"uncertainty quantification" Papers
62 papers found • Page 2 of 2
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla et al.
ICML 2024poster
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
Thomas Pouplin, Alan Jeffares, Nabeel Seedat et al.
ICML 2024poster
Second-Order Uncertainty Quantification: A Distance-Based Approach
Yusuf Sale, Viktor Bengs, Michele Caprio et al.
ICML 2024spotlight
T-Cal: An Optimal Test for the Calibration of Predictive Models
Donghwan Lee, Xinmeng Huang, Hamed Hassani et al.
ICML 2024poster
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Ziquan Liu, Yufei Cui, Yan Yan et al.
ICML 2024poster
TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression
Megh Shukla, Mathieu Salzmann, Alexandre Alahi
ICML 2024poster
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 2024oral
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
Chandra Mouli Sekar, Danielle Robinson, Shima Alizadeh et al.
ICML 2024poster
Winner-takes-all learners are geometry-aware conditional density estimators
Victor Letzelter, David Perera, Cédric Rommel et al.
ICML 2024poster