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.
Algorithm estimates nonparametric mixtures from grouped data.
Paper establishes MLE consistency for market microstructure models.
Consistent estimator derived for confounding strength in observational data.
MTSCI uses diffusion models to impute multivariate time series data with consistency.
We address the problem of estimating the parameters of a time-homogeneous Markov chain given only noisy, aggregate data. This arises when a population of individuals behave independently according to a Markov chain, but individual sample paths cannot be observed due to limitations of the observation process or the need…
LUQ learns QoI from dynamical systems for consistent observation inversion.
Theoretical analysis of MCR for improving imputation quality in partially observed data.
Latent-EnSF improves data assimilation for high-dimensional systems with sparse observations.
New framework learns policies for partially observable systems.
Consistency regularization improves robustness to noisy labels.
Observational cohort studies with oversampled exposed subjects are typically implemented to understand the causal effect of a rare exposure. Because the distribution of exposed subjects in the sample differs from the source population, estimation of a propensity score function (i.e., probability of exposure given basel…
Improves LSTM performance by initializing states via manifold learning.
Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this…
Method maps imperfect simulations to observed stellar spectra using unsupervised domain adaptation.
We prove strong consistency and asymptotic normality of least squares estimators for the subcritical Heston model based on continuous time observations. We also present some numerical illustrations of our results.
Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used in practice to ensure t…
New spectral clustering method handles discrete covariates for better community detection.
New method identifies causal relationships without strong assumptions.
Choice models, which capture popular preferences over objects of interest, play a key role in making decisions whose eventual outcome is impacted by human choice behavior. In most scenarios, the choice model, which can effectively be viewed as a distribution over permutations, must be learned from observed data. The ob…
In scientific inference problems, the underlying statistical modeling assumptions have a crucial impact on the end results. There exist, however, only a few automatic means for validating these fundamental modelling assumptions. The contribution in this paper is a general criterion to evaluate the consistency of a set …
Paper recovers uncertainty from dynamic valuation rules.
Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to l…
Aggregation distorts causal discovery results but recovery is possible with partial linearity or prior.
Improved VAEs learn consistent posterior distributions from missing data.
Graph-based semi-supervised learning is one of the most popular methods in machine learning. Some of its theoretical properties such as bounds for the generalization error and the convergence of the graph Laplacian regularizer have been studied in computer science and statistics literatures. However, a fundamental stat…
The paper examines how insurers can select claims for fraud investigation, proposing a randomized approach.
This work examines the problem of graph learning over a diffusion network when data can be collected from a limited portion of the network (partial observability). The main question is to establish technical guarantees of consistent recovery of the subgraph of probed network nodes, i) despite the presence of unobserved…
Many generative models can be expressed as a differentiable function of random inputs drawn from some simple probability density. This framework includes both deep generative architectures such as Variational Autoencoders and a large class of procedurally defined simulator models. We present a method for performing eff…
Study on estimating causal effects with limited data and multiple environments.
Proposes a new loss function for learning with noisy labels.
Flow models recover causal transformations from observational data and a valid ordering.
Gaussian process (GP) models have become a well-established frameworkfor the adaptive design of costly experiments, and notably of computerexperiments. GP-based sequential designs have been found practicallyefficient for various objectives, such as global optimization(estimating the global maximum or maximizer(s) of a …
A new -means method improves clustering accuracy and robustness.
We consider the problem of finding a consistent upper price bound for exotic options whose payoff depends on the stock price at two different predetermined time points (e.g. Asian option), given a finite number of observed call prices for these maturities. A model-free approach is used, only taking into account that th…
The problem of universal outlying sequence detection is studied, where the goal is to detect outlying sequences among sequences of samples. A sequence is considered as outlying if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequenc…
Paper proposes efficient algorithm for recovering sparsity pattern from deterministic missing data.
We revisit the problem of estimating the parameters of a partially observed diffusion process, consisting of a hidden state process and an observed process, with a continuous time parameter. The estimation is to be done online, i.e. the parameter estimate should be updated recursively based on the observation filtratio…
We derive high-probability finite-sample uniform rates of consistency for -NN regression that are optimal up to logarithmic factors under mild assumptions. We moreover show that -NN regression adapts to an unknown lower intrinsic dimension automatically. We then apply the -NN regression rates to establish new …
Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements. We study this problem from a statistical framework in which one of the graphs is an errorfu…
Prefix consistency improves model reliability by weighting answers based on their reproducibility.
Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest. The key object that defines an ExGM is often referred to as a graphon. This non-parametric perspective on network modeling poses challenging questions on how to make inference on the graphon und…
Paper proves identifiability and consistency of hub model for network inference.
Boosting method for causal SEMs from observational data.
This paper investigates Frequentist consistency properties of the posterior distributions constructed via Generalized Variational Inference (GVI). A number of generic and novel strategies are given for proving consistency, relying on the theory of -convergence. Specifically, this paper shows that under minimal regul…
It is shown that any handle-irreducible summand of every stable-ribbon surface-link is a unique ribbon surface-link up to equivalences, so that every stable-ribbon surface-link is a ribbon surface-link. This is a generalization of a previously observed result for a stably trivial surface-link. Two observations are give…
Inspired by Strotz's consistent planning strategy, we formulate the infinite horizon mean-variance stopping problem as a subgame perfect Nash equilibrium in order to determine time consistent strategies with no regret. Equilibria among stopping times or randomized stopping times may not exist. This motivates us to cons…
The paper demonstrates that a pure-diffusion 3/2 model is able to capture the observed upward-sloping implied volatility skew in VIX options. This observation contradicts a common perception in the literature that jumps are required for the consistent modelling of equity and VIX derivatives. The pure-diffusion model, h…
The Viterbi process can be extended indefinitely in a pairwise Markov model.