stephan Günnemann
Statistical Inference for Gradient Boosting Regression
Haimo Fang, Kevin Tan, Giles Hooker
Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation
Gianni Franchi, Nacim Belkhir, Dat NGUYEN et al.
Uncertainty Estimation by Flexible Evidential Deep Learning
Taeseong Yoon, Heeyoung Kim
Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
Richard Bergna, Sergio Calvo Ordoñez, Felix Opolka et al.
Uncertainty Quantification with the Empirical Neural Tangent Kernel
Joseph Wilson, Chris van der Heide, Liam Hodgkinson et al.
Valid Conformal Prediction for Dynamic GNNs
Ed Davis, Ian Gallagher, Daniel Lawson et al.