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.
The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.
Paper presents a method to reduce prediction variance of DNNs for unknown systems.
Algorithm estimates common mean from Gaussian variables with unknown variances.
This paper addresses error bounds and posterior variance for Gaussian process regression.
We study confidence intervals based on hard-thresholding, soft-thresholding, and adaptive soft-thresholding in a linear regression model where the number of regressors may depend on and diverge with sample size . In addition to the case of known error variance, we define and study versions of the estimators when…
In this work, we consider the identifiability assumption of Gaussian linear structural equation models (SEMs) in which each variable is determined by a linear function of its parents plus normally distributed error. It has been shown that linear Gaussian structural equation models are fully identifiable if all error va…
The lasso has been studied extensively as a tool for estimating the coefficient vector in the high-dimensional linear model; however, considerably less is known about estimating the error variance in this context. In this paper, we propose the natural lasso estimator for the error variance, which maximizes a penalized …
When randomized ensembles such as bagging or random forests are used for binary classification, the prediction error of the ensemble tends to decrease and stabilize as the number of classifiers increases. However, the precise relationship between prediction error and ensemble size is unknown in practice. In the standar…
The paper estimates common mean of entangled Gaussians with bounded variances.
Variational Bayes (VB) is a recent approximate method for Bayesian inference. It has the merit of being a fast and scalable alternative to Markov Chain Monte Carlo (MCMC) but its approximation error is often unknown. In this paper, we derive the approximation error of VB in terms of mean, mode, variance, predictive den…
Optimizes budgeted evaluations of LLMs by allocating queries to judges efficiently.
Paper tackles unknown variances in best-arm identification.
Develops new e-processes and confidence sequences for Gaussian means with unknown variance.
We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sample size n and may diverge with n. In addition to the case of known error-variance, we define and study versions of the estimators when the error-varian…
New algorithms improve best-arm identification with varying rewards.
We present a new method for high-dimensional linear regression when a scale parameter of the additive errors is unknown. The proposed estimator is based on a penalized Huber -estimator, for which theoretical results on estimation error have recently been proposed in high-dimensional statistics literature. However, t…
Sharp inequalities for matrix means with unknown variance.
We consider least squares estimation in a general nonparametric regression model. The rate of convergence of the least squares estimator (LSE) for the unknown regression function is well studied when the errors are sub-Gaussian. We find upper bounds on the rates of convergence of the LSE when the errors have uniformly …
Paper shows MoM is optimal under adversarial contamination for certain distributions.
New strategy optimally identifies best arm in unknown variance Gaussian bandits.
We study the problem of estimating low-rank matrices from linear measurements (a.k.a., matrix sensing) through nonconvex optimization. We propose an efficient stochastic variance reduced gradient descent algorithm to solve a nonconvex optimization problem of matrix sensing. Our algorithm is applicable to both noisy and…
For many important problems the quantity of interest is an unknown function of the parameters, which is a random vector with known statistics. Since the dependence of the output on this random vector is unknown, the challenge is to identify its statistics, using the minimum number of function evaluations. This problem …
Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of meas…
Mondrian random forests improve statistical inference for regression.
Paper improves parameter estimation of continuous distributions using preference feedback.
In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i.e., gradient magnitude with respect to logits, an angle that is not thoroughly studied so far. Consequently, we have two key findings: (1) Mean Absolute Error (MAE) D…
A new estimator for evaluating policies in unknown environments.
New method optimizes portfolio weights as functions, outperforming traditional approaches.
This paper introduces the first asymptotically optimal strategy for a multi armed bandit (MAB) model under side constraints. The side constraints model situations in which bandit activations are limited by the availability of certain resources that are replenished at a constant rate. The main result involves the deriva…
We consider the problem of identifying the parameters of an unknown mixture of two arbitrary -dimensional gaussians from a sequence of independent random samples. Our main results are upper and lower bounds giving a computationally efficient moment-based estimator with an optimal convergence rate, thus resolving a p…
In this paper, we study the problem of learning a mixture of Gaussians with streaming data: given a stream of points in dimensions generated by an unknown mixture of spherical Gaussians, the goal is to estimate the model parameters using a single pass over the data stream. We analyze a streaming version of …
Multiplicative noise models are often used instead of additive noise models in cases in which the noise variance depends on the state. Furthermore, when Poisson distributions with relatively small counts are approximated with normal distributions, multiplicative noise approximations are straightforward to implement. Th…
We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe ratio estimators when there are many assets in a portfolio. We allow for an increasing number of assets as well as time observations of the portfolio.…
Estimates missing data points in classifier inputs based on training data.
A new KF handles outliers without MSE loss.
New algorithm reduces regret for linear bandits with unknown noise variance.
Study shows exponential error reduction in multiclass classification without bias-variance trade-off.
The paper proposes a method for distribution-free prediction sets that adapt to unknown temporal changes.
Study proposes method to estimate causal effects from noisy treatment data.
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
Improved SGD with AdaGrad stepsizes adapts to unknown parameters and unbounded gradients.
This work uses ANOVA to understand how different factors contribute to test error in machine learning models.
Optimal estimator derived for partially observable LTI systems.
Paper proposes FPG algorithm for unbiased off-policy PG estimation.
The paper develops adaptive confidence intervals for Efron's Gaussian two-groups model with unknown contamination.
Stochastic gradient descent updates parameters with summation gradient computed from a random data batch. This summation will lead to unbalanced training process if the data we obtained is unbalanced. To address this issue, this paper takes the error variance and error mean both into consideration. The adaptively adjus…
This paper investigates methods for estimating the optimal stochastic control policy for a Markov Decision Process with unknown transition dynamics and an unknown reward function. This form of model-free reinforcement learning comprises many real world systems such as playing video games, simulated control tasks, and r…
Bayesian method recovers causal structure in SEMs with equal error variances.