conftrace_

predictive distribution

39 papers

Explore in graph

Co-occurring keywords

bayesian inference (1907) uncertainty quantification (1393) gaussian process (1200) variational inference (2069) uncertainty estimation (648) large language model (13587) gaussian process regression (133) neural network (6616) bayesian optimization (624) posterior distribution (259)

Papers

DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs ACL 2026
Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens ACL 2026
Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization JMLR 2025
Quantile Regression with Large Language Models for Price Prediction ACL 2025
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation AISTATS 2024
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language NIPS 2024
Probabilistic Calibration by Design for Neural Network Regression AISTATS 2024
Quasi-Bayes meets Vines NIPS 2024
Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees NIPS 2024
Maximum likelihood estimation in Gaussian process regression is ill-posed JMLR 2023
GFlowOut: Dropout with Generative Flow Networks ICML 2023
Generative Calibration for In-context Learning EMNLP 2023
Estimating Regression Predictive Distributions with Sample Networks AAAI 2023
Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates UAI 2023
Kalman Bayesian Neural Networks for Closed-Form Online Learning AAAI 2023
A Predictive Approach to Bayesian Nonparametric Survival Analysis AISTATS 2022
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime AISTATS 2022
Evaluating high-order predictive distributions in deep learning UAI 2022
Predictive variational Bayesian inference as risk-seeking optimization AISTATS 2022
The Neural Testbed: Evaluating Joint Predictions NIPS 2022
Uncertainty-aware sensitivity analysis using Rényi divergences UAI 2021
Estimating Uncertainty Intervals from Collaborating Networks JMLR 2021
Multi-class Gaussian Process Classification with Noisy Inputs JMLR 2021
On the Expressiveness of Approximate Inference in Bayesian Neural Networks NIPS 2020
Parametric Gaussian Process Regressors ICML 2020