Papers Bayesian Inference
“Bayesian Inference” 태그가 달린 논문 2,612편 · 필터 해제
Personalized Causal Recourse: A Human-In-The-Loop Approach
Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios. Traditional approaches to recourse oft…
Bayesian InferenceAsk the Right Comparison:Bias-Aware Bayesian Active Top-$k$ Ranking with LLM Judges
Large language models (LLMs) are increasingly used as cheap, scalable judges that compare candidate outputs pairwise -- to rank responses, select models, or triage papers. Yet LLM judges are both noisy and systematically…
Bayesian InferenceFull Bayesian Reinforcement Learning via LF-IBIS
Reinforcement Learning (RL) is a sequential decision-making framework in which an agent learns optimal policies through interaction with an environment by maximizing cumulative rewards. Among RL methods, Bayesian Reinfor…
Reinforcement LearningBayesian InferenceThe CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models
This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automating complex analysis workflows, with fundamental investment analysis a…
Bayesian InferenceActive LearningReduction of Probabilistic Chemical Reaction Networks
Programming adaptive behaviors at the cellular level is a long-standing goal that raises the question of how probabilistic computation can be implemented in biochemical systems. Chemical reaction networks (CRNs) provide …
Bayesian InferenceBeyond Global Divergences: A Local-Mass Perspective on Bayesian Inference
Global objectives, such as KL divergence and ELBO, are widely used in Bayesian inference for measuring distributional discrepancy. This paper studies their local-mass behaviour that is not directly captured by such objec…
Bayesian Inference\chisao{}: A GPU-Native Parallel Optimizer for Multimodal Black-Box Functions via Convergence-Anticonvergence Oscillation
Finding all modes of a multimodal black-box function is a fundamental challenge in optimization, Bayesian inference, and scientific computing. Existing approaches -- basin-hopping, CMA-ES, multistart gradient descent -- …
Bayesian InferenceTaxonomy-aware deep learning for hierarchical marine species classification in underwater imagery
Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Existing approaches struggle with severe domain shift across collection …
Bayesian InferenceHierarchical Partial-Order Models for Ranking
Rank aggregation combines information from ordered lists ranking items by preference. Classical parametric models for such data, including the Mallows and Plackett-Luce models, assume the orders concentrate around one or…
Bayesian InferenceEfficient Analytic Uncertainty Quantification for Multi-Modal Regression
Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate under the assumption that the conditional label marginal satisfies sing…
Bayesian InferenceActive LearningActive Inference as the Test-Time Scaling Law for Physical AI Agents
In this paper, a novel test-time scaling law for physical artificial intelligence (AI) agents is introduced. This scaling law enables physical AI agents to reason with their world models to generalize in unforeseen scena…
Reinforcement LearningBayesian InferenceAutonomous DrivingBayesian model selection of vine copulas: a loss-based perspective
The growing popularity of vine copulas in multivariate statistical analysis is largely driven by their ability to capture complex dependence structures. However, this flexibility comes at a cost, as the number of possibl…
Bayesian InferenceBayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network
Accurate 3D seismic velocity modeling through seismic travel-time tomography using both active- and passive-source data provides critical underpinning models for seismicity monitoring and hazard assessment. Because trave…
Bayesian InferenceNeural network surrogates with uncertainty quantification for inverse problems in partial differential equations
Inverse problems for differential equations arise throughout science and engineering, where one seeks to infer unknown model parameters from noisy or incomplete observations. Traditional numerical methods for these probl…
Bayesian InferenceFrom Drift to Coherence: Stabilizing Beliefs in LLMs
Large language models (LLMs) are often hypothesized to perform implicit Bayesian inference, yet a key coherence condition, the martingale property of predictive beliefs, has been shown to fail in controlled synthetic in-…
Bayesian InferenceQuestion AnsweringStop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
Sampling from complex, unnormalized probability densities is a fundamental challenge in Bayesian inference and probabilistic modeling. While Markov chain Monte Carlo (MCMC) methods provide asymptotic guarantees, they oft…
Bayesian InferenceBayesian Inference and Decision Audits for Public Archives of Frontier AI Evaluations
Public AI evaluations are often read as terminal leaderboards, yet the underlying evidence is a selective time series shaped by reporting rules, benchmark revisions, and missingness. Repeated public archives for LiveBenc…
Bayesian InferenceTrustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Models
We propose a novel Bayesian framework for joint image reconstruction and uncertainty quantification from compressed sensing magnetic resonance imaging data. The problem is formulated as a linear inverse problem, where pr…
Image ReconstructionBayesian InferenceMRI ReconstructionA Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt
Test-time training (TTT) adapts a pretrained model to each prompt via parameter updates, improving accuracy under pretraining-to-test distribution shifts. Yet, its performance often suffers from instability and sensitivi…
Bayesian InferenceAmortized mean-shift interacting particles
Bayesian inference for inverse problems is run to evaluate integrals -- posterior expectations, tail probabilities, and risks -- across a stream of observations. The standard estimate averages the integrand over posterio…
Bayesian Inference