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Tag: matching

  • Flow Matching is Adaptive to Manifold Structures

    Flow Matching is Adaptive to Manifold Structures arXiv:2602.22486v1 Announce Type: new Abstract: Flow matching has emerged as a simulation-free alternative to diffusion-based generative modeling, producing samples by solving an ODE whose time-dependent velocity field is learned along an interpolation between a simple source distribution (e.g., a standard normal) and a target data distribution. Flow-based methods…

  • Discrete Adjoint Matching

    Discrete Adjoint Matching arXiv:2602.07132v1 Announce Type: new Abstract: Computation methods for solving entropy-regularized reward optimization — a class of problems widely used for fine-tuning generative models — have advanced rapidly. Among those, Adjoint Matching (AM, Domingo-Enrich et al., 2025) has proven highly effective in continuous state spaces with differentiable rewards. Transferring these practical successes to…

  • Tilt Matching for Scalable Sampling and Fine-Tuning

    Tilt Matching for Scalable Sampling and Fine-Tuning arXiv:2512.21829v1 Announce Type: new Abstract: We propose a simple, scalable algorithm for using stochastic interpolants to sample from unnormalized densities and for fine-tuning generative models. The approach, Tilt Matching, arises from a dynamical equation relating the flow matching velocity to one targeting the same distribution tilted by a…

  • On The Hidden Biases of Flow Matching Samplers

    On The Hidden Biases of Flow Matching Samplers arXiv:2512.16768v1 Announce Type: new Abstract: We study the implicit bias of flow matching (FM) samplers via the lens of empirical flow matching. Although population FM may produce gradient-field velocities resembling optimal transport (OT), we show that the empirical FM minimizer is almost never a gradient field, even…

  • Time dependent loss reweighting for flow matching and diffusion models is theoretically justified

    Time dependent loss reweighting for flow matching and diffusion models is theoretically justified arXiv:2511.16599v1 Announce Type: new Abstract: This brief note clarifies that, in Generator Matching (which subsumes a large family of flow matching and diffusion models over continuous, manifold, and discrete spaces), both the Bregman divergence loss and the linear parameterization of the generator…

  • On the Equivalence of Optimal Transport Problem and Action Matching with Optimal Vector Fields

    On the Equivalence of Optimal Transport Problem and Action Matching with Optimal Vector Fields arXiv:2510.27385v1 Announce Type: new Abstract: Flow Matching (FM) method in generative modeling maps arbitrary probability distributions by constructing an interpolation between them and then learning the vector field that defines ODE for this interpolation. Recently, it was shown that FM can…

  • Mirror Flow Matching with Heavy-Tailed Priors for Generative Modeling on Convex Domains

    Mirror Flow Matching with Heavy-Tailed Priors for Generative Modeling on Convex Domains arXiv:2510.08929v1 Announce Type: new Abstract: We study generative modeling on convex domains using flow matching and mirror maps, and identify two fundamental challenges. First, standard log-barrier mirror maps induce heavy-tailed dual distributions, leading to ill-posed dynamics. Second, coupling with Gaussian priors performs poorly…

  • Online Matching via Reinforcement Learning: An Expert Policy Orchestration Strategy

    Online Matching via Reinforcement Learning: An Expert Policy Orchestration Strategy arXiv:2510.06515v1 Announce Type: new Abstract: Online matching problems arise in many complex systems, from cloud services and online marketplaces to organ exchange networks, where timely, principled decisions are critical for maintaining high system performance. Traditional heuristics in these settings are simple and interpretable but typically…

  • Batched Stochastic Matching Bandits

    Batched Stochastic Matching Bandits arXiv:2509.04194v1 Announce Type: new Abstract: In this study, we introduce a novel bandit framework for stochastic matching based on the Multi-nomial Logit (MNL) choice model. In our setting, $N$ agents on one side are assigned to $K$ arms on the other side, where each arm stochastically selects an agent from its…

  • Likelihood Matching for Diffusion Models

    Likelihood Matching for Diffusion Models arXiv:2508.03636v1 Announce Type: new Abstract: We propose a Likelihood Matching approach for training diffusion models by first establishing an equivalence between the likelihood of the target data distribution and a likelihood along the sample path of the reverse diffusion. To efficiently compute the reverse sample likelihood, a quasi-likelihood is considered…

  • Momentum Multi-Marginal Schr”odinger Bridge Matching

    Momentum Multi-Marginal Schr”odinger Bridge Matching arXiv:2506.10168v1 Announce Type: new Abstract: Understanding complex systems by inferring trajectories from sparse sample snapshots is a fundamental challenge in a wide range of domains, e.g., single-cell biology, meteorology, and economics. Despite advancements in Bridge and Flow matching frameworks, current methodologies rely on pairwise interpolation between adjacent snapshots. This hinders…

  • On the minimax optimality of Flow Matching through the connection to kernel density estimation

    On the minimax optimality of Flow Matching through the connection to kernel density estimation arXiv:2504.13336v1 Announce Type: new Abstract: Flow Matching has recently gained attention in generative modeling as a simple and flexible alternative to diffusion models, the current state of the art. While existing statistical guarantees adapt tools from the analysis of diffusion models,…

  • Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling

    Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling arXiv:2504.10612v1 Announce Type: cross Abstract: Generative models often map noise to data by matching flows or scores, but these approaches become cumbersome for incorporating partial observations or additional priors. Inspired by recent advances in Wasserstein gradient flows, we propose Energy Matching, a framework that…

  • Robust random graph matching in dense graphs via vector approximate message passing

    Robust random graph matching in dense graphs via vector approximate message passing arXiv:2412.16457v1 Announce Type: new Abstract: In this paper, we focus on the matching recovery problem between a pair of correlated Gaussian Wigner matrices with a latent vertex correspondence. We are particularly interested in a robust version of this problem such that our observation…

  • Propensity-Score Matching Is the Bedrock of Causal Inference

    Propensity-Score Matching Is the Bedrock of Causal Inference And how to get started with it using Python Continue reading on Towards Data Science ยป Ari Joury, PhD Go to original source