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.
Double Q-learning has the same mean-squared error as Q-learning under certain conditions.
This study calculates the maximum error of a famous estimation method.
Paper solves outlier robust mean estimation near breakdown point.
New method optimizes tail dependence coefficient estimation.
This paper concerns error bounds for recursive equations subject to Markovian disturbances. Motivating examples abound within the fields of Markov chain Monte Carlo (MCMC) and Reinforcement Learning (RL), and many of these algorithms can be interpreted as special cases of stochastic approximation (SA). It is argued tha…
Nonparametric modeling approaches show very promising results in the area of system identification and control. A naturally provided model confidence is highly relevant for system-theoretical considerations to provide guarantees for application scenarios. Gaussian process regression represents one approach which provid…
The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been…
Cryptocurrency prices predicted using LSTM, SVM, and polynomial regression.
Improved multi-task averaging reduces mean squared error in high-dimensional data.
This letter presents an improved version of diffusion least mean ppower (LMP) algorithm for distributed estimation. Instead of sum of mean square errors, a weighted sum of mean square error is defined as the cost function for global and local cost functions of a network of sensors. The weight coefficients are updated b…
Study on LMMSE estimation with model mismatch, quantifying MSE trade-offs.
A fast method for LOOCV in k-NN regression reduces computation time.
We consider a univariate semimartingale model for (the logarithm of) an asset price, containing jumps having possibly infinite activity (IA). The nonparametric threshold estimator of the integrated variance IV proposed in Mancini 2009 is constructed using observations on a discrete time grid, and precisely it sums up t…
This paper presents a stochastic behavior analysis of a kernel-based stochastic restricted-gradient descent method. The restricted gradient gives a steepest ascent direction within the so-called dictionary subspace. The analysis provides the transient and steady state performance in the mean squared error criterion. It…
Artificial neural network training with stochastic gradient descent can be destabilized by "bad batches" with high losses. This is often problematic for training with small batch sizes, high order loss functions or unstably high learning rates. To stabilize learning, we have developed adaptive learning rate clipping (A…
ABae efficiently computes subset means with expensive predicates using stratified sampling.
Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adaptation to combination MCC and combination to adaptation MCC, are developed to deal with the distributed estimation over network in impulsive (long-tailed) noise environments. The cost functions used in distri…
Enhances RL for jump processes using MSBVE algorithm.
Paper optimizes diffusion models for denoising tasks with theoretical guarantees.
Despite the simplicity and intuitive interpretation of Minimum Mean Squared Error (MMSE) estimators, their effectiveness in certain scenarios is questionable. Indeed, minimizing squared errors on average does not provide any form of stability, as the volatility of the estimation error is left unconstrained. When this v…
Improved stock volume prediction using Kalman Filters with various hidden states.
Optimizes embedding accuracy for data variance and error.
Network agents solve adaptive regression problems with compressed signals.
The paper examines prediction and estimation risks of ridgeless least squares under general error assumptions.
We consider adaptive system identification problems with convex constraints and propose a family of regularized Least-Mean-Square (LMS) algorithms. We show that with a properly selected regularization parameter the regularized LMS provably dominates its conventional counterpart in terms of mean square deviations. We es…
The paper proposes a method to improve forecast combination accuracy using portfolio theory.
This book introduces linear models and their theories rigorously.
Efficiently estimates private least squares with linear error growth.
Paper analyzes convergence of two time-scale stochastic approximation using martingale approach.
Optimizes calibration error estimators for better classifier trustworthiness.
The paper analyzes how data augmentation affects the test error in regression models.
New algorithm for mean estimation in add-remove model achieves optimal error.
This work addresses privacy in Bayesian estimation, achieving near-optimal error rates.
In this paper, we consider a statistical problem of learning a linear model from noisy samples. Existing work has focused on approximating the least squares solution by using leverage-based scores as an importance sampling distribution. However, no finite sample statistical guarantees and no computationally efficient o…
Constrained adaptive filtering algorithms inculding constrained least mean square (CLMS), constrained affine projection (CAP) and constrained recursive least squares (CRLS) have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under mean square error (…
While the objective in traditional multi-armed bandit problems is to find the arm with the highest mean, in many settings, finding an arm that best captures information about other arms is of interest. This objective, however, requires learning the underlying correlation structure and not just the means of the arms. Se…
Unified framework for robust A/B testing under model misspecification.
The stochastic gradient descent (SGD) optimization algorithm plays a central role in a series of machine learning applications. The scientific literature provides a vast amount of upper error bounds for the SGD method. Much less attention as been paid to proving lower error bounds for the SGD method. It is the key cont…
Transformer model with mixed-frequency data improves stock volatility prediction.
Improved estimator for least squares using random projections achieves smaller error.
Biased mean regression estimates factors exceeding expected loss or radiation release severity.
This study explains gradient flow dynamics in neural networks for small initialisation.
JSRT improves regression tree performance by incorporating global node information.
Asymptotic error distribution for approximation of a stochastic integral with respect to continuous semimartingale by Riemann sum with general stochastic partition is studied. Effective discretization schemes of which asymptotic conditional mean-squared error attains a lower bound are constructed. Two applications are …
Unified framework for finite-sample RL algorithms using Lyapunov theory.
Paper provides statistical guarantees for GNNs in link prediction.
In this paper, we provide a unified analysis of temporal difference learning algorithms with linear function approximators by exploiting their connections to Markov jump linear systems (MJLS). We tailor the MJLS theory developed in the control community to characterize the exact behaviors of the first and second order …
This paper describes a recursive estimation procedure for multivariate binary densities (probability distributions of vectors of Bernoulli random variables) using orthogonal expansions. For covariates, there are basis coefficients to estimate, which renders conventional approaches computationally prohibitive …