"uncertainty quantification" Papers
113 papers found • Page 3 of 3
Conference
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla et al.
ICML 2024arXiv:2402.00809
60
citations
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
Thomas Pouplin, Alan Jeffares, Nabeel Seedat et al.
ICML 2024arXiv:2406.03258
7
citations
Second-Order Uncertainty Quantification: A Distance-Based Approach
Yusuf Sale, Viktor Bengs, Michele Caprio et al.
ICML 2024spotlightarXiv:2312.00995
33
citations
Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction
Jeffrey Wen, Rizwan Ahmad, Phillip Schniter
ECCV 2024arXiv:2405.18527
5
citations
T-Cal: An Optimal Test for the Calibration of Predictive Models
Donghwan Lee, Xinmeng Huang, Hamed Hassani et al.
ICML 2024arXiv:2203.01850
26
citations
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Ziquan Liu, Yufei Cui, Yan Yan et al.
ICML 2024arXiv:2405.08886
9
citations
TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression
Megh Shukla, Mathieu Salzmann, Alexandre Alahi
ICML 2024arXiv:2310.18953
5
citations
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction
Wei Qian, Chenxu Zhao, Yangyi Li et al.
AAAI 2024paperarXiv:2401.01549
10
citations
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
Shunsuke Horii, Yoichi Chikahara
AAAI 2024paperarXiv:2312.10435
6
citations
Uncertainty Regularized Evidential Regression
Kai Ye, Tiejin Chen, Hua Wei et al.
AAAI 2024paperarXiv:2401.01484
11
citations
Using AI Uncertainty Quantification to Improve Human Decision-Making
Laura Marusich, Jonathan Bakdash, Yan Zhou et al.
ICML 2024oralarXiv:2309.10852
12
citations
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
Chandra Mouli Sekar, Danielle Robinson, Shima Alizadeh et al.
ICML 2024
Winner-takes-all learners are geometry-aware conditional density estimators
Victor Letzelter, David Perera, Cédric Rommel et al.
ICML 2024arXiv:2406.04706
6
citations