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.
We consider models of the population or opinion dynamics which result in the non-linear stochastic differential equations (SDEs) exhibiting the spurious long-range memory. In this context, the correspondence between the description of the birth-death processes as the continuous-time Markov chains and the continuous SDE…
A fundamental problem in Bayesian inference and statistical machine learning is to efficiently sample from multimodal distributions. Due to metastability, multimodal distributions are difficult to sample using standard Markov chain Monte Carlo methods. We propose a new sampling algorithm based on a birth-death mechanis…
Paper disproves conjecture about log-Sobolev constants.
Decision trees are flexible models that are well suited for many statistical regression problems. In a Bayesian framework for regression trees, Markov Chain Monte Carlo (MCMC) search algorithms are required to generate samples of tree models according to their posterior probabilities. The critical component of such an …
We propose a general method to obtain approximation of the first passage time distribution for the birth-death processes. We rely on the general properties of birth-death processes, Keilson's theorem and the concept of Riemann sum to obtain closed-form expressions. We apply the method to the three selected birth-death …
Study birth-death dynamics for sampling Gibbs measures with nonconvex potentials.
The paper develops a stationary-distribution theory for Random Forest ensemble size selection.
The study establishes a curvature-dimension condition for discrete Markov chains.
New method uses birth-death process and exploration component to accelerate sampling from multimodal distributions.
Neural models learn continuous-time Markov chain transition rates from data.
Near a birth-death critical point in a one-parameter family of gradient flows, there are precisely two Morse critical points of index difference one on the birth side. This paper gives a self-contained proof of the folklore theorem that these two critical points are joined by a unique gradient trajectory up to time-shi…
We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle filter (APF) with automatic Rao-Blackwellization via delayed sampling. Birth-death models of evolution are an important family of phylogenetic…
FS&P uses birth-death process to ensure global convergence of stochastic conic particle gradient descent.
Paper proposes scalable method for analyzing multi-omic data.
Neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for the favorable training properties of "overparameterized" models. In this regime, gradient descent obeys a deterministic partial differential equation (PDE) that converges to a glo…
In this paper, we study curvature dimension conditions on birth-death processes which correspond to linear graphs, i.e., weighted graphs supported on the infinite line or the half line. We give a combinatorial characterization of Bakry and Émery's condition for linear graphs and prove the triviality of edge w…
Bayesian inference for biochemical reaction networks using jump-diffusion approximations.
New model assesses risks of staking and borrowing in smart contracts.
Bayesian method learns graph structures from Gaussian data efficiently.
Flow Matching for count data improves sample quality and efficiency.
The surgery technique of Gromov and Lawson may be used to construct families of positive scalar curvature metrics which are parameterised by Morse functions. This has played an important role in the study of the space of metrics of positive scalar curvature on a smooth manifold and its corresponding moduli spaces. In t…
New RL algorithm reduces regret in birth-death queueing problems.
Framework uses deep learning and statistical models to solve PDEs with discontinuous coefficients.
Paper compares higher torsions and removes fiberwise Morse function assumption.
USD algorithm transports distributions with or without mass conservation.
New method optimizes multiple objectives using particle dynamics and gradient flow.
A microscopic approach to macroeconomic features is intended. A model for macroeconomic behavior under heterogeneous spatial economic conditions is reviewed. A birth-death lattice gas model taking into account the influence of an economic environment on the fitness and concentration evolution of economic entities is nu…
According to Kiyoshi Igusa a generalized Morse function on an n-dimensional manifold M is a smooth function with only Morse and birth-death singularities and a framed function is a generalized Morse function with an additional structure: a framing of the negative eigenspace at each critical point of the function f. In …
We study the maps induced on link Floer homology by elementary decorated link cobordisms. We compute these for births, deaths, stabilizations, and destabilizations, and show that saddle cobordisms can be computed in terms of maps in a decorated skein exact triangle that extends the oriented skein exact triangle in knot…
Unified framework for analyzing gradient flows of measures with exponential decay of entropy.
Study finds on-chain data can proxy off-chain cryptocurrency pricing.
In recent years, multi object tracking (MOT) problem has drawn attention to it and has been studied in various research areas. However, some of the challenging problems including time dependent cardinality, unordered measurement set, and object labeling remain unclear. In this paper, we propose robust nonparametric met…
The study connects monopole chains to Higgs bundles and classifies symmetric chains.
New proof of chain duality for simplicial complexes.
Improves multi-label classification with a new network model.
Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collections often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics' distribution and popularity are time-evolving. Several mode…
Reduces identity testing of reversible Markov chains to simpler symmetric chain tests.
We present a new family of models that is based on graphs that may have undirected, directed and bidirected edges. We name these new models marginal AMP (MAMP) chain graphs because each of them is Markov equivalent to some AMP chain graph under marginalization of some of its nodes. However, MAMP chain graphs do not onl…
We introduce some chain maps between Khovanov complexes. Each of the chain maps commutes with a chain homotopy map and a retraction maps which obtain a Reidemeister invariance of Khovanov homology.
Mack's estimator improves chain ladder prediction for large exposure insurance models.
Polynomial invariants classify molecular chains based on their contact arrangements.
Unified Morse-Bott-Smale chain complex, resolves well-definedness issue.
This study aims to improve communication between fragmented blockchain systems in finance.
We analyze a new Markov chain model for better sampling and optimization.
This work improves generalisation bounds using chaining and information theory.
The aim of this paper is to define a chain level refinement of the Batalin-Vilkovisky (BV) algebra structure on the homology of the free loop space of a closed, oriented -manifold. For this purpose, we define a (nonsymmetric) cyclic dg operad which consists of "de Rham chains" of free loops with marked points…
Study on identifying AMP chain graph models under known and unknown component decompositions.
No hyperbolic group can have an infinite chain of free subgroups of fixed rank.