Sunday, 21 July 2024
Getreidemarkt 9, 1060 Vienna, BA Gebäude, 11th floor (TU the Sky
room)
Co-located with
ICML
Accepted papers are available at OpenReview.
Poster |
Implicitly Bayesian Prediction Rules in Deep
Learning
Bruno Kacper Mlodozeniec, Richard E. Turner, David Krueger |
Poster |
PAC-Bayesian Soft Actor-Critic Learning
Bahareh Tasdighi, Abdullah Akgül, Manuel Haussmann, Kenny Kazimirzak Brink, Melih Kandemir |
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 |
Poster |
In-Context In-Context Learning with Transformer
Neural Processes
Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner |
Poster |
Fluctuation without dissipation: Microcanonical
Langevin Monte Carlo
Jakob Robnik, Uros Seljak |
Poster |
Non-asymptotic approximations of Gaussian neural
networks via second-order Poincaré inequalities
Alberto Bordino, Stefano Favaro, Sandra Fortini |
Accepted papers are available at OpenReview for archival purposes. This does not constitute a proceedings for the symposium.
|
Hodge-Compositional Edge Gaussian Processes
Maosheng Yang, Viacheslav Borovitskiy, Elvin Isufi |
|
Transport meets Variational Inference: Controlled
Monte Carlo Diffusions
Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken |
|
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 |
|
The Allocore Tensor Decomposition for Sparse Count
Data
John Hood, Aaron Schein |
|
Bayesian Semi-structured Subspace Inference
Daniel Dold, David Ruegamer, Beate Sick, Oliver Dürr |
|
Decoupling Feature Extraction and Classification
Layers for Calibrated Neural Networks
Mikkel Jordahn, Pablo Olmos |
|
Differentiable Annealed Importance Sampling Minimizes
The Jensen-Shannon Divergence Between Initial and
Target Distribution
Johannes Zenn, Robert Bamler |
|
Debiased Distribution Compression
Lingxiao Li, Raaz Dwivedi, Lester Mackey |
|
Variational Resampling
Oskar Kviman, Nicola Branchini, Víctor Elvira, Jens Lagergren |
|
ZigZag: Universal Sampling-free Uncertainty
Estimation Through Two-Step Inference
Nikita Durasov, Nik Dorndorf, Hieu Le, Pascal Fua |
|
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 |
|
Adaptive importance sampling for heavy-tailed
distributions via alpha-divergence minimization
Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux, Víctor Elvira |
|
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 |
|
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 |
|
Liouville Flow Importance Sampler
Yifeng Tian, Nishant Panda, Yen Ting Lin |
|
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 |
|
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 |
|
Using Autodiff to Estimate Posterior Moments,
Marginals and Samples
Sam Bowyer, Thomas Heap, Laurence Aitchison |
|
Partially Stochastic Infinitely Deep Bayesian
Neural Networks
Sergio Calvo-Ordoñez, Matthieu Meunier, Francesco Piatti, Yuantao Shi |
|
Robust and Conjugate Gaussian Process
Regression
Matias Altamirano Francois-Xavier Briol, Jeremias Knoblauch |
|
Is In-Context Learning in Large Language Models
Bayesian? A Martingale Perspective
Fabian Falck*, Ziyu Wang*, Chris Holmes |
|
Approximate Bayesian Computation with Path
Signatures
Joel Dyer, Patrick Cannon, Sebastian M. Schmon |
|
IBIA: An Incremental Build-Infer-Approximate
Framework for Approximate Inference of Partition
Function
Shivani Bathla, Vinita Vasudevan |
|
Leveraging Self-Consistency for Data-Efficient
Amortized Bayesian Inference
Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev |
|
Are you using test log-likelihood correctly?
Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick |
|
Minimizing f-Divergences by Interpolating
Velocity Fields
Song Liu, Jiahao Yu, Jack Simons, Mingxuan Yi, Mark Beaumont |
|
Function-space Parameterization of Neural
Networks for Sequential Learning
Aidan Scannell, Riccardo Mereu, Paul Edmund Chang, Ella Tamir, Joni Pajarinen, Arno Solin |
|
Causal Discovery using Bayesian Model
Selection
Anish Dhir, Samuel Power, Mark van der Wilk |