Abstract
Recent advances in ML and AI have led to impressive achievements, yet models often struggle to express uncertainty, and more importantly, make decisions that account for uncertainty. This hinders the deployment of AI models in critical applications, ranging from scientific discovery, where uncertainty quantification is essential, to real-world scenarios with unpredictable and dynamic environments, where models may encounter data vastly different from their training sets.
Through the use of probability, Bayesian methods offer a powerful framework to address these limitations by quantifying uncertainty, incorporating prior knowledge, enabling adaptive decision-making and information gathering in uncertain environments. These approaches have led to significant progress and success in relevant fields, tackling critical problems such as drug discovery, hyperparameter tuning and environmental monitoring. However, challenges remain in both theory and practice, such as establishing performance guarantees and scaling up these methods to handle the complexity and dimensionality of larger data and models. On the other hand, the development of frontier models (e.g., based on large language models) presents new opportunities to enhance Bayesian methods with stronger priors and tools not previously available.
This workshop aims to bring together researchers from different but closely related areas, including Bayesian optimization, active learning, uncertainty quantification, Gaussian processes, spatiotemporal modeling, and sequential experimental design. We seek to foster a vibrant exchange of ideas, showcase successful applications, and prompt fruitful discussion to collaboratively tackle the emerging challenges and shape the future directions of Bayesian decision-making and uncertainty in the new era of ML and AI.
Speakers
Schedule
| Time (Canada) | Event |
|---|---|
| 09:00 - 09:30 | Introduction and Opening Remarks: Andreas Krause slides |
| 09:30 - 10:00 | Invited Talk: Mark van der Wilk - Open Problems in Gaussian Process Approximation and Benchmarking slides |
| 10:00 - 10:30 | Discussion Break |
| 10:30 - 11:00 | Invited Talk: Esther Rolf - We need to talk (more) about uncertainty in geospatial machine learning slides |
| 11:00 - 11:10 | Contributed Talk: Mathieu Alain - Graph Classification Gaussian Processes via Hodgelet Spectral Features |
| 11:10 - 11:20 | Contributed Talk: Taiwo Adebiyi - Gaussian Process Thompson Sampling via Rootfinding |
| 11:20 - 11:30 | Contributed Talk: Freddie Bickford Smith - Rethinking Aleatoric and Epistemic Uncertainty |
| 11:30 - 12:30 | Lunch and Poster Session Setup |
| 12:30 - 12:40 | Contributed Talk: Patrick O’Hara - Distributionally Robust Optimisation with Bayesian Ambiguity Sets |
| 12:40 - 12:50 | Contributed Talk: Joachim Schaeffer - Lithium-Ion Battery System Health Monitoring and Resistance-Based Fault Analysis from Field Data Using Recursive Spatiotemporal Gaussian Processes |
| 12:50 - 13:00 | Contributed Talk: Rafael Oliveira - Variational Search Distributions |
| 13:00 - 13:30 | Invited Talk: Roman Garnett - What I learned while writing the BayesOpt book slides |
| 13:30 - 14:00 | Discussion Break |
| 14:00 - 14:30 | Invited Talk: Jacob R. Garnder - Bayesian optimization needs better deep learning |
| 14:30 - 15:00 | Lightning Talks: Joshua Hang Sai Ip, Yibo Jiang, Dingyang Chen, Guiomar Pescador-Barrios, Sebastian W. Ober, Conor Heins, Richard Bergna, Martin Trapp |
| 15:00 - 16:00 | Poster Session |
| 16:00 - 16:30 | Invited Talk: Virginia Aglietti - FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch slides |
| 16:30 - 17:25 | Panel Discussion |
| 17:25 - 17:30 | Closing Remarks |
Organizers
Advisory Committee
Sponsors
Accepted Workshop Papers
| Title | Authors |
|---|---|
| Graph Classification Gaussian Processes via Hodgelet Spectral Features paper | Mathieu Alain, So Takao, Bastian Rieck, Xiaowen Dong, and Emmanuel Noutahi |
| Bayesian Optimization over Bounded Domains with Beta Product Kernels paper | Huy Hoang Nguyen, Han Zhou, Matthew B. Blaschko, and Aleksei Tiulpin |
| Integration-free kernels for equivariant Gaussian fields with application in dipole moment prediction paper | Tim Steinert, David Ginsbourger, August Smart Lykke-Møller, Ove Christiansen, and Henry Moss |
| Distributionally Robust Optimisation with Bayesian Ambiguity Sets paper | Charita Dellaporta, Patrick O'Hara, and Theodoros Damoulas |
| Preference-based Multi-Objective Bayesian Optimization with Gradients paper | Joshua Hang Sai Ip, Ankush Chakrabarty, Hideyuki Masui, Ali Mesbah, and Diego Romeres |
| Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations paper | Richard Bergna, Sergio Calvo Ordoñez, Felix Opolka, Pietro Lio, and José Miguel Hernández-Lobato |
| Information Directed Tree Search: Reasoning and Planning with Language Agents paper | Yash Chandak, HyunJi Nam, Allen Nie, Jonathan Lee, and Emma Brunskill |
| Convergence Rates of Bayesian Network Policy Gradient for Cooperative Multi-Agent Reinforcement Learning paper | Dingyang Chen, Zhenyu Zhang, Xiaolong Kuang, Xinyang Shen, Ozalp Ozer, and Qi Zhang |
| Bayesian Outcome Weighted Learning paper | Nikki L. B. Freeman, and Sophia Yazzourh |
| NODE-GAMLSS: Interpretable Uncertainty Modelling via Deep Distributional Regression paper | Ananyapam De, Anton Frederik Thielmann, and Benjamin Säfken |
| Variational Inference for Interacting Particle Systems with Discrete Latent States paper | Giosue Migliorini, and Padhraic Smyth |
| BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories paper | Rui-Yang Zhang, Henry Moss, Lachlan Astfalck, Edward Cripps, and David S. Leslie |
| GLEAM-AI: Neural Surrogate for Accelerated Epidemic Analytics and Forecasting paper | Mohammadmehdi Zahedi, Dongxia Wu, Jessica T. Davis, Yian Ma, Alessandro Vespignani, Rose Yu, and Matteo Chinazzi |
| Active Learning for Affinity Prediction of Antibodies paper | Alexandra Gessner, Sebastian W. Ober, Owen Niall Vickery, Dino Oglic, and Talip Ucar |
| Gradient-free variational learning with conditional mixture networks paper | Conor Heins, Hao Wu, Dimitrije Markovic, Alexander Tschantz, Jeff Beck, and Christopher Buckley |
| Toward Information Theoretic Active Inverse Reinforcement Learning paper | Ondrej Bajgar, Dewi Sid William Gould, Jonathon Liu, Oliver Newcombe, Rohan Narayan Langford Mitta, and Jack Golden |
| Posterior Sampling via Autoregressive Generation paper | Kelly W. Zhang, Tiffany Cai, Hongseok Namkoong, and Daniel Russo |
| Amortized Bayesian Workflow (Extended Abstract) paper | Marvin Schmitt, Chengkun LI, Aki Vehtari, Luigi Acerbi, Paul-Christian Bürkner, and Stefan T. Radev |
| Efficient Experimentation for Estimation of Continuous and Discrete Conditional Treatment Effects paper | Muhammed Razzak, Panagiotis Tigas, Andrew Jesson, Yarin Gal, and Uri Shalit |
| Probabilistic predictions with Fourier neural operators paper | Christopher Bülte, Philipp Scholl, and Gitta Kutyniok |
| A Bayesian Approach Towards Crowdsourcing the Truths from LLMs paper | Peiran Yao, Jerin George Mathew, Shehraj Singh, Donatella Firmani, and Denilson Barbosa |
| Inverse-Free Sparse Variational Gaussian Processes paper | Stefano Cortinovis, Laurence Aitchison, James Hensman, Stefanos Eleftheriadis, and Mark van der Wilk |
| Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough? paper | Guiomar Pescador-Barrios, Sarah Lucie Filippi, and Mark van der Wilk |
| Variational Last Layers for Bayesian Optimization paper | Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, and James Harrison |
| A Fast, Robust Elliptical Slice Sampling Method for Truncated Multivariate Normal Distributions paper | Kaiwen Wu, and Jacob R. Gardner |
| Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy paper | Will LeVine, Benjamin Pikus, Jacob Phillips, Berk Norman, Fernando Amat Gil, and Sean M. Hendryx |
| Constrained Multi-objective Bayesian Optimization paper | Diantong Li, Fengxue Zhang, Chong Liu, and Yuxin Chen |
| MHP-DDP: Multivariate Hawkes Process with Dependent Dirichlet Process paper | Alex Ziyu Jiang, and Abel Rodriguez |
| Finding Interior Optimum of Black-box Constrained Objective with Bayesian Optimization paper | Fengxue Zhang, Zejie Zhu, and Yuxin Chen |
| Incremental Uncertainty-aware Performance Monitoring with Labeling Intervention paper | Alexander Koebler, Thomas Decker, Ingo Thon, Volker Tresp, and Florian Buettner |
| (Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models paper | Andreas Kirsch |
| BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings paper | Karine Karine, Susan Murphy, and Benjamin Marlin |
| Two Students: Enabling Uncertainty Quantification in Federated Learning Clients paper | Cristovão Iglesias Jr, Sidney Alves de Outeiro, Claudio Miceli de Farias, and Miodrag Bolic |
| Uncertainty Quantification and Calibration for Audio-driven Disease Diagnosis paper | Shubham Kulkarni, Hideaki Watanabe, and Fuminori Homma |
| Hi-fi functional priors by learning activations paper | Marcin Sendera, Amin Sorkhei, and Tomasz Kuśmierczyk |
| Amortized Decision-Aware Bayesian Experimental Design paper | Daolang Huang, Yujia Guo, Luigi Acerbi, and Samuel Kaski |
| Posterior Inferred, Now What? Streamlining Prediction in Bayesian Deep Learning paper | Rui Li, Marcus Klasson, Arno Solin, and Martin Trapp |
| Bayesian Nonparametric Learning using the Maximum Mean Discrepancy Measure for Synthetic Data Generation paper | Forough Fazeli-Asl, Michael Minyi Zhang, and Lizhen Lin |
| Lightspeed Black-box Bayesian Optimization via Local Score Matching paper | Yakun Wang, Sherman Khoo, and Song Liu |
| The role of tail dependence in estimating posterior expectations paper | Nicola Branchini, and Víctor Elvira |
| Universal Functional Regression with Neural Operator Flows paper | Yaozhong Shi, Angela F Gao, Zachary E Ross, and Kamyar Azizzadenesheli |
| Variational Bayes Gaussian Splatting paper | Toon Van de Maele, Ozan Catal, Alexander Tschantz, Christopher Buckley, and Tim Verbelen |
| Efficient Bayesian Additive Regression Models For Microbiome Studies paper | Tinghua Chen, Michelle Pistner Nixon, and Justin D Silverman |
| Rethinking Aleatoric and Epistemic Uncertainty paper | Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, and Tom Rainforth |
| Big Batch Bayesian Active Learning by Considering Predictive Probabilities paper | Sebastian W. Ober, Samuel Power, Tom Diethe, and Henry Moss |
| Variational Inference in Similarity Spaces: A Bayesian Approach to Personalized Federated Learning paper | Pedro H Barros, Fabricio Murai, Amir Houmansadr, Alejandro C. Frery, and Heitor Soares Ramos Filho |
| Variational Search Distributions paper | Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong, and Edwin V. Bonilla |
| Learning to Defer with an Uncertain Rejector via Conformal Prediction paper | Yizirui Fang, and Eric Nalisnick |
| Post-Calibration Techniques: Balancing Calibration and Score Distribution Alignment paper | Agathe Fernandes Machado, Arthur Charpentier, Emmanuel Flachaire, Ewen Gallic, and Francois HU |
| Uncertainty as a criterion for SOTIF evaluation of deep learning models in autonomous driving systems paper | Ho Suk, and Shiho Kim |
| Atomic Layer Deposition Optimization via Targeted Adaptive Design. paper | Marieme Ngom, Carlo Graziani, and Noah Paulson |
| Fast, Precise Thompson Sampling for Bayesian Optimization paper | David Sweet |
| Scalable Permutation Invariant Multi-Output Gaussian Processes for Cancer Drug Response paper | Leiv Rønneberg, and Vidhi Lalchand |
| Bayesian Optimal Experimental Design of Streaming Data Incorporating Machine Learning Generated Synthetic Data paper | Kentaro Hoffman, and Tyler McCormick |
| An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits paper | Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias Oechtering, and Mikael Skoglund |
| Decision-Driven Calibration for Cost-Sensitive Uncertainty Quantification paper | Gregory Canal, Vladimir Leung, John J. Guerrerio, Philip Sage, and I-Jeng Wang |
| Data-Efficient Variational Mutual Information Estimation via Bayesian Self-Consistency paper | Desi R. Ivanova, Marvin Schmitt, and Stefan T. Radev |
| Riemannian Black Box Variational Inference paper | Mykola Lukashchuk, Wouter W. L. Nuijten, Dmitry Bagaev, Ismail Senoz, and Bert de Vries |
| Bayesian Optimization for High-dimensional Urban Mobility Problems paper | Seongjin Choi, Sergio Rodriguez, and Carolina Osorio |
| Optimizing Detection Time and Specificity: Early Classification of Time Series with Sensitivity Constraint paper | Jiaming Qiu, Ying-Qi Zhao, and Yingye Zheng |
| Adaptive Transductive Inference via Sequential Experimental Design with Contextual Retention paper | Tareq Si Salem |
| Direct Acquisition Optimization for Low-Budget Active Learning paper | Zhuokai Zhao, Yibo Jiang, and Yuxin Chen |
| ROSA: An Optimization Algorithm for Multi-Modal Derivative-Free Functions in High Dimensions paper | Ilija Ilievski, Wenyu Wang, and Christine A. Shoemaker |
| A scalable Bayesian continual learning framework for online and sequential decision making paper | Hanwen Xing, and Christopher Yau |
| Failure Prediction from Few Expert Demonstrations paper | Anjali Parashar, Kunal Garg, Joseph Zhang, and Chuchu Fan |
| Probabilistic Fusion Approach for Robust Battery Prognostics paper | Jokin Alcibar, Ekhi Zugasti, Aitor Aguirre-Ortuzar, and Jose I. Aizpurua |
| Spectral structure learning for clinical time series paper | Ivan Lerner, Francis Bach, and Anita Burgun |
| Computationally Efficient Laplace Approximations for Neural Networks paper | Swarnali Raha, Kshitij Khare, and Rohit K Patra |
| Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization paper | Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu, Yian Ma, and Rose Yu |
| Efficient Modeling of Irregular Time-Series with Stochastic Optimal Control paper | Byoungwoo Park, Hyungi Lee, and Juho Lee |
| Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure paper | Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, and Eytan Bakshy |
| Gaussian Process Thompson Sampling via Rootfinding paper | Taiwo Adebiyi, Bach Do, and Ruda Zhang |
| Gaussian Randomized Exploration for Semi-bandits with Sleeping Arms paper | ZHIMING HUANG, Bingshan Hu, and jianping pan |
| Efficient Local Unlearning for Gaussian Processes with Out-of-Distribution Data paper | Juliusz Ziomek, and Ilija Bogunovic |
| Latent Spatial Dirichlet Allocation paper | Junsouk Choi, Veerabhadran Baladandayuthapani, and Jian Kang |
| Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition paper | Fengxue Zhang, Thomas Desautels, and Yuxin Chen |
| Learning from Less: Bayesian Neural Networks for Optimization Proxy using Limited Labeled Data paper | Parikshit Pareek, Kaarthik Sundar, Deepjyoti Deka, and Sidhant Misra |
| Gaussian Process Conjoint Analysis for Adaptive Marginal Effect Estimation paper | Yehu Chen, Jacob Montgomery, and Roman Garnett |
| Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design paper | Sahel Iqbal, Hany Abdulsamad, Sara Perez-Vieites, Simo Särkkä, and Adrien Corenflos |
| Bayesian Rashomon Sets for Model Uncertainty: A critical comparison paper | Aparajithan Venkateswaran, Anirudh Sankar, Arun Chandrasekhar, and Tyler McCormick |
| Cold Posterior Effect towards Adversarial Robustness paper | Bruce Rushing, Antonios Alexos, Harrison Espino, Nicholas Cohen, and Pierre Baldi |
| Mode Collapse in Variational Deep Gaussian Processes paper | Francisco Javier Sáez-Maldonado, Juan Maroñas, and Daniel Hernández-Lobato |
| TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions paper | Wei-Ting Tang, Ankush Chakrabarty, and Joel Paulson |
| Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness paper | Nikola Pavlovic, Sudeep Salgia, and Qing Zhao |
| Stochastic Gradient MCMC for Gaussian Process Inference on Massive Geostatistical Data paper | Mohamed A. Abba, Brian J. Reich, Reetam Majumder, and Brandon Feng |
| TP$^2$DP$^2$: A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior paper | Yiwei Dong, Shaoxin Ye, Yuwen Cao, Qiyu Han, Hongteng Xu, and Hanfang Yang |
| Cost-effective Reduced-Order Modeling via Bayesian Active Learning paper | Amir Hossein Rahmati, Nathan Urban, Byung-Jun Yoon, and Xiaoning Qian |
| Improved Depth Estimation of Bayesian Neural Networks paper | Bart van Erp, and Bert de Vries |
| Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow paper | Henry Moss, Victor Picheny, Hrvoje Stojic, Sebastian W. Ober, Artem Artemev, Andrei Paleyes, Sattar Vakili, Stratis Markou, Jixiang Qing, Nasrulloh Ratu Bagus Satrio Loka, and Ivo Couckuyt |
| Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series paper | Eshant English, and Christoph Lippert |
| Had enough of experts? Elicitation and evaluation of Bayesian priors from large language models paper | David Antony Selby, Kai Spriestersbach, Yuichiro Iwashita, Dennis Bappert, Archana Warrier, Sumantrak Mukherjee, Muhammad Nabeel Asim, Koichi Kise, and Sebastian Josef Vollmer |
| Preconditioned Crank-Nicolson Algorithms for Wide Bayesian Neural Networks paper | Lucia Pezzetti, Stefano Favaro, and Stefano Peluchetti |
| Graph Agnostic Causal Bayesian Optimisation paper | Sumantrak Mukherjee, Mengyan Zhang, Seth Flaxman, and Sebastian Josef Vollmer |
| Bayesian Optimization of High-dimensional Outputs with Human Feedback paper | Qing Feng, Zhiyuan Jerry Lin, Yujia Zhang, Benjamin Letham, Jelena Markovic-Voronov, Ryan-Rhys Griffiths, Peter I. Frazier, and Eytan Bakshy |
| An Active Learning Performance Model for Parallel Bayesian Calibration of Expensive Simulations paper | Özge Sürer, and Stefan M. Wild |
| Using Rashomon Sets for Robust Active Learning paper | Simon Dovan Nguyen, Tyler McCormick, and Kentaro Hoffman |
| Lithium-Ion Battery System Health Monitoring and Resistance-Based Fault Analysis from Field Data Using Recursive Spatiotemporal Gaussian Processes paper | Joachim Schaeffer, Eric Lenz, Duncan Gulla, Martin Z. Bazant, Richard Braatz, and Rolf Findeisen |
| Ensemble Mashups: A Simple Recipe For Better Bayesian Optimization paper | Anand Ravishankar, Fernando Llorente, Yuanqing Song, and Petar Djuric |
| Active Learning for Optimal Minimization of Experimental Characterization Uncertainty paper | Marcus Schwarting, Nathan Seifert, Logan Ward, Ben Blaiszik, Ian Foster, Yuxin Chen, and Kirill Prozument |