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.
Study robust linear regression without distributional assumptions for heavy-tailed responses.
In this paper, we study the price responsiveness of electricity consumption from empirical commercial and industrial load data obtained from Texas. Employing a dynamical system perspective, we show that price responsive demand can be modeled as a hybrid of a Hammerstein model with delay following a price surge, and a l…
New geometric theory explains nonuniform origami responses.
Objects are represented in sensory systems by continuous manifolds due to sensitivity of neuronal responses to changes in physical features such as location, orientation, and intensity. What makes certain sensory representations better suited for invariant decoding of objects by downstream networks? We present a theory…
Paper investigates optimal interpolation methods in linear regression.
A new model for complex cells accounts for insensitivity to image shifts.
The paper provides risk bounds for learning many response functions using linear regression.
Statistical properties of order-driven double-auction markets with Bid-Ask spread are investigated through the dynamical quantities such as response function. We first attempt to utilize the so-called {\it Madhavan-Richardson-Roomans model} (MRR for short) to simulate the stochastic process of the price-change in empir…
LaRT models LLMs' response accuracy and CoT length to evaluate reasoning ability and speed.
Estimates personalized treatment response curves using covariates.
Mean Field Variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is its (sometimes severe) underestimates of the uncertainty of model variables and lack of information about model variable covariance.…
Item response theory (IRT) is a non-linear generative probabilistic paradigm for using exams to identify, quantify, and compare latent traits of individuals, relative to their peers, within a population of interest. In pre-existing multidimensional IRT methods, one requires a factorization of the test items. For this t…
Theory for soft-margin classifiers on object manifolds.
Proposes a new random forest weighted local Fréchet regression method.
We simplify complex regression coefficients using linearization and feature comparison.
Inference methods are often formulated as variational approximations: these approximations allow easy evaluation of statistics by marginalization or linear response, but these estimates can be inconsistent. We show that by introducing constraints on covariance, one can ensure consistency of linear response with the var…
The standard linear and logistic regression models assume that the response variables are independent, but share the same linear relationship to their corresponding vectors of covariates. The assumption that the response variables are independent is, however, too strong. In many applications, these responses are collec…
Paper uses SLT to improve model selection for SHM.
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often mo…
The study uses response theory to understand RNNs processing input signals.
Optimum in Convex Hulls (OCH) generalizes clinical trial results to broader populations.
This paper compares two loss functions for learning from aggregated responses and introduces an interpolating estimator.
Eigen component analysis combines quantum mechanics with machine learning for efficient data analysis.
A new neural network model uses polynomial chaos theory to improve neural signal processing.
Kernel models learn low-dimensional predictive subspaces from input data.
Extends RRR to capture nonlinear interactions in multi-response regression.
Develops a new theory for neural systems stability and width effects.
Enhances preference learning by incorporating response times into binary choices.
Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is that it underestimates the uncertainty of model variables (sometimes severely) and provides no information about model variable cova…
Our objective is to estimate the unknown compositional input from its output response through an unknown system after estimating the inverse of the original system with a training set. The proposed methods using artificial neural networks (ANNs) can compete with the optimal bounds for linear systems, where convex optim…
In this paper we formulate a geometric theory of the mechanics of growing solids. Bulk growth is modeled by a material manifold with an evolving metric. Time dependence of metric represents the evolution of the stress-free (natural) configuration of the body in response to changes in mass density and "shape". We show t…
Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving test scoring and better informing p…
Objective: Predict individual septic children's personalized physiologic responses to vasoactive titrations by training a Recurrent Neural Network (RNN) using EMR data. Materials and Methods: This study retrospectively analyzed EMR of patients admitted to a pediatric ICU from 2009 to 2017. Data included charted time se…
New algorithm speeds up IRT model fitting for large datasets.
This article considers algorithmic and statistical aspects of linear regression when the correspondence between the covariates and the responses is unknown. First, a fully polynomial-time approximation scheme is given for the natural least squares optimization problem in any constant dimension. Next, in an average-case…
Analyzing real data on international trade covering the time interval 1950-2000, we show that in each year over the analyzed period the network is a typical representative of the ensemble of maximally random weighted networks, whose directed connections (bilateral trade volumes) are only characterized by the product of…
Study on recovering sparse linear classifiers from mixed binary responses.
We study the following basic machine learning task: Given a fixed set of -dimensional input points for a linear regression problem, we wish to predict a hidden response value for each of the points. We can only afford to attain the responses for a small subset of the points that are then used to construct linear pre…
The paper reformulates regression in infinite dimensions as an inverse problem, showing it's equivalent to compact inverse problems.
Quantum connections replace metrics with operator inner products.
New Bayesian method for sparse multidimensional item response theory.
Interpretable text-response modelling for structured outcomes
Study presents MMC model for better fitting multiple choice data.
Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.
Unified framework for estimating reward functions in competitive games.
Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is that it underestimates the uncertainty of model variables (sometimes severely) and provides no information about model variable cova…
We propose methods for estimating correspondence between two point sets under the presence of outliers in both the source and target sets. The proposed algorithms expand upon the theory of the regression without correspondence problem to estimate transformation coefficients using unordered multisets of covariates and r…
New neural network models for complex functional data analysis.