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Tag: discrete

  • 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…

  • Corrected Samplers for Discrete Flow Models

    Corrected Samplers for Discrete Flow Models arXiv:2601.22519v1 Announce Type: new Abstract: Discrete flow models (DFMs) have been proposed to learn the data distribution on a finite state space, offering a flexible framework as an alternative to discrete diffusion models. A line of recent work has studied samplers for discrete diffusion models, such as tau-leaping and…

  • Detecting Stochasticity in Discrete Signals via Nonparametric Excursion Theorem

    Detecting Stochasticity in Discrete Signals via Nonparametric Excursion Theorem arXiv:2601.06009v1 Announce Type: new Abstract: We develop a practical framework for distinguishing diffusive stochastic processes from deterministic signals using only a single discrete time series. Our approach is based on classical excursion and crossing theorems for continuous semimartingales, which correlates number $N_varepsilon$ of excursions of magnitude…

  • BayesSum: Bayesian Quadrature in Discrete Spaces

    BayesSum: Bayesian Quadrature in Discrete Spaces arXiv:2512.16105v1 Announce Type: new Abstract: This paper addresses the challenging computational problem of estimating intractable expectations over discrete domains. Existing approaches, including Monte Carlo and Russian Roulette estimators, are consistent but often require a large number of samples to achieve accurate results. We propose a novel estimator, emph{BayesSum}, which…

  • Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling

    Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling arXiv:2510.01329v1 Announce Type: new Abstract: Standard discrete diffusion models treat all unobserved states identically by mapping them to an absorbing [MASK] token. This creates an ‘information void’ where semantic information that could be inferred from unmasked tokens is lost between denoising steps. We introduce Continuously Augmented…

  • Enhancing Gradient-based Discrete Sampling via Parallel Tempering

    Enhancing Gradient-based Discrete Sampling via Parallel Tempering arXiv:2502.19240v1 Announce Type: new Abstract: While gradient-based discrete samplers are effective in sampling from complex distributions, they are susceptible to getting trapped in local minima, particularly in high-dimensional, multimodal discrete distributions, owing to the discontinuities inherent in these landscapes. To circumvent this issue, we combine parallel tempering, also…

  • Discrete Markov Probabilistic Models

    Discrete Markov Probabilistic Models arXiv:2502.07939v1 Announce Type: new Abstract: This paper introduces the Discrete Markov Probabilistic Model (DMPM), a novel algorithm for discrete data generation. The algorithm operates in the space of bits ${0,1}^d$, where the noising process is a continuous-time Markov chain that can be sampled exactly via a Poissonian clock that flips labels…

  • Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application

    Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application arXiv:2412.05906v1 Announce Type: new Abstract: We study the discrete-time linear-quadratic (LQ) control model using reinforcement learning (RL). Using entropy to measure the cost of exploration, we prove that the optimal feedback policy for the problem must be Gaussian type. Then, we apply the results…

  • Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces

    Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces arXiv:2412.01019v1 Announce Type: new Abstract: Energy-based models (EBMs) offer a flexible framework for probabilistic modelling across various data domains. However, training EBMs on data in discrete or mixed state spaces poses significant challenges due to the lack of robust and fast sampling…