"uncertainty estimation" Papers

AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking

Soyoung Yoon, Gyuwan Kim, Gyu-Hwung Cho et al.

Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations

Ao Zhou, Bin Liu, Jin Wang et al.

Calibrating LLMs with Information-Theoretic Evidential Deep Learning

Yawei Li, David Rügamer, Bernd Bischl et al.

Causal Discovery via Bayesian Optimization

Bao Duong, Sunil Gupta, Thin Nguyen

Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification

Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar et al.

Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation

Jeongin Kim, Wonho Bae, YouLee Han et al.

Do LLMs estimate uncertainty well in instruction-following?

Juyeon Heo, Miao Xiong, Christina Heinze-Deml et al.

DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation

Maregu Assefa, Muzammal Naseer, IYYAKUTTI IYAPPAN GANAPATHI et al.

Enhancing Deep Batch Active Learning for Regression with Imperfect Data Guided Selection

Yinjie Min, Furong Xu, Xinyao Li et al.

Enhancing Uncertainty Estimation and Interpretability with Bayesian Non-negative Decision Layer

XINYUE HU, Zhibin Duan, Bo Chen et al.

Epistemic Uncertainty for Generated Image Detection

Jun Nie, Yonggang Zhang, Tongliang Liu et al.

Forking Paths in Neural Text Generation

Eric Bigelow, Ari Holtzman, Hidenori Tanaka et al.

Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detection

Ruiyang Zhang, Hu Zhang, Zhedong Zheng

Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging

Xiaoling Hu, Karthik Gopinath, Peirong Liu et al.

Improving Uncertainty Estimation through Semantically Diverse Language Generation

Lukas Aichberger, Kajetan Schweighofer, Mykyta Ielanskyi et al.

MonoInstance: Enhancing Monocular Priors via Multi-view Instance Alignment for Neural Rendering and Reconstruction

Wenyuan Zhang, Yixiao Yang, Han Huang et al.

Multi-domain Distribution Learning for De Novo Drug Design

Arne Schneuing, Ilia Igashov, Adrian Dobbelstein et al.

Neural Context Flows for Meta-Learning of Dynamical Systems

Roussel Desmond Nzoyem, David Barton, Tom Deakin

Neural Stochastic Differential Equations for Uncertainty-Aware Offline RL

Cevahir Koprulu, Franck Djeumou, ufuk topcu

Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation

Sebastian Schmidt, Julius Koerner, Dominik Fuchsgruber et al.

Quantifying Uncertainty in the Presence of Distribution Shifts

Yuli Slavutsky, David Blei

REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability

Kristoffer K. Wickstrøm, Thea Brüsch, Michael C. Kampffmeyer et al.

Robust and Computation-Aware Gaussian Processes

Robust Sampling for Active Statistical Inference

Puheng Li, Tijana Zrnic, Emmanuel Candes

SEGA: Shaping Semantic Geometry for Robust Hashing under Noisy Supervision

Yiyang Gu, Bohan Wu, Qinghua Ran et al.

Self-Evolutionary Large Language Models Through Uncertainty-Enhanced Preference Optimization

Jianing Wang, Yang Zhou, Xiaocheng Zhang et al.

Test Time Scaling for Neural Processes

Hyungi Lee, Moonseok Choi, Hyunsu Kim et al.

Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It

Guoxuan Xia, Olivier Laurent, Gianni Franchi et al.

Training-Free Bayesianization for Low-Rank Adapters of Large Language Models

Haizhou Shi, Yibin Wang, Ligong Han et al.

U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening

Sungpyo Kim, Jeonghyeok Do, Jaehyup Lee et al.

Uncertainty and Influence aware Reward Model Refinement for Reinforcement Learning from Human Feedback

Zexu Sun, Yiju Guo, Yankai Lin et al.

Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models

Xiao Liang, Di Wang, Zhicheng Jiao et al.

Uncertainty-Informed Meta Pseudo Labeling for Surrogate Modeling with Limited Labeled Data

Xingyu Ren, Pengwei Liu, Pengkai Wang et al.

Uncertainty modeling for fine-tuned implicit functions

Anna Susmelj, Mael Macuglia, Natasa Tagasovska et al.

Uncertainty-Sensitive Privileged Learning

Fan-Ming Luo, Lei Yuan, Yang Yu

Uncertainty Weighted Gradients for Model Calibration

Jinxu Lin, Linwei Tao, Minjing Dong et al.

Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling

Guillem Capellera, Antonio Rubio, Luis Ferraz et al.

Vicinal Label Supervision for Reliable Aleatoric and Epistemic Uncertainty Estimation

Linye Li, Yufei Chen, Xiaodong Yue

Zero-shot Model-based Reinforcement Learning using Large Language Models

Abdelhakim Benechehab, Youssef Attia El Hili, Ambroise Odonnat et al.

A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models

Sebastian Gregor Gruber, Florian Buettner

Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory

Jonas Kälble, Sascha Wirges, Maxim Tatarchenko et al.

An Empirical Study Into What Matters for Calibrating Vision-Language Models

Weijie Tu, Weijian Deng, Dylan Campbell et al.

Bayesian Self-Training for Semi-Supervised 3D Segmentation

Ozan Unal, Christos Sakaridis, Luc Van Gool

cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process

Yihang Chen, TSAI HOR CHAN, Guosheng Yin et al.

Conformalized Adaptive Forecasting of Heterogeneous Trajectories

Yanfei Zhou, Lars Lindemann, Matteo Sesia

Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts

Ha Manh Bui, Anqi Liu

Depth-guided NeRF Training via Earth Mover’s Distance

Anita Rau, Josiah Aklilu, Floyd C Holsinger et al.

Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution

Johannes Zenn, Robert Bamler

Efficient Exploration for LLMs

Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao et al.

Enabling Uncertainty Estimation in Iterative Neural Networks

Nikita Durasov, Doruk Oner, Jonathan Donier et al.