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.
DPERC efficiently estimates covariance matrices for mixed data with missing values.
This paper compares imputation and direct parameter estimation methods for missing data in correlation matrix visualization.
Develops a direct debiased machine learning framework using Bregman divergence.
We present a distributed (non-Bayesian) learning algorithm for the problem of parameter estimation with Gaussian noise. The algorithm is expressed as explicit updates on the parameters of the Gaussian beliefs (i.e. means and precision). We show a convergence rate of with the constant term depending on the numb…
Estimates causal effects using machine learning for binary treatment and mediator.
LayerNorm transformers have dead directions that can be read from their parameters alone.
Paper tackles hyper-gradient estimation in decentralized FL over time-varying networks.
Regularization is a popular technique in machine learning for model estimation and avoiding overfitting. Prior studies have found that modern ordered regularization can be more effective in handling highly correlated, high-dimensional data than traditional regularization. The reason stems from the fact that the ordered…
The modelling of data on a spherical surface requires the consideration of directional probability distributions. To model asymmetrically distributed data on a three-dimensional sphere, Kent distributions are often used. The moment estimates of the parameters are typically used in modelling tasks involving Kent distrib…
We propose directed time series regression, a new approach to estimating parameters of time-series models for use in certainty equivalent model predictive control. The approach combines merits of least squares regression and empirical optimization. Through a computational study involving a stochastic version of a well …
New algorithms estimate Jacobian matrices for large-scale machine learning.
Proposes a new estimator for causal mediation with continuous treatments.
Paper proposes DAG-DB for learning discrete DAGs via backpropagation.
New algorithm for estimating MLR parameters with non-Gaussian noise.
We introduce the "NoBackTrack" algorithm to train the parameters of dynamical systems such as recurrent neural networks. This algorithm works in an online, memoryless setting, thus requiring no backpropagation through time, and is scalable, avoiding the large computational and memory cost of maintaining the full gradie…
Paper efficiently infers differential parameters in time-varying models using time score matching.
A new method optimizes neural sequence models for better task performance.
Parametric images provide insight into the spatial distribution of physiological parameters, but they are often extremely noisy, due to low SNR of tomographic data. Direct estimation from projections allows accurate noise modeling, improving the results of post-reconstruction fitting. We propose a method, which we name…
Direct approach for handling contextual bandits with latent state dynamics.
Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, rivaling other state-of-the-art deep RL methods. Yet, there are two limitations in the current ES practice that may hinder its otherwise further capabilities. First, most current methods rely on Monte Carlo type …
Novel approach for SEM in small samples with .
Proposes a new measure to evaluate stability of statistical parameters under distributional shifts.
Estimation of density derivatives is a versatile tool in statistical data analysis. A naive approach is to first estimate the density and then compute its derivative. However, such a two-step approach does not work well because a good density estimator does not necessarily mean a good density-derivative estimator. In t…
Estimates parameters in max-linear Bayesian networks with noise.
IGNIS uses neural networks to estimate copula parameters robustly.
Policy gradient methods in reinforcement learning update policy parameters by taking steps in the direction of an estimated gradient of policy value. In this paper, we consider the statistically efficient estimation of policy gradients from off-policy data, where the estimation is particularly non-trivial. We derive th…
Direct neural ratio estimator for likelihood-free inference.
A new method recovers latent potentials from graph flows, preserving ordering and stability.
We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a directed graph that allows…
Gibbs sampling is a workhorse for Bayesian inference but has several limitations when used for parameter estimation, and is often much slower than non-sampling inference methods. SAME (State Augmentation for Marginal Estimation) \cite{Doucet99,Doucet02} is an approach to MAP parameter estimation which gives improved pa…
Efficiently estimates missing data parameters without iterations.
Paper estimates Hurst parameter from implied volatilities.
New meta-learning method improves domain generalization by balancing parameters closer to domain centroids.
Algorithm identifies Pareto front using multiple context directions and reuses exploration samples.
In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes (RANS) equations combined with the turbulence model are applied to solve incompr…
Study identifies parameters in causal models with latent confounding.
This work simplifies Bayesian inference for neural networks by identifying influential parameter directions.
Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables im…
Psychiatric neuroscience is increasingly aware of the need to define psychopathology in terms of abnormal neural computation. The central tool in this endeavour is the fitting of computational models to behavioural data. The most prominent example of this procedure is fitting reinforcement learning (RL) models to decis…
This paper analyzes quantiles of heavy-tailed distributions, separating projection direction and quantile threshold effects.
New IDS algorithm refines parameter norm bounds for better bandit performance.
Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear parameterization of an interaction model can be more expressive than a direct parameterization based on probabilities, leading to a powerful way of defining restrictions derived from marginal, conditional and context-specific ind…
In stochastic gradient descent, especially for neural network training, there are currently dominating first order methods: not modeling local distance to minimum. This information required for optimal step size is provided by second order methods, however, they have many difficulties, starting with full Hessian having…
New CRB derived for curved models using extrinsic geometry.
Estimates high-dimensional posterior densities by marginal distributions and neural networks.
This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial cha…
Optimal neural network approximation for Wasserstein gradient direction via convex optimization.
We consider inference about a scalar parameter under a non-parametric model based on a one-step estimator computed as a plug in estimator plus the empirical mean of an estimator of the parameter's influence function. We focus on a class of parameters that have influence function which depends on two infinite dimensiona…