"bayesian inference" Papers
31 papers found
Conference
Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler
Zixuan Hu, Li Shen, Zhenyi Wang et al.
Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment
Yuang Cai, Yuyu Yuan, Jinsheng Shi et al.
Bayesian Concept Bottleneck Models with LLM Priors
Jean Feng, Avni Kothari, Lucas Zier et al.
Bayesian scaling laws for in-context learning
Aryaman Arora, Dan Jurafsky, Christopher Potts et al.
Bayesian Test-Time Adaptation for Vision-Language Models
Lihua Zhou, Mao Ye, Shuaifeng Li et al.
Bi-Directional Communication-Efficient Stochastic FL via Remote Source Generation
Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh et al.
BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach
Haozhao Wang, Shengyu Wang, Jiaming Li et al.
Consensus-Driven Active Model Selection
Justin Kay, Grant Horn, Subhransu Maji et al.
ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction
Sankeerth Durvasula, Sharanshangar Muhunthan, Zain Moustafa et al.
Efficient Reinforcement Learning with Large Language Model Priors
Xue Yan, Yan Song, Xidong Feng et al.
Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
Julius Vetter, Manuel Gloeckler, Daniel Gedon et al.
Model-Informed Flows for Bayesian Inference
Joohwan Ko, Justin Domke
Modeling Neural Activity with Conditionally Linear Dynamical Systems
Victor Geadah, Amin Nejatbakhsh, David Lipshutz et al.
Multilevel neural simulation-based inference
Yuga Hikida, Ayush Bharti, Niall Jeffrey et al.
Parallelizing MCMC Across the Sequence Length
David Zoltowski, Skyler Wu, Xavier Gonzalez et al.
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
Ting Wei, Biao Mei, Junliang Lyu et al.
Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability
Po-Chen Kuo, Han Hou, Will Dabney et al.
Reverse Diffusion Sequential Monte Carlo Samplers
Luhuan Wu, Yi Han, Christian Andersson Naesseth et al.
Squared families are useful conjugate priors
Russell Tsuchida, Jiawei Liu, Cheng Soon Ong et al.
The Polynomial Stein Discrepancy for Assessing Moment Convergence
Narayan Srinivasan, Matthew Sutton, Christopher Drovandi et al.
Bayesian Detector Combination for Object Detection with Crowdsourced Annotations
Zhi Qin Tan, Olga Isupova, Gustavo Carneiro et al.
Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification
Luyang Fang, Yongkai Chen, Wenxuan Zhong et al.
Diffusive Gibbs Sampling
Wenlin Chen, Mingtian Zhang, Brooks Paige et al.
Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
Fabian Falck, Ziyu Wang, Christopher Holmes
Kernel Semi-Implicit Variational Inference
Ziheng Cheng, Longlin Yu, Tianyu Xie et al.
Listening to the noise: Blind Denoising with Gibbs Diffusion
David Heurtel-Depeiges, Charles Margossian, Ruben Ohana et al.
Logistic Variational Bayes Revisited
Michael Komodromos, Marina Evangelou, Sarah Filippi
Path-Guided Particle-based Sampling
Mingzhou Fan, Ruida Zhou, Chao Tian et al.
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning
Hyeong Kyu Choi, Sharon Li
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
Louis Sharrock, Jack Simons, Song Liu et al.
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
Shunsuke Horii, Yoichi Chikahara