Poster "uncertainty quantification" Papers
87 papers found • Page 1 of 2
Conference
A Generic Framework for Conformal Fairness
Aditya Vadlamani, Anutam Srinivasan, Pranav Maneriker et al.
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation
Da Long, Zhitong Xu, Guang Yang et al.
Architectural and Inferential Inductive Biases for Exchangeable Sequence Modeling
Daksh Mittal, Leon Li, Thomson Yen et al.
Bayesian Concept Bottleneck Models with LLM Priors
Jean Feng, Avni Kothari, Lucas Zier et al.
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 Information Pursuit for Interactively Guiding Large Language Models
Kwan Ho Ryan Chan, Yuyan Ge, Edgar Dobriban et al.
Conformal Linguistic Calibration: Trading-off between Factuality and Specificity
Zhengping Jiang, Anqi Liu, Ben Van Durme
Conformal Prediction for Time-series Forecasting with Change Points
Sophia Sun, Rose Yu
Conformal Prediction in The Loop: A Feedback-Based Uncertainty Model for Trajectory Optimization
Han Wang, Chao Ning
Conformal Prediction Sets Can Cause Disparate Impact
Jesse Cresswell, Bhargava Kumar, Yi Sui et al.
Contextual Thompson Sampling via Generation of Missing Data
Kelly W Zhang, Tianhui Cai, Hongseok Namkoong et al.
Debiasing Mini-Batch Quadratics for Applications in Deep Learning
Lukas Nicola Tatzel, Bálint Mucsányi, Osane Hackel et al.
Direct Prediction Set Minimization via Bilevel Conformal Classifier Training
Yuanjie Shi, Hooman Shahrokhi, Xuesong Jia et al.
Distribution-Free Data Uncertainty for Neural Network Regression
Domokos M. Kelen, Ádám Jung, Péter Kersch et al.
Enhancing Vision-Language Model Reliability with Uncertainty-Guided Dropout Decoding
Yixiong Fang, Ziran Yang, Zhaorun Chen et al.
Evidential Knowledge Distillation
Liangyu Xiang, Junyu Gao, Changsheng Xu
Exploiting the Asymmetric Uncertainty Structure of Pre-trained VLMs on the Unit Hypersphere
Li Ju, Max Andersson, Stina Fredriksson et al.
Exploring the Noise Robustness of Online Conformal Prediction
HuaJun Xi, Kangdao Liu, Hao Zeng et al.
From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
Nikita Kotelevskii, Vladimir Kondratyev, Martin Takáč 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.
Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks
Debargha Ganguly, Vikash Singh, Sreehari Sankar et al.
Handling Missing Responses under Cluster Dependence with Applications to Language Model Evaluation
Zhenghao Zeng, David Arbour, Avi Feller et al.
Image Super-Resolution with Guarantees via Conformalized Generative Models
Eduardo Adame, Daniel Csillag, Guilherme Tegoni Goedert
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.
Learning Normal Flow Directly From Events
Dehao Yuan, Levi Burner, Jiayi Wu et al.
Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
Emanuel Sommer, Jakob Robnik, Giorgi Nozadze 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.
POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality
Joey Wilson, Marcelino M. de Almeida, Sachit Mahajan 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
Quantifying and Narrowing the Unknown: Interactive Text-to-Video Retrieval via Uncertainty Minimization
Bingqing Zhang, Zhuo Cao, Heming Du et al.
Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inference
Mizuki Niihori, Shuichi Nishino, Teruyuki Katsuoka et al.
Rethinking Approximate Gaussian Inference in Classification
Bálint Mucsányi, Nathaël Da Costa, Philipp Hennig
Revisiting Source-Free Domain Adaptation: a New Perspective via Uncertainty Control
Gezheng Xu, Hui GUO, Li Yi et al.
Solving and Learning Partial Differential Equations with Variational Q-Exponential Processes
Guangting Yu, Shiwei Lan
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.
Variational Polya Tree
Lu Xu, Tsai Hor Chan, Lequan Yu et al.
ViLU: Learning Vision-Language Uncertainties for Failure Prediction
Marc Lafon, Yannis Karmim, Julio Silva-Rodríguez et al.
A Bayesian Approach to Online Planning
Nir Greshler, David Ben Eli, Carmel Rabinovitz et al.