← Optimization & Theory
Machine Learning › Optimization & Theory ›

Bayesian Inference

4821 directly classified papers

Papers per year

Papers

Diffusion Models for Attribution AAAI 2025
A Systematic Examination of Preference Learning through the Lens of Instruction-Following NAACL 2025
A Bayesian Optimization Approach to Machine Translation Reranking NAACL 2025
A Fully Probabilistic Perspective on Large Language Model Unlearning: Evaluation and Optimization EMNLP 2025
Bidirectional Human–AI Collaboration for Equitable Student Performance Prediction via Deep Uncertainty Learning IJCAI 2025
Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models IJCAI 2025
Stochasticity-aware No-Reference Point Cloud Quality Assessment IJCAI 2025
Relation-Augmented Dueling Bayesian Optimization via Preference Propagation IJCAI 2025
Weight-Aware Activation Sparsity with Constrained Bayesian Optimization Scheduling for Large Language Models EMNLP 2025
Distribution-Free Uncertainty Quantification in Mechanical Ventilation Treatment: A Conformal Deep Q-Learning Framework AAAI 2025
Approximate Bilevel Difference Convex Programming for Bayesian Risk Markov Decision Processes AAAI 2025
Backward Filtering Forward Guiding JMLR 2025
Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks JMLR 2025
Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity JMLR 2025
How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences JMLR 2025
Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers JMLR 2025
Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights JMLR 2025
Bayesian Data Sketching for Varying Coefficient Regression Models JMLR 2025
Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights JMLR 2025
Modelling Populations of Interaction Networks via Distance Metrics JMLR 2025
Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models JMLR 2025
Regularized Rényi Divergence Minimization through Bregman Proximal Gradient Algorithms JMLR 2025
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs JMLR 2025
Inferring Change Points in High-Dimensional Regression via Approximate Message Passing JMLR 2025
Graph-accelerated Markov Chain Monte Carlo using Approximate Samples JMLR 2025