ICML 2024 Past Generative models
ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling
2nd SPIGM @ ICML
- Submission deadline
- May 28, 2024, 11:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (116)
Fetched from OpenReview (v2) on 2026-06-10.
-
A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models
-
A Practical Diffusion Path for Sampling
-
Accelerating Best-of-N via Speculative Rejection
-
Accelerating NCE Convergence with Adaptive Normalizing Constant Computation
-
Accelerating statistical inferences in astrophysics with Neural Networks and Hamiltonian Monte Carlo
-
Aligned Diffusion Models for Retrosynthesis
-
All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models
-
Amortized Active Causal Induction with Deep Reinforcement Learning
-
Amortized Probabilistic Detection of Communities in Graphs
-
Analyzing GFlowNets: Stability, Expressiveness, and Assessment
-
Assessing the Viability of Generative Modeling in Simulated Astronomical Observations
-
Bayesian Reward Models for LLM Alignment
-
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
-
Bidirectional Consistency Models
-
CADO: Cost-Aware Diffusion Solvers for Combinatorial Optimization through RL fine-tuning
-
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
-
Cell Morphology-Guided Small Molecule Generation with GFlowNets
-
Collective Variable Free Transition Path Sampling with Generative Flow Network
-
Color Style Transfer with Modulated Flows
-
Conditional Common Entropy for Instrumental Variable Testing and Partial Identification
-
Conditional Flow Matching for Time Series Modelling
-
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
-
Conformalized Credal Set Predictors
-
Continual Deep Learning on the Edge via Stochastic Local Competition among Subnetworks
-
Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport
-
Demystifying amortized causal discovery with transformers
-
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors
-
Diffusion Domain Expansion: Learning to Coordinate Pre-Trained Diffusion Models
-
Diffusion Models with Group Equivariance
-
Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning
-
DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
-
DiMViS: Diffusion-based Multi-View Synthesis
-
Discrete Diffusion Posterior Sampling for Protein Design
-
Disentangled Representation Learning through Geometry Preservation with the Gromov-Monge Gap
-
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
-
E-ProTran: Efficient Probabilistic Transformers for Forecasting
-
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
-
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders
-
EigenVI: score-based variational inference with orthogonal function expansions
-
Energy-Free Guidance of Geometric Diffusion Models for 3D Molecule Inverse Design
-
Equivariant Flow Matching for Molecular Conformer Generation
-
EVCL: Elastic Variational Continual Learning with Weight Consolidation
-
Exact Soft Analytical Side-Channel Attacks using Tractable Circuits
-
Fast yet Safe: Early-Exiting with Risk Control
-
Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable
-
From Graph Diffusion to Graph Classification
-
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles
-
Generative Autoencoding of Dropout Patterns
-
Generative Classifiers Avoid Shortcut Solutions
-
Generative Design of Decision Tree Policies for Reinforcement Learning
-
Generative Fractional Diffusion Models
-
GLAD: Improving Latent Graph Generative Modeling with Simple Quantization
-
Glauber Generative Model: Discrete Diffusion Models via Binary Classification
-
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
-
Identifying latent state transition in non-linear dynamical systems
-
Improving Consistency Models with Generator-Induced Coupling
-
Improving Flow Matching for Posterior Inference with Physics-based Controls
-
Improving GFlowNets for Text-to-Image Diffusion Alignment
-
Improving GFlowNets with Monte Carlo Tree Search
-
In-Context Learning with Topological Information for LLM-Based Knowledge Graph Completion
-
Incorporating Stability Into Flow Matching
-
Inferring Physiological Properties of Motor Neurons using Neural Posterior Estimation
-
Informed Meta-Learning
-
Investigating Generalization Behaviours of Generative Flow Networks
-
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling
-
Learnability of Parameter-Bounded Bayes Nets
-
Learning high-dimensional mixed models via amortized variational inference
-
Learning Latent Graph Structures and their Uncertainty
-
Lifted Residual Score Estimation
-
Many-to-many Image Generation with Auto-regressive Diffusion Models
-
Modelling Latent Dynamical Systems with Recognition-Parametrised Models
-
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning
-
Neural Ratio Estimators Meet Distributional Shift and Mode Misspecification: A Cautionary Tale from Strong Gravitational Lensing
-
Neurosymbolic Markov Models
-
Non-Parameteric Conformal Distributionally Robust Optimization
-
On Conditional Sampling with Joint Flow Matching
-
On the Expressive Power of Tree-Structured Probabilistic Circuits
-
Policy Gradients for Optimal Parallel Tempering MCMC
-
Predictive Uncertainties Based on Proper Scoring Rules
-
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
-
ProxyTune: Hyperparameter tuning through iteratively refined proxies
-
QGFN: Controllable Greediness with Action Values
-
Quantifying Aleatoric and Epistemic Uncertainty: A Credal Approach
-
Recursive Introspection: Teaching LLM Agents How to Self-Improve
-
Regression-Stratified Sampling for Optimized Algorithm Selection in Time-Constrained Tabular AutoML
-
Regularized Distribution Matching Distillation for One-step Unpaired Image-to-Image Translation
-
Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian Neural Networks
-
Reliability Thresholds for the Bethe Free Energy Approximation
-
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
-
RNA-FrameFlow for de novo 3D RNA Backbone Design
-
Rule-Enhanced Graph Learning
-
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models
-
Scaling the Vocabulary of Non-autoregressive Models for Efficient Generative Retrieval
-
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
-
Simple and Effective Masked Diffusion Language Models
-
Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices
-
SOLMformer - Incorporating Sequence and Observation Level Metadata for Categorical Time Series Modeling
-
Stabilizing the Training of Consistency Models with Score Guidance
-
Stein Variational Newton Neural Network Ensembles
-
Stochastic Concept Bottleneck Models
-
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models
-
Teaching dark matter simulations to speak the halo language
-
Test-Time Adaptation with State-Space Models
-
The Convolution-Closed Hurdle Motif With an Application to Tensor Decomposition
-
The GAN is dead; long live the GAN! A Modern Baseline GAN
-
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
-
Transferable Reinforcement Learning via Generalized Occupancy Models
-
Transformer Conformal Prediction for Time Series
-
Transformer Neural Autoregressive Flows
-
Transformers with Stochastic Competition for Tabular Data Modelling
-
Tuning-Free Alignment of Diffusion Models with Direct Noise Optimization
-
Upper Error Bounds for Score-Based Inverse Problem Solving in Imaging
-
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate
-
Variational Inference with Censored Gaussian Process Regressors
-
von Mises Quasi-Processes for Bayesian Circular Regression
-
Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion