conftrace_

posterior distribution

259 papers

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Co-occurring keywords

bayesian inference (1907) variational inference (2069) markov chain monte carlo (725) uncertainty quantification (1393) bayesian neural network (201) thompson sampling (237) gaussian process (1200) neural network (6616) kullback-leibler divergence (181) generative model (2892)

Papers

Learning-Augmented Ski Rental with Discrete Distribution: A Bayesian Approach AAAI 2026
BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow Generation EACL 2026
BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling AAAI 2026
The Curse of Conditions: Analyzing and Improving Optimal Transport for Conditional Flow-Based Generation ICCV 2025
Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning NAACL 2025
Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights JMLR 2025
Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood AAAI 2025
Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers JMLR 2025
TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing NIPS 2024
Uncertainty Visualization via Low-Dimensional Posterior Projections CVPR 2024
Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox NIPS 2024
Structural Inference of Dynamical Systems with Conjoined State Space Models NIPS 2024
Better-than-KL PAC-Bayes Bounds COLT 2024
On cyclical MCMC sampling AISTATS 2024
Direct Amortized Likelihood Ratio Estimation AAAI 2024
Approximate Bayesian Class-Conditional Models under Continuous Representation Shift AISTATS 2024
Estimation of partially known Gaussian graphical models with score-based structural priors AISTATS 2024
Data-Driven Knowledge-Aware Inference of Private Information in Continuous Double Auctions AAAI 2024
Subsampling Error in Stochastic Gradient Langevin Diffusions AISTATS 2024
Tensor-train methods for sequential state and parameter learning in state-space models JMLR 2024
Kermut: Composite kernel regression for protein variant effects NIPS 2024
On the Quantification of Image Reconstruction Uncertainty Without Training Data WACV 2024
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support AISTATS 2024
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs AISTATS 2024
On Truthing Issues in Supervised Classification JMLR 2024