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