List of Papers
Note that only papers in the proceedings track are archived. Links to the PDFs to follow.
Proceedings Track
| No. | Remark | Paper |
|---|---|---|
| 1 | Talk + Poster |
Divide, Conquer, Combine Bayesian Decision
Tree Sampling
Jodie Anne
Cochrane, Adrian Wills, Sarah J Johnson
|
| 2 | Talk + Poster |
U-ensembles: Improved diversity in the small
data regime using unlabeled data
Konstantinos Pitas, Hani Anouar Bourrous, Julyan Arbel
|
| 3 | Talk + Poster |
From predictions to confidence intervals: an
empirical study of conformal prediction methods
for in-context learning
Zhe
Huang, Simone Rossi, Rui Yuan, Thomas Hannagan
|
| 4 | Talk + Poster |
Normalizing Flow Regression for Bayesian
Inference with Offline Likelihood
Evaluations
Chengkun LI, Bobby
Huggins, Petrus Mikkola, Luigi Acerbi
|
| 5 | Talk + Poster |
Deep Q-Exponential Processes
Zhi Chang, Chukwudi Paul Obite, Shuang Zhou, Shiwei Lan
|
| 6 | Talk + Poster |
Sparse Gaussian Neural Processes
Tommy Rochussen, Vincent Fortuin
|
| 7 | Talk + Poster |
Massively Parallel Expectation Maximization
For Approximate Posteriors
Thomas Heap, Sam Bowyer, Laurence Aitchison
|
Workshop Track
Not archived.
| No. | Remark | Paper |
|---|---|---|
| 1 | Poster |
Approximate Posteriors in Neural Networks: A
Sampling Perspective
Julius
Kobialka, Emanuel Sommer, Juntae Kwon, Daniel Dold,
David Rügamer
|
| 2 | Poster |
Variational diffusion transformers for
conditional sampling of supernovae
spectra
Yunyi Shen, Alexander
Thomas Gagliano
|
| 3 | Poster |
Post-Hoc Uncertainty Quantification in
Pre-Trained Neural Networks via Activation-Level
Gaussian Processes
Richard
Bergna, Stefan Depeweg, Sergio Calvo Ordoñez, Jonathan
Plenk, Alvaro Cartea, José Miguel Hernández-Lobato
|
| 4 | Poster |
Inclusive KL Minimization: A
Wasserstein-Fisher-Rao Gradient Flow
Perspective
Jia-Jie Zhu
|
| 5 | Poster |
Variational Learning Induces Adaptive Label
Smoothing
Sin-Han Yang, Zhedong
Liu, Gian Maria Marconi, Mohammad Emtiyaz Khan
|
| 6 | Poster |
Compact Memory for K-prior Based Continual
Learning
Yohan Jung, Hyungi Lee,
Wenlong Chen, Thomas Möllenhoff, Yingzhen Li, Juho Lee,
Mohammad Emtiyaz Khan
|
| 7 | Poster |
Improving Robustness to Model
Misspecification in Bayesian Experimental
Design
Alex Forster, Desi R.
Ivanova, Tom Rainforth
|
| 8 | Poster |
SDE Matching: Scalable and Simulation-Free
Training of Latent Stochastic Differential
Equations
Grigory Bartosh,
Dmitry Vetrov, Christian A. Naesseth
|
| 9 | Poster |
Semantic Calibration of LLMs Through the
Lens of Temperature Scaling
Tom
A. Lamb, Desi R. Ivanova, Philip Torr, Tim G. J. Rudner
|
| 10 | Poster |
JoLT: Joint Probabilistic Predictions on
Tabular Data Using LLMs
Aliaksandra Shysheya, John F Bronskill, James Requeima,
Shoaib Ahmed Siddiqui, Javier Gonzalez, David Duvenaud,
Richard E. Turner
|
| 11 | Poster |
Adjustment for Confounding using Pre-Trained
Representations
Rickmer Schulte,
David Rügamer, Thomas Nagler
|
| 12 | Poster |
Uncertainty Quantification for Prior-Fitted
Networks using Martingale Posteriors
Thomas Nagler, David Rügamer
|
| 13 | Poster |
Exploring Pseudo-Token Approaches in
Transformer Neural Processes
Jose Miguel Lara Rangel, Nanze Chen, Fengzhe Zhang
|
| 14 | Poster |
Semi-Supervised Bayesian Active Learning
with Task-Driven Representations
Kianoosh Ashouritaklimi, Tom Rainforth
|
| 15 | Poster |
What Actually Matters for Materials
Discovery: Pitfalls and Recommendations in
Bayesian Optimization
Tristan
Cinquin, Stanley Lo, Felix Strieth-Kalthoff, Alan
Aspuru-Guzik, Geoff Pleiss, Robert Bamler, Tim G. J.
Rudner, Vincent Fortuin, Agustinus Kristiadi
|
| 16 | Poster |
Learning Likelihood-Free Reference
Priors
Nicholas George Bishop,
Joel Dyer, Daniel Jarne Ornia, Ani Calinescu, Michael J.
Wooldridge
|
| 17 | Poster |
Neural Flow Samplers with Shortcut
Models
Wuhao Chen, Zijing Ou,
Yingzhen Li
|
| 18 | Poster |
Inference-Time Prior Adaptation in
Simulation Based Inference via Guided Diffusion
Models
Paul Edmund Chang, Severi
Rissanen, Nasrulloh Ratu Bagus Satrio Loka, Daolang
Huang, Luigi Acerbi
|
| 19 | Poster |
Learning Graph Structure for GNNs via
Marginal Likelihood
Anita Yang,
Thomas Möllenhoff, Ken-Ichi Kawarabayashi, Mohammad
Emtiyaz Khan
|
| 20 | Poster |
Variational Bayes Portfolio
Construction
Nicolas Nguyen,
James Ridgway, Claire Vernade
|
| 21 | Poster |
Estimating the Data-Influence of Latent
Variable Models using Variational Bayes
Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric
Nalisnick
|
| 22 | Poster |
Are Your Continuous Approximations Really
Continuous? Reimagining VI with Bitstring
Representations
Aleksanteri
Sladek, Martin Trapp, Arno Solin
|
| 23 | Poster |
Simulation-based inference with diffusion
models for spatial statistics
Herman Tesso, Ayush Bharti, Elizaveta Semenova
|
| 24 | Poster |
Tighter sparse variational Gaussian
processes
Thang D Bui, Matthew
Ashman, Richard E. Turner
|
| 25 | Poster |
Stacking Variational Bayesian Monte
Carlo
Francesco Silvestrin,
Chengkun LI, Luigi Acerbi
|
| 26 | Poster |
Sampling with diffusion models by amortizing
posterior inference
Yi Han,
Luhuan Wu, John Patrick Cunningham
|
| 27 | Poster |
Heteroscedastic Variational Last
Layers
James Harrison, John
Willes, Paul Brunzema, Jasper Snoek
|
| 28 | Poster |
Towards One Model for Classical
Dimensionality Reduction: A Probabilistic
Perspective on UMAP and t-SNE
Aditya Ravuri, Neil D Lawrence
|
| 29 | Poster |
Beyond Schrödinger Bridges: A Least-Squares
Approach for Learning Stochastic Dynamics with
Unknown Volatility
Renato
Berlinghieri, Yunyi Shen, Tamara Broderick
|
| 30 | Poster |
Generative Uncertainty in Diffusion
Models
Metod Jazbec, Eliot
Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick,
Stephan Mandt
|
| 31 | Poster |
Observation Noise and Initialization in Wide
Neural Networks
Sergio Calvo
Ordoñez, Jonathan Plenk, Richard Bergna, Alvaro Cartea,
José Miguel Hernández-Lobato, Konstantina Palla, Kamil
Ciosek
|
| 32 | Poster |
Recurrent Memory for Online Interdomain
Gaussian Processes
Wenlong Chen,
Naoki Kiyohara, Harrison Zhu, Yingzhen Li
|
| 33 | Poster |
Transcending Bayesian Inference:
Transformers Extrapolate Rules Compositionally
Under Model Misspecification
Szilvia Ujváry, Anna Mészáros, Wieland Brendel, Patrik
Reizinger, Ferenc Huszár
|
Fast Track
Not archived.
| No. | Remark | Paper |
|---|---|---|
| 1 | Poster |
Repulsive Latent Score Distillation for
Solving Inverse Problems
Nicolas
Zilberstein, Morteza Mardani, Santiago Segarra
|
| 2 | Poster |
Amortized Bayesian Experimental Design for
Decision-Making
Daolang Huang,
Yujia Guo, Luigi Acerbi, Samuel Kaski
|
| 3 | Poster |
The Local Learning Coefficient: A
Singularity-Aware Complexity Measure
Edmund Lau, Zach Furman, George Wang, Daniel
Murfet, Susan Wei
|
| 4 | Poster |
Calibrating LLMs with Information-theoretic
Evidential Deep Learning
Yawei
Li, David Rügamer, Bernd Bischl, Mina Rezaei
|
| 5 | Poster |
Microcanonical Langevin Ensembles: Advancing
the Sampling of Bayesian Neural
Networks
Emanuel Sommer, Jakob
Robnik, Giorgi Nozadze, Uros Seljak, David Rügamer
|
| 6 | Poster |
Online Student-t Processes with an
Overall-local Scale Structure for Modelling
Non-stationary Data
Taole Sha,
Michael Minyi Zhang
|
| 7 | Poster |
How much is a noisy image worth? Data
Scaling Laws for Ambient Diffusion
Giannis Daras, Yeshwanth Cherapanamjeri,
Constantinos Daskalakis
|
| 8 | Poster |
LLM Processes: Numerical Predictive
Distributions Conditioned on Natural
Language
James Requeima, John
Bronskill, Dami Choi, Richard E. Turner, David Duvenaud
|
| 9 | Poster |
Differentiation and Specialization of
Attention Heads via the Refined Local Learning
Coefficient
George Wang, Jesse
Hoogland, Stan van Wingerden, Zach Furman, Daniel Murfet
|
| 10 | Poster |
Paths and Ambient Spaces in Neural Loss
Landscapes
Daniel Dold, Julius
Kobialka, Nicolai Palm, Emanuel Sommer, David Rügamer,
Oliver Dürr
|
| 11 | Poster |
Additive Model Boosting: New Insights and
Path(ologie)s
Rickmer Schulte,
David Rügamer
|
| 12 | Poster |
Multi-marginal Schrödinger Bridges with
Iterative Reference Refinement
Yunyi Shen, Renato Berlinghieri, Tamara Broderick
|
| 13 | Poster |
Bayesian Optimization Via Continual
Variational Last Layer Training
Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian
Trimpe, Jasper Snoek, James Harrison
|
| 14 | Poster |
Generalization Bounds for Dependent Data
using Online-to-Batch Conversion
Sagnik Chatterjee, Manuj Mukherjee, Alhad Sethi
|
| 15 | Poster |
Variational Search Distributions
Daniel M. Steinberg, Rafael Oliveira, Cheng Soon
Ong, Edwin V. Bonilla
|
| 16 | Poster |
A Meta-Learning Approach to Bayesian Causal
Discovery
Anish Dhir, Matthew
Ashman, James Requeima, Mark van der Wilk
|
| 17 | Poster |
Amortized Probabilistic Conditioning for
Optimization, Simulation and Inference
Paul E. Chang, Nasrulloh Loka, Daolang Huang,
Ulpu Remes, Samuel Kaski, Luigi Acerbi
|
| 18 | Poster |
PABBO: Preferential Amortized Black-Box
Optimization
Xinyu Zhang,
Daolang Huang, Samuel Kaski, Julien Martinelli
|
| 19 | Poster |
Prior-Dependent Allocations for Bayesian
Fixed-Budget Best-Arm Identification in
Structured Bandits
Nicolas
Nguyen, Imad Aouali, András György, Claire Vernade
|
| 20 | Poster |
Streamlining Prediction in Bayesian Deep
Learning
Rui Li, Marcus Klasson,
Arno Solin, Martin Trapp
|
| 21 | Poster |
Towards Self-Supervised Covariance
Estimation in Deep Heteroscedastic
Regression
Megh Shukla, Aziz
Shameem, Mathieu Salzmann, Alexandre Alahi
|
| 22 | Poster |
Is merging worth it? Securely evaluating the
information gain for causal dataset
acquisition
Jake Fawkes, Lucile
Ter-Minassian, Desi Ivanova, Uri Shalit, Chris Holmes
|
| 23 | Poster |
Connecting Federated ADMM to Bayes
Siddharth Swaroop, Mohammad Emtiyaz Khan, Finale
Doshi-Velez
|
| 24 | Poster |
A Generative Model of Symmetry
Transformations
James Urquhart
Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy,
Javier Antoran, David Krueger, Richard E. Turner, Eric
Nalisnick, José Miguel Hernández-Lobato
|
