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.
MC-pix2pix generates high-quality synthetic sonar data for ATR systems.
Traffic sign recognition is an important component of many advanced driving assistance systems, and it is required for full autonomous driving. Computational performance is usually the bottleneck in using large scale neural networks for this purpose. SqueezeNet is a good candidate for efficient image classification of …
We investigate nearest neighbor and generative models for transferring pose between persons. We take in a video of one person performing a sequence of actions and attempt to generate a video of another person performing the same actions. Our generative model (pix2pix) outperforms k-NN at both generating corresponding f…
Study uses cGAN to translate multispectral to nighttime satellite imagery.
The article proposes a deep learning method to test and infer the Markov property in time series data.
The paper extends Hoeffding's inequality for Markov chains using a generalized concentrability condition.
We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform u…
Geometrical and appearance quality requirements set the limits of the current industrial performance in injection molding. To guarantee the product's quality, it is necessary to adjust the process settings in a closed loop. Those adjustments cannot rely on the final quality because a part takes days to be geometrically…
The study establishes a curvature-dimension condition for discrete Markov chains.
New method detects changes in high-dimensional Markov processes without explicit likelihood evaluation.
PL-MCMC samples from normalizing flows' conditional distributions.
Develops a new framework for conditional independence.
We study discretizations of polynomial processes using finite state Markov processes satisfying suitable moment matching conditions. The states of these Markov processes together with their transition probabilities can be interpreted as Markov cubature rules. The polynomial property allows us to study such rules using …
Transformers can learn Markov processes with constant depth, surprising results.
A new method finds DAG models without ground truth.
In this paper we discuss four problems regarding Markov equivalences for subclasses of loopless mixed graphs. We classify these four problems as finding conditions for internal Markov equivalence, which is Markov equivalence within a subclass, for external Markov equivalence, which is Markov equivalence between subclas…
We explore models for translating abstract musical ideas (scores, rhythms) into expressive performances using Seq2Seq and recurrent Variational Information Bottleneck (VIB) models. Though Seq2Seq models usually require painstakingly aligned corpora, we show that it is possible to adapt an approach from the Generative A…
The article examines entropy-information inequalities for continuous-time Markov chains under curvature-dimension conditions.
Model credit ratings using economic states with Markov chains.
Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences. Several algorithms attempt to do so by discovering the Markov blanket of a target, but they all contain a forward selection step which variables must pas…
CMRFs extend PGMs for topological data, capturing both conditional and marginal dependencies.
Develops a new model to track financial market interconnectedness over time.
We systematically investigate the problem of representing Markov chains by families of random maps, and which regularity of these maps can be achieved depending on the properties of the probability measures. Our key idea is to use techniques from optimal transport to select optimal such maps. Optimal transport theory a…
Generalized Precision Matrix for scalable estimation of nonparametric Markov networks.
A new algorithm for robust causal discovery in small sample sizes.
In this paper, we present a novel framework incorporating a combination of sparse models in different domains. We posit the observed data as generated from a linear combination of a sparse Gaussian Markov model (with a sparse precision matrix) and a sparse Gaussian independence model (with a sparse covariance matrix). …
The paper establishes CLTs for Markov chains and improves sampling algorithms for heavy-tailed distributions.
Derives optimal control conditions using calculus of variations.
We propose a novel framework of estimating systemic risk measures and risk allocations based on Markov chain Monte Carlo (MCMC) methods. We consider a class of allocations whose jth component can be written as some risk measure of the jth conditional marginal loss distribution given the so-called crisis event. By consi…
The paper introduces a method to learn Markov state abstractions for reinforcement learning.
Study approximates financial market with discrete-time models.
Overview of risk-sensitive Markov decision processes with Optimized Certainty Equivalent.
Hidden Markov Chains and Linear-chain CRFs are equivalent.
Study nonparametric estimator for Markov chain transition matrices in offline setting.
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorit…
Unified framework for drawdown risk computation under Markov models.
This paper applies AMP theory to improve learning tasks.
Gaussian Belief Propagation (BP) algorithm is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is well known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network i…
Continuous Hidden Markov Models for Equity Returns
Markov Chain Monte Carlo is repeatedly used to analyze the properties of intractable distributions in a convenient way. In this paper we derive conditions for geometric ergodicity of a general class of nonparametric stochastic volatility models with skewness driven by hidden Markov Chain with switching.
We study optimal investment strategies that maximize expected utility from consumption and terminal wealth in a pure-jump asset price model with Markov-modulated (regime switching) jump-size distributions. We give sufficient conditions for existence of optimal policies and find closed-form expressions for the optimal v…
We show that, for generative classifiers, conditional independence corresponds to linear constraints for the induced discrimination functions. Discrimination functions of undirected Markov network classifiers can thus be characterized by sets of linear constraints. These constraints are represented by a second order fi…
Inferring the causal structure that links n observables is usually based upon detecting statistical dependences and choosing simple graphs that make the joint measure Markovian. Here we argue why causal inference is also possible when only single observations are present. We develop a theory how to generate causal grap…
New insights into Markov chain geometry via positive transition measures.
Paper introduces v-CMC linking causality and utility.
This paper develops tools for nonreversible MCMC with convergence guarantees.
We introduce a general framework for measuring risk in the context of Markov control processes with risk maps on general Borel spaces that generalize known concepts of risk measures in mathematical finance, operations research and behavioral economics. Within the framework, applying weighted norm spaces to incorporate …
The paper defines conditions for learning causal graphs from data with unobserved variables.