ICLR 2025 Past Other
Frontiers in Probabilistic Inference: Learning meets Sampling
FPI-ICLR2025
- Submission deadline
- Feb 12, 2025, 00:00 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 (88)
Fetched from OpenReview (v2) on 2026-06-10.
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A Probabilistic Approach to Self-Supervised Learning using Cyclical Stochastic Gradient MCMC
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Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
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Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
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Amortized Posterior Sampling with Diffusion Prior Distillation
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An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
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Approximate Posteriors in Neural Networks: A Sampling Perspective
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Atomic Posterior Ensembles for Simulation-Based Inference
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Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space
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Beyond Schrödinger Bridges: A Least-Squares Approach for Learning Stochastic Dynamics with Unknown Volatility
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Blink of an eye: a simple theory for feature localization in generative models
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Breaking the Likelihood--Quality Trade-off in Diffusion Models by Merging Pretrained Experts
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Can Transformers Learn Full Bayesian Inference In Context?
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Clifford Group Equivariant Diffusion Models For 3D Molecular Generation
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Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond
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Consistency Training with Physical Constraints
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Continuously Tempered Diffusion Samplers
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Controllable Generation via Locally Constrained Resampling
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DDPS: Discrete Diffusion Posterior Sampling for Paths in Layered Graphs
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Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo
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Deep Optimal Sensor Placement for Black Box Stochastic Simulations
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DeepRV: pre-trained spatial priors for accelerated disease mapping.
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Distributionally Robust Posterior Sampling - A Variational Bayes Approach
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Do You See the Shape? Diffusion Models for Noisy Radar Scattering Problems
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Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation
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Efficient Asynchronize Stochastic Gradient Algorithm with Structured Data
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Efficiently Warmstarting MCMC for BNNs
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Electrostatics-based particle sampling and approximate inference
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Ensemble Kalman Sampling and Diffusion Prior in Tandem: A Split Gibbs Framework
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EQM-MPD: EQUIVARIANT ON-MANIFOLD MOTION PLANNING DIFFUSION
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Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms
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Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
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Flat Posterior For Bayesian Model Averaging
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Follow Hamiltonian Leader: An Efficient Energy-Guided Sampling Method
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Generalised Parallel Tempering: Flexible Replica Exchange via Flows and Diffusions
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Global-Order GFlowNets
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Greed is Good: Guided Generation from a Greedy Perspective
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Improving the evaluation of samplers on multi-modal targets
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Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspective
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Inference-Time Prior Adaptation in Simulation-Based Inference via Guided Diffusion Models
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Inherent Exploration via Sampling for Stochastic Policies
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Iterative Importance Fine-tuning of Diffusion Models
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LEAPS: A discrete neural sampler via locally equivariant networks
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Learning Decision Trees as Amortized Structure Inference
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Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks
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Low Stein Discrepancy via Message-Passing Monte Carlo
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Nested Slice Sampling
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Neural Flow Samplers with Shortcut Models
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Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings
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No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
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Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
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Path Planning for Masked Diffusion Models with Applications to Biological Sequence Generation
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PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion
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Performance Evaluation of the Tensor Train Sampler in ML QUBO-based ADMET Classification
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Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models
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PINN-MEP: Continuous Neural Representations for Minimum Energy Path Discovery in Molecular Systems
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Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
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Predicting 3D Structure by Latent Posterior Sampling
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Probabilistic video prediction using conditional score diffusion
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Provable Maximum Entropy Manifold Exploration via Diffusion Models
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Quantification vs. Reduction: On Evaluating Regression Uncertainty
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Quasi-random Multi-Sample Inference for Large Language Models
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Recurrent Memory for Online Interdomain Gaussian Processes
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Rethinking the Training of Diffusion Bridge Samplers: Losses and Exploration
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Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
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Sampling On Metric Graphs
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Sampling through Algorithmic Diffusion in non-convex Perceptron problems
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Scalable Equilibrium Sampling with Sequential Boltzmann Generators
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Scalable Thompson Sampling via Ensemble++
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Scaling Deep Learning Solutions for Transition Path Sampling
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Score-Based Deterministic Density Sampling
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Score-Debiased Kernel Density Estimation
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SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations
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Self-Supervised Learning Encodes Uncertainty
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SFBD: A Method for Training Diffusion Models with Noisy Data
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Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control
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Single-Step Consistent Diffusion Samplers
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Steering Rectified Flow Models in the Vector Field for Controlled Image Generation
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StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces
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Tensor-Train Unsupervised Image Segmentation
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Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
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Uncertainty Quantification for Prior-Fitted Networks using Martingale Posteriors
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Underdamped Diffusion Bridges with Applications to Sampling
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Variational diffusion transformers for conditional sampling of supernovae spectra
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VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference
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von Mises-Fisher Sampling of GloVe Vectors
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Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion
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Wild posteriors in the wild
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α-PFN: In-Context Learning Entropy Search