NeurIPS 2025 Past Other
2nd edition of Frontiers in Probabilistic Inference: Learning meets Sampling
FPI-NEURIPS2025
- Submission deadline
- Sep 3, 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 (66)
Fetched from OpenReview (v2) on 2026-06-10.
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$p\textrm{-less}$ Sampling: A Robust Hyperparameter-Free Approach for LLM Decoding
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A Gradient Flow approach to Solving Inverse Problems with Latent Diffusion Models
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A Priori Sampling of Transition States with Guided Diffusion
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A Sampling-Based Domain Generalization Study with Diffusion Generative Models
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A Unification of Discrete, Gaussian, and Simplicial Diffusion
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Adaptive Destruction Processes for Diffusion Samplers
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Adaptive Inference Scaling via Monte Carlo Sampling
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An Eulerian Perspective on Straight-Line Sampling
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Bring fusion energy closer: challenges for probabilistic inference in the multiphysics, multiscale environment of fusion devices
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Can We Estimate The Entropy Of Arbitrary Distributions Known Up To A Normalization Constant?
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Categorical Flow Matching via Simplex-to-Euclidean Bijections
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Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
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Combined Representation and Generation with Diffusive State Prediction Information Bottleneck
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Compression Meets Sampling: On Energy-Efficient Random Variate Generation
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Constrained Flow Optimization via Sequential Fine-Tuning for Molecular Design
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Control Consistency Losses for Diffusion Bridges
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Convergences guarantees of GFlowNets
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Counterdiabatic Hamiltonian Monte Carlo
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Data Generation without Function Estimation
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Data-to-Energy Stochastic Dynamics
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DDS-E-Sim: A Transformer-based Probabilistic Generative Framework for Simulating Error-Prone DNA Sequences for DNA Data Storage
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Discrete Stochastic Localization for Non-Autoregressive Generation
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e-SimFT: Pareto-Optimal Sampling of Generative Design Models Fine-tuned with Simulation Feedback
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Energy-Based Physics-Informed Diffusion Transformers Sampling for Time Series Forecasting
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Enhancing Diversity in Large Language Models via Determinantal Point Processes
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Entangled Schrödinger Bridge Matching
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Frame-based Equivariant Diffusion Models for 3D Molecular Generation
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From Predictors to Samplers via the Training Trajectory
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Generalised Flow Maps on Riemannian Manifolds
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GNN-Guided Block Selection in Gibbs MCMC
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Improving Constrained Language Generation via Self-Distilled Twisted Sequential Monte Carlo
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Inference-time alignment of language models by importance sampling on pre-logit space
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Infinite Dimensional Adjoint Sampler: Scalable Sampling on Function Spaces
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Ising Machines for Model Predictive Path Integral-Based Optimal Control
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Latent Spaces for Langevin Dynamics
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Learning Boltzmann Generators via Constrained Mass Transport
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Learning Discrete Distributions from Metastable Data via Pseudo-Likelihood
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Learning Paths for Dynamic Measure Transport: A Control Perspective
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Learning Velocity Prior-Guided Hamiltonian-Jacobi Flows with Unbalanced Optimal Transport
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Machine-Learned Sampling of Conditioned Path Measures
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Model Agnostic Conditioning of Boltzmann Generators for Peptide Cyclization
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moPPIt-v3: Motif-Specific Peptides Generated via Multi-Objective-Guided Discrete Flow Matching
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Multi-Objective Nanobody Design via Masked Discrete Diffusion with Simplex Refinement
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Multimarginal Flow Matching with Adversarially Learnt Interpolants
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Optimizing Input of Denoising Score Matching is Biased Towards Higher Score Norm
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Ordered Diversity Sampling for Text
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Particle Monte Carlo methods for Lattice Field Theory
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Pokie: Posterior Accuracy and Model Comparison
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Probabilistic Modeling of Antibody Structural Dynamics
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Relative Trajectory Balance is equivalent to Trust-PCL
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Robust Multi-task Modeling for Bayesian Optimization via In-Context Learning
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Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
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Sampling from Energy distributions with Target Concrete Score Identity
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Sampling Strategies for Transformer-Based Mechanism Synthesis
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ScooBDoob: Schrödinger Bridge with Doob’s h-Transform for Molecular Dynamics
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Solving Inverse Problems with Stochastic Interpolants: Self-Consistent Generative Modeling from Corrupted Data
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Steering Pretrained Drafters during Speculative Decoding
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Token-Level Guided Discrete Diffusion for Membrane Protein Design
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Tokenised Flow Matching for Hierarchical Simulation Based Inference
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torchgfn: A PyTorch GFlowNet library
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Towards Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space
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Towards Integrating Uncertainty for Domain-Agnostic Segmentation
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Uncertainty Weighted Deep Ensemble to Enhance Protein Property Prediction
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Value Matching: Scalable and Gradient-Free Reward-Guided Flow Adaptation
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Variational Entropy Search is Just 1D Regression
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Weighted Conditional Flow Matching