NeurIPS 2024PastAgents
NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty
NeurIPS BDU Workshop 2024
- Submission deadline
- Sep 6, 2024, 12:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (107)
Fetched from OpenReview (v2) on 2026-06-10.
(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models
A Bayesian Approach Towards Crowdsourcing the Truths from LLMs
A Fast, Robust Elliptical Slice Sampling Method for Truncated Multivariate Normal Distributions
A scalable Bayesian continual learning framework for online and sequential decision making
Active Learning for Affinity Prediction of Antibodies
Active Learning for Optimal Minimization of Experimental Characterization Uncertainty
Adaptive Transductive Inference via Sequential Experimental Design with Contextual Retention
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Amortized Bayesian Workflow (Extended Abstract)
Amortized Decision-Aware Bayesian Experimental Design
An Active Learning Performance Model for Parallel Bayesian Calibration of Expensive Simulations
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits
Atomic Layer Deposition Optimization via Targeted Adaptive Design.
BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories
Bayesian Nonparametric Learning using the Maximum Mean Discrepancy Measure for Synthetic Data Generation
Bayesian Optimal Experimental Design of Streaming Data Incorporating Machine Learning Generated Synthetic Data
Bayesian Optimization for High-dimensional Urban Mobility Problems
Bayesian Optimization of High-dimensional Outputs with Human Feedback
Bayesian Optimization over Bounded Domains with Beta Product Kernels
Bayesian Outcome Weighted Learning
Bayesian Rashomon Sets for Model Uncertainty: A critical comparison
Big Batch Bayesian Active Learning by Considering Predictive Probabilities
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings
Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder
Cold Posterior Effect towards Adversarial Robustness
Computation-Aware Robust Gaussian Processes
Computationally Efficient Laplace Approximations for Neural Networks
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series
Constrained Multi-objective Bayesian Optimization
Convergence Rates of Bayesian Network Policy Gradient for Cooperative Multi-Agent Reinforcement Learning
Cost-effective Reduced-Order Modeling via Bayesian Active Learning
Data-Efficient Variational Mutual Information Estimation via Bayesian Self-Consistency
Decision-Driven Calibration for Cost-Sensitive Uncertainty Quantification
Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization
Direct Acquisition Optimization for Low-Budget Active Learning
Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Efficient Bayesian Additive Regression Models For Microbiome Studies
Efficient Experimentation for Estimation of Continuous and Discrete Conditional Treatment Effects
Efficient Local Unlearning for Gaussian Processes with Out-of-Distribution Data
Efficient Modeling of Irregular Time-Series with Stochastic Optimal Control
Ensemble Mashups: A Simple Recipe For Better Bayesian Optimization
Exploring and Addressing Reward Confusion in Offline Preference Learning
Failure Prediction from Few Expert Demonstrations
Fast, Precise Thompson Sampling for Bayesian Optimization
Finding Interior Optimum of Black-box Constrained Objective with Bayesian Optimization
Gaussian Process Conjoint Analysis for Adaptive Marginal Effect Estimation
Gaussian Process Thompson Sampling via Rootfinding
Gaussian Randomized Exploration for Semi-bandits with Sleeping Arms
GLEAM-AI: Neural Surrogate for Accelerated Epidemic Analytics and Forecasting
Gradient-free variational learning with conditional mixture networks
Graph Agnostic Causal Bayesian Optimisation
Graph Classification Gaussian Processes via Hodgelet Spectral Features
Had enough of experts? Elicitation and evaluation of Bayesian priors from large language models
Hi-fi functional priors by learning activations
Higher Uncertainty Leads to Less Exploration in a Combinatorial Discovery Game
Improved Depth Estimation of Bayesian Neural Networks
Incentivized Exploration in Two-sided Matching Markets
Incremental Uncertainty-aware Performance Monitoring with Labeling Intervention
Information Directed Tree Search: Reasoning and Planning with Language Agents
Integration-free kernels for equivariant Gaussian fields with application in dipole moment prediction
Inverse-Free Sparse Variational Gaussian Processes
Latent Spatial Dirichlet Allocation
Learning from Less: Bayesian Neural Networks for Optimization Proxy using Limited Labeled Data
Learning to Defer with an Uncertain Rejector via Conformal Prediction
Lightspeed Black-box Bayesian Optimization via Local Score Matching
Lithium-Ion Battery System Health Monitoring and Resistance-Based Fault Analysis from Field Data Using Recursive Spatiotemporal Gaussian Processes
MHP-DDP: Multivariate Hawkes Process with Dependent Dirichlet Process
Mode Collapse in Variational Deep Gaussian Processes
NODE-GAMLSS: Interpretable Uncertainty Modelling via Deep Distributional Regression
Optimizing Detection Time and Specificity: Early Classification of Time Series with Sensitivity Constraint
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness
Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy
Post-Calibration Techniques: Balancing Calibration and Score Distribution Alignment
Posterior Inferred, Now What? Streamlining Prediction in Bayesian Deep Learning
Posterior Sampling via Autoregressive Generation
Practical Bayesian Algorithm Execution via Posterior Sampling
Preconditioned Crank-Nicolson Algorithms for Wide Bayesian Neural Networks
Preference-based Multi-Objective Bayesian Optimization with Gradients
Probabilistic Active Few-Shot Learning in Vision-Language Models
Probabilistic Fusion Approach for Robust Battery Prognostics
Probabilistic predictions with Fourier neural operators
Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design
Rethinking Aleatoric and Epistemic Uncertainty
Riemannian Black Box Variational Inference
Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition
ROSA: An Optimization Algorithm for Multi-Modal Derivative-Free Functions in High Dimensions
Scalable Permutation Invariant Multi-Output Gaussian Processes for Cancer Drug Response
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure
Spectral structure learning for clinical time series
Stochastic Gradient MCMC for Gaussian Process Inference on Massive Geostatistical Data
The Importance of Being Bayesian in Online Conformal Prediction
The role of tail dependence in estimating posterior expectations
Toward Information Theoretic Active Inverse Reinforcement Learning
TP$^2$DP$^2$: A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Two Students: Enabling Uncertainty Quantification in Federated Learning Clients
Uncertainty as a criterion for SOTIF evaluation of deep learning models in autonomous driving systems
Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
Uncertainty Quantification and Calibration for Audio-driven Disease Diagnosis
Universal Functional Regression with Neural Operator Flows
Using Rashomon Sets for Robust Active Learning
Variational Bayes Gaussian Splatting
Variational Inference for Interacting Particle Systems with Discrete Latent States
Variational Inference in Similarity Spaces: A Bayesian Approach to Personalized Federated Learning
Variational Last Layers for Bayesian Optimization
Variational Search Distributions