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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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80160239319 · May 202619922001200920172026
48 results for diffusion rates

GADD accelerates uniform-rate discrete diffusion models by 2 orders of magnitude.

problem Slow sampling in uniform-rate discrete diffusion models.
method Gibbs-based corrector (GADD) that constructs Gibbs posterior likelihoods directly from the concrete score function.
result Achieves an overall sampling complexity of O(polylog(ε1))\mathcal{O}(\mathrm{polylog} (\varepsilon^{-1})).

The paper develops a neural network method for estimating drift functions of diffusion processes from discrete observations.

problem Nonparametric estimation of drift function for diffusion processes from high-frequency discrete observations.
method Neural network-based estimator for drift function estimation.
result Derives a non-asymptotic convergence rate for the neural network estimator.

New sampling and diffusion models methods introduced without density function assumptions.

problem Sampling and diffusion models without regularity assumptions.
method Inspired by reverse diffusion process, novel sampling and diffusion algorithms.
result Explicit convergence rate and dimension-free particle approximation convergence result.

This work extends diffusion models to handle heavy-tailed targets, improving score estimation and sampling guarantees.

problem Score estimation and sampling guarantees for heavy-tailed targets in diffusion models.
method Kernel density estimation and minimax rates analysis for score estimation and sampling guarantees.
result Sharp minimax rates for score estimation and sampling guarantees for heavy-tailed targets, revealing qualitative differences between exponential and polynomial tails.

New findings on diffusion rates in wind-tree model with rational parameters.

problem Understanding diffusion rates in the wind-tree model with rational parameters.
method Analyzing real numbers in [0,1) as diffusion rates and providing a criterion for Lyapunov spectrum.
result Exhibit an infinite family of wind-tree billiards with the interior of the Lyapunov spectrum being the full square (0,1)^2.

Algorithm minimizes risk for multiclass classification of stochastic diffusion paths.

problem Multiclass classification of stochastic diffusion paths with distinct drift functions.
method Empirical risk minimization using L2 risk.
result Achieves fast rates of convergence under margin assumption.

Study on policy gradient for stochastic bandits using diffusion approximation.

problem Improving policy gradient methods for stochastic bandits with optimal regret bounds.
method Continuous-time diffusion approximation of policy gradient with learning rate analysis.
result Proved optimal regret bound of O(klog(k)log(n)/η)O(k \log(k) \log(n) / η) for η=O(Δ2/log(n))η= O(Δ^2/\log(n)).

We develop a maximum penalized quasi-likelihood estimator for estimating in a nonparametric way the diffusion function of a diffusion process, as an alternative to more traditional kernel-based estimators. After developing a numerical scheme for computing the maximizer of the penalized maximum quasi-likelihood function…

2010-08-14abs ↗pdf ↗

Sharp Lipschitz bounds for flow-matching and diffusion models with optimal sampling rates.

problem Establishing optimal Lipschitz regularity for flow-matching and diffusion models.
method Sharp Lipschitz regularity theory for flow-matching vector fields and diffusion-model scores.
result Achieves optimal sampling rate of d/N\sqrt{d}/N for Euler-type samplers in dimension dd.

Unified framework for pricing various debt securities.

problem Pricing of different types of debt securities under general short-rate processes.
method Unifying framework using continuous-time Markov chain approximations and bi-dimensional diffusion processes.
result Closed-form matrix expressions and efficient algorithms for pricing various debt securities.

Study no-arbitrage conditions in 1D diffusion markets with interest rates.

problem Determining no-arbitrage conditions in 1D diffusion markets with interest rates.
method Established deterministic criteria for no-arbitrage notions in terms of scale function and speed measure.
result Revealed various effects, e.g., NIP not excluded by reflecting boundaries.

New method simulates sticky boundaries in multidimensional diffusions.

problem Simulating sticky boundaries in multidimensional diffusions.
method Approximate sticky diffusion by a Markov chain, using either finite difference or matching local moments.
result Validates both construction methods for first-order simulation schemes.

Graph diffusion processes approximate manifold heat semigroups using graph transition matrices.

problem Approximating manifold heat semigroups from graph data under low regularity conditions.
method Iterating graph transition matrix PP to approximate Qt=etΔQ_t = e^{tΔ}, bounding error in \infty-norm.
result Convergence rates O(N2/(d+6))O(N^{-2/(d+6)}) for manifold heat semigroup approximation, valid for in-sample and out-of-sample.

The paper develops a new formula for financial pricing under multiple interest rates and collateralization.

problem Financial pricing under multiple interest rates and collateralization.
method Derives a change of measure formula for recursive conditional expectations in a jump-diffusion setting.
result Generalizes the change of numéraire technique for multiple interest rates and collateralization.

Study on DiTs' rates of approximation and estimation under various data assumptions.

problem Investigating statistical rates of conditional diffusion transformers.
method Discretization and Taylor expansion of conditional diffusion score function under Hölder smooth data assumption.
result Establishes statistical limits for conditional and unconditional DiTs, offering practical guidance.

The distribution of price returns for a class of uncorrelated diffusive dynamics is considered. The basic assumptions are (1) that there is a "consensus" value associated with a stock, and (2) that the rate of diffusion depends on the deviation of the stock price from the consensus value. We find an analytical expressi…

2004-09-14abs ↗pdf ↗

Gradient guidance improves diffusion models for optimizing specific objectives.

problem Improving diffusion models for specific optimization tasks.
method Established a mathematical framework for gradient-guided diffusion, linking it to optimization theory. Developed a modified gradient guidance method and iteratively fine-tuned version.
result Gradient-guided diffusion models are essentially solutions to regularized optimization problems, preserving latent structure.

Study adapts liquidity model to equity auctions, revealing accelerated event rates and reduced price impact.

problem Understanding and predicting price dynamics in equity auctions.
method Adapted latent/revealed order book framework to equity auctions, measuring order submissions, cancellations, and diffusion rates.
result Equity auctions exhibit accelerated event rates leading to reduced price impact and decreased volatility.

Generative diffusion models are analyzed for their information dynamics.

problem Lack of a unified theoretical understanding of generative diffusion models.
method Integrated perspective connecting information-theoretic, dynamical, and thermodynamic aspects.
result Generative bandwidth is directly governed by the divergence of the score function's vector field.

A discrete diffusion model learns denoising, scoring, and bridging in different coordinates.

problem Understanding what a discrete diffusion model learns in different coordinate systems.
method Rigorous derivation of continuous-time Markov chain ELBO, Oracle Distance theorem, and exact coordinates for optimizer.
result The negative ELBO is exactly equal to the data entropy plus the path KL from the oracle reverse process to the learned one.

Theoretical analysis shows MDMs can be efficient but not for all metrics.

problem Understanding the efficiency-accuracy trade-off of diffusion language models.
method Theoretical analysis of Masked Diffusion Model (MDM) using perplexity and sequence error rate as metrics.
result MDM achieves near-optimal perplexity but requires linear scaling for sequence error rate, highlighting efficiency-accuracy trade-offs.

Study builds a classifier for diffusions with unknown diffusion but known drifts.

problem Multiclass classification of S.D.E. paths with unknown diffusion coefficient.
method Plug-in classifier using nonparametric estimators of drift and diffusion functions.
result Consistent classification procedure with rate of convergence under different assumptions.

Diffusion models adapt to low-dimensional structures for nonparametric density estimation.

problem High-dimensional statistical inference challenges.
method Viewing diffusion models as implicit density estimators and exploiting their low-dimensional structure.
result Achieves minimax optimal rate for total variation distance with factorizable density.

New diffusion models learn distributions from samples with improved error bounds.

problem Statistical guarantees for score-based diffusion models on low-dimensional data.
method Derive finite-sample error bounds for Wasserstein-pp distance.
result Error bounds scale as n1/dp,q(μ)n^{-1 / d^\ast_{p,q}(μ)} for diffusion models.

Diffusion models can generalize well even with coarse scores, thanks to the manifold hypothesis.

problem Understanding why diffusion models generate novel samples with coarse scores.
method Exploring the manifold hypothesis to explain diffusion model behavior.
result Diffusion models trained with coarse scores can achieve near-parametric rates of generalization, faster than estimating the full data distribution.

KIPLMC methods improve statistical inference in latent variable models.

problem Statistical inference in latent variable models.
method Joint diffusion process in parameter and latent variable spaces, with two explicit discretizations.
result KIPLMC methods achieve accelerated convergence rates in Wasserstein-2 distance.

Masking diffusion outperforms other discrete diffusion models by incorporating jump times into the model.

problem Improving the performance of discrete diffusion models.
method Conditioning on the jump schedule of discrete Markov processes.
result Schedule-conditioned discrete diffusion (SCUD) models outperform classical and masking diffusion models.

DDPMs are robust to noisy score estimates and achieve optimal convergence rates in Wasserstein-2 distance.

problem Evaluating the quality of DDPMs in Wasserstein distance with noisy score estimates.
method Established finite-sample guarantees in Wasserstein-2 distance for DDPMs, considering noisy score estimates.
result Optimal convergence rates in Wasserstein-2 distance for DDPMs, matching Gaussian case.

Study shows diffusion models adapt to manifold hypothesis without dimensionality issues.

problem Empirical success of diffusion models in high-dimensional data.
method Developed a new framework connecting diffusion models to Gaussian Processes theory.
result Achieves rates independent of ambient dimension in terms of score learning and sampling complexity.

Proposes new Monte Carlo methods for calibrating local volatility models with stochastic components.

problem Calibrating local volatility models with stochastic drift and diffusion.
method Developed Monte Carlo algorithms for three models: local volatility with stochastic interest rates, stochastic local volatility with deterministic interest rates, and stochastic local volatility with stochastic interest rates.
result Conditions for the existence of local volatility given European option prices, stochastic interest rate model parameters, and correlations.