List of papers

Proceedings Track

Accepted papers are available at OpenReview.

Talk + Poster	Implicitly Bayesian Prediction Rules in Deep Learning Bruno Kacper Mlodozeniec, Richard E. Turner, David Krueger
Talk + Poster	PAC-Bayesian Soft Actor-Critic Learning Bahareh Tasdighi, Abdullah Akgül, Manuel Haussmann, Kenny Kazimirzak Brink, Melih Kandemir
Talk + Poster	Bayesian Optimization for Precision Agriculture with Scalable Probabilistic Models Ruhana Azam, Sang T. Truong, samuel bonfim fernandes, Andrew D.B. Leakey, Alexander Lipka, Mohammed El-Kebir, Sanmi Koyejo
Talk + Poster	In-Context In-Context Learning with Transformer Neural Processes Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner
Talk + Poster	Fluctuation without dissipation: Microcanonical Langevin Monte Carlo Jakob Robnik, Uros Seljak
Talk + Poster	Non-asymptotic approximations of Gaussian neural networks via second-order Poincaré inequalities Alberto Bordino, Stefano Favaro, Sandra Fortini

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Workshop Track

Accepted papers are available at OpenReview for archival purposes. This does not constitute a proceedings for the symposium.

Poster	Estimating Expectations without Sampling: Neural Stein Estimation Mohsin Hasan, Dinghuai Zhang, Cheikh Ahmed, Awa Khouna, Yoshua Bengio
Poster	Gaussian Stochastic Weight Averaging for Bayesian Low-rank Adaptation of Large Language Models Emre Onal, Klemens Flöge, Emma Caldwell, Arsen Sheverdin, Vincent Fortuin
Poster	Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
Poster	Can a Confident Prior Replace a Cold Posterior? Martin Marek, Brooks Paige, Pavel Izmailov
Poster	Globally Convergent Variational Inference Declan McNamara, Jackson Loper, Jeffrey Regier
Poster	Uncertainty-Guided Optimization on Large Language Model Search Trees Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi
Poster	On Uncertainty Quantification for Near-Bayes Optimal Algorithms Ziyu Wang, Christopher C. Holmes
Poster	Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes Jihao Andreas Lin, Shreyas Padhy, Bruno Kacper Mlodozeniec, José Miguel Hernández-Lobato
Poster	Word Embedding Uncertainty Estimation Väinö Yrjänäinen, Isac Boström, Måns Magnusson
Poster	Evaluating approximate Bayesian inference for radio galaxy classification Devina Mohan, Anna M M Scaife
Poster	Hamilton Monte Carlo with discrete categorical parameters using the Concrete distribution Jakob Torgander, Måns Magnusson, Jonas Wallin
Poster	HybridBNN: Joint Training of Deterministic and Stochastic Layers in Bayesian Neural Nets Amin Nejatbakhsh, Julien Boussard
Poster	Explainable Attribution using Additive Gaussian Processes Xiaoyu Lu, Alexis Boukouvalas, James Hensman
Poster	Unity by Diversity: Improved Representation Learning in Multimodal VAEs Thomas M. Sutter, Yang Meng, Norbert Fortin, Julia E Vogt, Babak Shahbaba, Stephan Mandt
Poster	Ai-sampler: Adversarial Learning of Markov kernels with involutive maps Evgenii Egorov, Riccardo Valperga, Stratis Gavves
Poster	Structured Partial Stochasticity in Bayesian Neural Networks Tommy Rochussen
Poster	How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss
Poster	On the Properties and Estimation of Pointwise Mutual Information Profiles Paweł Czyż, Frederic Grabowski, Julia E Vogt, Niko Beerenwinkel, Alexander Marx
Poster	Variational Linearized Laplace Approximation for Bayesian Deep Learning Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-Lobato
Poster	Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez

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Fast Track

Poster	Hodge-Compositional Edge Gaussian Processes Maosheng Yang, Viacheslav Borovitskiy, Elvin Isufi
Poster	Transport meets Variational Inference: Controlled Monte Carlo Diffusions Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken
Poster	Stochastic Gradient Descent for Gaussian Processes Done Right Jihao Andreas Lin, Shreyas Padhy, Javier Antoran, Austin Tripp, Alexander Terenin, Csaba Szepesvari, José Miguel Hernández-Lobato, David Janz
Poster	The Allocore Tensor Decomposition for Sparse Count Data John Hood, Aaron Schein
Poster	Bayesian Semi-structured Subspace Inference Daniel Dold, David Ruegamer, Beate Sick, Oliver Dürr
Poster	Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks Mikkel Jordahn, Pablo Olmos
Poster	Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution Johannes Zenn, Robert Bamler
Poster	Debiased Distribution Compression Lingxiao Li, Raaz Dwivedi, Lester Mackey
Poster	Variational Resampling Oskar Kviman, Nicola Branchini, Víctor Elvira, Jens Lagergren
Poster	ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference Nikita Durasov, Nik Dorndorf, Hieu Le, Pascal Fua
Poster	Batch and match: black-box variational inference with a score-based divergence Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
Poster	Adaptive importance sampling for heavy-tailed distributions via alpha-divergence minimization Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux, Víctor Elvira
Poster	How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer
Poster	An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation Jonas Arruda, Yannik Schälte, Clemens Peiter, Olga Teplytska, Ulrich Jaehde, Jan Hasenauer
Poster	Liouville Flow Importance Sampler Yifeng Tian, Nishant Panda, Yen Ting Lin
Poster	Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
Poster	Outlier-robust Kalman Filtering through Generalised Bayes Gerardo Duran-Martin, Matias Altamirano, Alexander Y. Shestopaloff, Leandro Sánchez-Betancourt, Jeremias Knoblauch, Matt Jones, François-Xavier Briol, Kevin Murphy
Poster	Using Autodiff to Estimate Posterior Moments, Marginals and Samples Sam Bowyer, Thomas Heap, Laurence Aitchison
Poster	Partially Stochastic Infinitely Deep Bayesian Neural Networks Sergio Calvo-Ordoñez, Matthieu Meunier, Francesco Piatti, Yuantao Shi
Poster	Robust and Conjugate Gaussian Process Regression Matias Altamirano Francois-Xavier Briol, Jeremias Knoblauch
Poster	Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective Fabian Falck*, Ziyu Wang*, Chris Holmes
Poster	Approximate Bayesian Computation with Path Signatures Joel Dyer, Patrick Cannon, Sebastian M. Schmon
Poster	IBIA: An Incremental Build-Infer-Approximate Framework for Approximate Inference of Partition Function Shivani Bathla, Vinita Vasudevan
Poster	Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev
Poster	Are you using test log-likelihood correctly? Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick
Poster	Minimizing f-Divergences by Interpolating Velocity Fields Song Liu, Jiahao Yu, Jack Simons, Mingxuan Yi, Mark Beaumont
Poster	Function-space Parameterization of Neural Networks for Sequential Learning Aidan Scannell, Riccardo Mereu, Paul Edmund Chang, Ella Tamir, Joni Pajarinen, Arno Solin
Poster	Causal Discovery using Bayesian Model Selection Anish Dhir, Samuel Power, Mark van der Wilk