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Workshop

Structured Probabilistic Inference and Generative Modeling

Dinghuai Zhang ⋅ Yuanqi Du ⋅ Chenlin Meng ⋅ Shawn Tan ⋅ Yingzhen Li ⋅ Max Welling ⋅ Yoshua Bengio
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Abstract

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Schedule

Timezone: Asia/Seoul
4:00 AM
Opening Remark
Dinghuai Zhang ⋅ Yuanqi Du ⋅ Chenlin Meng ⋅ Shawn Tan ⋅ Yingzhen Li ⋅ Max Welling ⋅ Yoshua Bengio
4:10 AM
Invited Talk by Karen Ullrich
Karen Ullrich
4:50 AM
Invited Talk by Tommi Jaakkola
Tommi Jaakkola
5:30 AM
Coffee Break
5:50 AM
Invited Talk by Durk Kingma
Diederik Kingma
6:40 AM
Provable benefits of score matching
Andrej Risteski
8:00 AM
Panel Discussion
Chenlin Meng ⋅ Yang Song ⋅ Yilun Xu ⋅ Ricky T. Q. Chen ⋅ Charlotte Bunne ⋅ Arash Vahdat
9:00 AM
Invited Talk by Ruqi Zhang
Ruqi Zhang
9:40 AM
Invited Talk by Stefano Ermon
Stefano Ermon
10:50 AM
Closing Remark
11:00 AM
Poster Session 2
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Brian Trippe ⋅ Luhuan Wu ⋅ Christian Andersson Naesseth ⋅ David Blei ⋅ John Cunningham
Generative semi-supervised learning with a neural seq2seq noisy channel
Soroosh Mariooryad ⋅ Matt Shannon ⋅ Siyuan Ma ⋅ Tom Bagby ⋅ David Kao ⋅ Daisy Stanton ⋅ Eric Battenberg ⋅ RJ Skerry-Ryan
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Chris Emezue ⋅ Alexandre Drouin ⋅ Tristan Deleu ⋅ Stefan Bauer ⋅ Yoshua Bengio
Conditional Graph Generation with Graph Principal Flow Network
Tianze Luo ⋅ Zhanfeng Mo ⋅ Sinno Jialin Pan
Deep Generative Clustering with Multimodal Variational Autoencoders
Emanuele Palumbo ⋅ Sonia Laguna ⋅ Daphné Chopard ⋅ Julia Vogt
Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces
Miles Wang-Henderson ⋅ Bartu Soyuer ⋅ Parnian Kassraie ⋅ Andreas Krause ⋅ Ilija Bogunovic
Plug-and-Play Controllable Graph Generation with Diffusion Models
Kartik Sharma ⋅ Srijan Kumar ⋅ Rakshit Trivedi
Towards Modular Learning of Deep Causal Generative Models
Md Musfiqur Rahman ⋅ Murat Kocaoglu
Pretrained Language Models to Solve Graph Tasks in Natural Language
Frederik Wenkel ⋅ Guy Wolf ⋅ Boris Knyazev
A Generative Model for Text Control in Minecraft
Shalev Lifshitz ⋅ Keiran Paster ⋅ Harris Chan ⋅ Jimmy Ba ⋅ Sheila McIlraith
Visual Chain-of-Thought Diffusion Models
William Harvey ⋅ Frank Wood
Collapsed Inference for Bayesian Deep Learning
Zhe Zeng ⋅ Guy Van den Broeck
Your Diffusion Model is Secretly a Zero-Shot Classifier
Alexander Li ⋅ Mihir Prabhudesai ⋅ Shivam Duggal ⋅ Ellis Brown ⋅ Deepak Pathak
Test-time Adaptation with Diffusion Models
Mihir Prabhudesai ⋅ Tsung-Wei Ke ⋅ Alexander Li ⋅ Deepak Pathak ⋅ Katerina Fragkiadaki
Beyond Confidence: Reliable Models Should Also Consider Atypicality
Mert Yuksekgonul ⋅ Linjun Zhang ⋅ James Zou ⋅ Carlos Guestrin
Implications of kernel mismatch for OOD data
Beau Coker ⋅ Finale Doshi-Velez
Scaling Graphically Structured Diffusion Models
Christian Weilbach ⋅ William Harvey ⋅ Hamed Shirzad ⋅ Frank Wood
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Xiaoyu Liu ⋅ Jiaxin Yuan ⋅ Bang An ⋅ Yuancheng Xu ⋅ Yifan Yang ⋅ Furong Huang
Nonparametric posterior normalizing flows
Sinead A Williamson ⋅ Evan Ott
Diffusion Probabilistic Models Generalize when They Fail to Memorize
TaeHo Yoon ⋅ Joo Young Choi ⋅ Sehyun Kwon ⋅ Ernest Ryu
Solving Inverse Physics Problems with Score Matching
Benjamin Holzschuh ⋅ Simona Vegetti ⋅ Nils Thuerey
Attention as Implicit Structural Inference
Ryan Singh ⋅ Christopher Buckley
Prediction under Latent Subgroup Shifts with High-dimensional Observations
William Walker ⋅ Arthur Gretton ⋅ Maneesh Sahani
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Tristan Deleu ⋅ Mizu Nishikawa-Toomey ⋅ Jithendaraa Subramanian ⋅ Nikolay Malkin ⋅ Laurent Charlin ⋅ Yoshua Bengio
Diffusion Based Causal Representation Learning
Amir Mohammad Karimi Mamaghan ⋅ Francesco Quinzan ⋅ Andrea Dittadi ⋅ Stefan Bauer
Provable benefits of score matching
Chirag Pabbaraju ⋅ Dhruv Rohatgi ⋅ Anish Sevekari ⋅ Holden Lee ⋅ Ankur Moitra ⋅ Andrej Risteski
An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction
Urchade Zaratiana ⋅ Nadi Tomeh ⋅ Pierre Holat ⋅ Thierry Charnois
Nested Diffusion Processes for Anytime Image Generation
Noam Elata ⋅ Bahjat Kawar ⋅ Tomer Michaeli ⋅ Michael Elad
Training Diffusion Models with Reinforcement Learning
Kevin Black ⋅ Michael Janner ⋅ Yilun Du ⋅ Ilya Kostrikov ⋅ Sergey Levine
Generating Turn-Based Player Behavior via Experience from Demonstrations
Kuang-Da Wang ⋅ Wei-Yao Wang ⋅ Ping-Chun Hsieh ⋅ Wen-Chih Peng
Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation
Waïss Azizian ⋅ Guillaume Baudart ⋅ Marc Lelarge
Collaborative Score Distillation for Consistent Visual Synthesis
Subin Kim ⋅ Kyungmin Lee ⋅ June Suk Choi ⋅ Jongheon Jeong ⋅ Kihyuk Sohn ⋅ Jinwoo Shin
Exploring Exchangeable Dataset Amortization for Bayesian Posterior Inference
Sarthak Mittal ⋅ Niels Bracher ⋅ Guillaume Lajoie ⋅ Priyank Jaini ⋅ Marcus Brubaker
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Kenza Tazi ⋅ Jihao Andreas Lin ⋅ ST John ⋅ Hong Ge ⋅ Richard E Turner ⋅ Ross Viljoen ⋅ Alex Gardner
Generative Marginalization Models
Sulin Liu ⋅ Peter Ramadge ⋅ Ryan P. Adams
Early Exiting for Accelerated Inference in Diffusion Models
Taehong Moon ⋅ Moonseok Choi ⋅ EungGu Yun ⋅ Jongmin Yoon ⋅ Gayoung Lee ⋅ Juho Lee
MissDiff: Training Diffusion Models on Tabular Data with Missing Values
Yidong Ouyang ⋅ Liyan Xie ⋅ Chongxuan Li ⋅ Guang Cheng
CM-GAN: Stabilizing GAN Training with Consistency Models
Haoye Lu ⋅ Yiwei Lu ⋅ Dihong Jiang ⋅ Spencer Szabados ⋅ Sun Sun ⋅ Yaoliang Yu
Flow Matching for Scalable Simulation-Based Inference
Jonas Wildberger ⋅ Maximilian Dax ⋅ Simon Buchholz ⋅ Stephen R. Green ⋅ Jakob Macke ⋅ Bernhard Schölkopf
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
Simon Buchholz ⋅ Goutham Rajendran ⋅ Elan Rosenfeld ⋅ Bryon Aragam ⋅ Bernhard Schölkopf ⋅ Pradeep Ravikumar
Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection
Vitória Barin-Pacela ⋅ Kartik Ahuja ⋅ Simon Lacoste-Julien ⋅ Pascal Vincent
Causal Discovery with Language Models as Imperfect Experts
Stephanie Long ⋅ Alex Piche ⋅ Valentina Zantedeschi ⋅ Tibor Schuster ⋅ Alexandre Drouin
Fast and Functional structured data generator
Alessandra Carbone ⋅ Aurélien Decelle ⋅ Lorenzo Rosset ⋅ Beatriz Seoane
Structured Neural Networks for Density Estimation
Asic Chen ⋅ Ruian Shi ⋅ Xiang Gao ⋅ Ricardo Baptista ⋅ Rahul G. Krishnan
Diffusion Generative Inverse Design
Marin Vlastelica ⋅ Tatiana Lopez-Guevara ⋅ Kelsey Allen ⋅ Peter Battaglia ⋅ Arnaud Doucet ⋅ Kimberly Stachenfeld
Tree Variational Autoencoders
Laura Manduchi ⋅ Moritz Vandenhirtz ⋅ Alain Ryser ⋅ Julia Vogt
Morse Neural Networks for Uncertainty Quantification
Benoit Dherin ⋅ Huiyi Hu ⋅ JIE REN ⋅ Michael Dusenberry ⋅ Balaji Lakshminarayanan
STable Permutation-based Framework for Table Generation in Sequence-to-Sequence Models
Michał Pietruszka ⋅ Michał Turski ⋅ Łukasz Borchmann ⋅ Tomasz Dwojak ⋅ Gabriela Pałka ⋅ Karolina Szyndler ⋅ Dawid Jurkiewicz ⋅ Łukasz Garncarek
Neuro-Causal Factor Analysis
Alex Markham ⋅ Mingyu Liu ⋅ Bryon Aragam ⋅ Liam Solus
Autoregressive Diffusion Models with non-Uniform Generation Order
Filip Ekström Kelvinius ⋅ Fredrik Lindsten
Variational Point Encoding Deformation for Dental Modeling
Johan Ye ⋅ Thomas Ørkild ⋅ Peter Søndergard ⋅ Søren Hauberg
BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery
Yashas Annadani ⋅ Nick Pawlowski ⋅ Joel Jennings ⋅ Stefan Bauer ⋅ Cheng Zhang ⋅ Wenbo Gong
On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization
Chieh-Hsin Lai ⋅ Yuhta Takida ⋅ Toshimitsu Uesaka ⋅ Naoki Murata ⋅ Yuki Mitsufuji ⋅ Stefano Ermon
Uncovering Latent Structure Using Random Partition Models
Thomas Sutter ⋅ Alain Ryser ⋅ Joram Liebeskind ⋅ Julia Vogt
Improving Training of Likelihood-based Generative Models with Gaussian Homotopy
Ba-Hien Tran ⋅ Giulio Franzese ⋅ Pietro Michiardi ⋅ Maurizio Filippone
Lexinvariant Language Models
Qian Huang ⋅ Eric Zelikman ⋅ Sarah Chen ⋅ Yuhuai Wu ⋅ Greg Valiant ⋅ Percy Liang
BatchGFN: Generative Flow Networks for Batch Active Learning
Shreshth Malik ⋅ Salem Lahlou ⋅ Andrew Jesson ⋅ Moksh Jain ⋅ Nikolay Malkin ⋅ Tristan Deleu ⋅ Yoshua Bengio ⋅ Yarin Gal
Balanced Training of Energy-Based Models with Adaptive Flow Sampling
Louis Grenioux ⋅ Eric Moulines ⋅ Marylou Gabrié
PRODIGY: Enabling In-context Learning Over Graphs
Qian Huang ⋅ Hongyu Ren ⋅ Peng Chen ⋅ Gregor Kržmanc ⋅ Daniel Zeng ⋅ Percy Liang ⋅ Jure Leskovec
Dimensionality Reduction as Probabilistic Inference
Aditya Ravuri ⋅ Francisco Vargas ⋅ Vidhi Lalchand ⋅ Neil Lawrence
DiffMol: 3D Structured Molecule Generation with Discrete Denoising Diffusion Probabilistic Models
Weitong Zhang ⋅ Xiaoyun Wang ⋅ Justin Smith ⋅ Joe Eaton ⋅ Brad Rees ⋅ Quanquan Gu
Diffusion Probabilistic Models for Structured Node Classification
Hyosoon Jang ⋅ Seonghyun Park ⋅ Sangwoo Mo ⋅ Sungsoo Ahn
Score-based Enhanced Sampling for Protein Molecular Dynamics
Jiarui Lu ⋅ Bozitao Zhong ⋅ Jian Tang
Reinforcement Learning-Driven Linker Design via Fast Attention-based Point Cloud Alignment
Rebecca Manuela Neeser ⋅ Mehmet Akdel ⋅ Daniel Kovtun ⋅ Luca Naef
AbODE: Ab initio antibody design using conjoined ODEs
Yogesh Verma ⋅ Markus Heinonen ⋅ Vikas Garg
Inferring Hierarchical Structure in Multi-Room Maze Environments
Daria de Tinguy ⋅ Toon Van de Maele ⋅ Tim Verbelen ⋅ Bart Dhoedt
Parallel Sampling of Diffusion Models
Andy Shih ⋅ Suneel Belkhale ⋅ Stefano Ermon ⋅ Dorsa Sadigh ⋅ Nima Anari
PITS: Variational Pitch Inference Without Fundamental Frequency for End-to-End Pitch-Controllable TTS
Junhyeok Lee ⋅ Wonbin Jung ⋅ Hyunjae Cho ⋅ Jaeyeon Kim ⋅ Jaehwan Kim
Regularized Data Programming with Automated Bayesian Prior Selection
Jacqueline Maasch ⋅ Hao Zhang ⋅ Qian Yang ⋅ Fei Wang ⋅ Volodymyr Kuleshov
On the Identifiability of Markov Switching Models
Carles Balsells-Rodas ⋅ Yixin Wang ⋅ Yingzhen Li
Optimizing protein fitness using Bi-level Gibbs sampling with Graph-based Smoothing
Andrew Kirjner ⋅ Jason Yim ⋅ Raman Samusevich ⋅ Tommi Jaakkola ⋅ Regina Barzilay ⋅ Ila R. Fiete
Robust and Scalable Bayesian Online Changepoint Detection
Matias Altamirano ⋅ Francois-Xavier Briol ⋅ Jeremias Knoblauch
GSURE-Based Diffusion Model Training with Corrupted Data
Bahjat Kawar ⋅ Noam Elata ⋅ Tomer Michaeli ⋅ Michael Elad
Hierarchical Graph Generation with $K^{2}$-trees
Yunhui Jang ⋅ Dongwoo Kim ⋅ Sungsoo Ahn
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
Minsu Kim ⋅ Federico Berto ⋅ Sungsoo Ahn ⋅ Jinkyoo Park
Thompson Sampling for Improved Exploration in GFlowNets
Jarrid Rector-Brooks ⋅ Kanika Madan ⋅ Moksh Jain ⋅ Maksym Korablyov ⋅ Chenghao Liu ⋅ Sarath Chandar ⋅ Nikolay Malkin ⋅ Yoshua Bengio
GFlowNets for Causal Discovery: an Overview
Dragos Cristian Manta ⋅ Edward Hu ⋅ Yoshua Bengio
Concept Algebra for Score-based Conditional Model
Zihao Wang ⋅ Lin Gui ⋅ Jeffrey Negrea ⋅ Victor Veitch
Diffusion Models with Grouped Latents for Interpretable Latent Space
Sangyun Lee ⋅ Gayoung Lee ⋅ Hyunsu Kim ⋅ Kim Junho ⋅ Youngjung Uh
Multilevel Control Functional
Kaiyu Li ⋅ Zhuo Sun
HINT: Hierarchical Coherent Networks For Constrained Probabilistic Forecasting
Kin Gutierrez ⋅ David Luo ⋅ Cristian Challu ⋅ Stefania La Vattiata ⋅ Max Mergenthaler Canseco ⋅ Artur Dubrawski