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.
Alpha-based performance evaluation may fail to capture correlated residuals due to model errors. This paper proposes using the Generalized Information Ratio (GIR) to measure performance under misspecified benchmarks. Motivated by the theoretical link between abnormal returns and residual covariance matrix, GIR is deriv…
The skip-connections used in residual networks have become a standard architecture choice in deep learning due to the increased training stability and generalization performance with this architecture, although there has been limited theoretical understanding for this improvement. In this work, we analyze overparameter…
In this paper we consider portmanteau tests for testing the adequacy of multiplicative seasonal autoregressive moving-average (SARMA) models under the assumption that the errors are uncorrelated but not necessarily independent.We relax the standard independence assumption on the error term in order to extend the range …
New diagnostic method detects misspecified models in inverse PDE problems.
CRC improves multivariate forecasting accuracy without risking performance degradation.
New measure assesses deep neural networks' robustness to adversarial attacks.
Optimal a priori estimates are derived for the population risk, also known as the generalization error, of a regularized residual network model. An important part of the regularized model is the usage of a new path norm, called the weighted path norm, as the regularization term. The weighted path norm treats the skip c…
Proposes a robust method for high-dimensional linear models.
Robust forecast framework reduces distribution error by 63%.
This paper characterizes the conditional distribution properties of the finite sample ridge regression estimator and uses that result to evaluate total regression and generalization errors that incorporate the inaccuracies committed at the time of parameter estimation. The paper provides explicit formulas for those err…
Paper studies M-estimators with derivatives and residual distribution for robust adaptive tuning.
Neural operators correct PDE residuals to improve BIP solutions.
New method for distributional off-policy evaluation using Bellman residual minimization.
Wide residual networks generalize well with uniform convergence to RNTK as width increases.
The study analyzes numerical stability in large language models using mixed-precision arithmetic.
D2SRM solves complex PDEs using deep learning.
SA-PEF improves federated learning efficiency by correcting gradient mismatches.
AAS optimizes neural network PDE approximations by adaptively sampling.
Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument, and random measurement errors. Several novel biological technologies, such as ma…
This work presents a technique for statistically modeling errors introduced by reduced-order models. The method employs Gaussian-process regression to construct a mapping from a small number of computationally inexpensive `error indicators' to a distribution over the true error. The variance of this distribution can be…
New bounds on ReLU networks for low-regular functions.
Proposes a neural network method to correct residual distortions in coordinate transformations.
Develops efficient inference for noise heterogeneity in machine learning models.
Inverse depth scaling found in LLMs due to similar layers averaging error.
Improved stochastic approximation method reduces residual error.
New method for Sharpe ratio analysis in high dimensions using residual-based nodewise regression.
DIET tests conditional independence using marginal dependence measures of residual information.
Deep learning enhances options hedging performance.
While training error of most deep neural networks degrades as the depth of the network increases, residual networks appear to be an exception. We show that the main reason for this is the Lyapunov stability of the gradient descent algorithm: for an arbitrarily chosen step size, the equilibria of the gradient descent ar…
Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. …
We show that Residual Networks (ResNet) is equivalent to boosting feature representation, without any modification to the underlying ResNet training algorithm. A regret bound based on Online Gradient Boosting theory is proved and suggests that ResNet could achieve Online Gradient Boosting regret bounds through neural n…
Causality-aware methods outperform linear residualization in confounding adjustment for anticausal prediction.
New robust estimator improves variable selection and coefficient estimation in linear regression with heavy-tailed errors and outliers.
Leasing is a popular channel to market new cars. Pricing a leasing contract is complicated because the leasing rate embodies an expectation of the residual value of the car after contract expiration. To aid lessors in their pricing decisions, the paper develops resale price forecasting models. A peculiarity of the leas…
We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polyso…
RKD improves model compression by distilling residual knowledge from a deep teacher model.
SCORE improves tree-based predictions with boosted residual extraTrees.
FetchSGD reduces communication in federated learning with sketching.
This paper employs the extrinsic information transfer (EXIT) method, a technique imported from the analysis of the iterative decoding of error control codes, to study the performance of belief propagation in community detection in the presence of side information. We consider both the detection of a single (hidden) com…
New method improves matrix completion accuracy, especially in noisy data.
A new method boosts exploration in bandit algorithms, reducing regret.
Efficiently refits black box predictions with wild refitting method.
A method to reduce bias in model-based policy evaluation by shifting operators.
To have a superior generalization, a deep learning neural network often involves a large size of training sample. With increase of hidden layers in order to increase learning ability, neural network has potential degradation in accuracy. Both could seriously limit applicability of deep learning in some domains particul…
Itô processes are the most common form of continuous semimartingales, and include diffusion processes. This paper is concerned with the nonparametric regression relationship between two such Itô processes. We are interested in the quadratic variation (integrated volatility) of the residual in this regression, over a un…
RSIC identifies multiple ranks of interest in NMF by analyzing residual sensitivity.
We propose a novel exponentially-modified Gaussian (EMG) mixture residual model. The EMG mixture is well suited to model residuals that are contaminated by a distribution with positive support. This is in contrast to commonly used robust residual models, like the Huber loss or , which assume a symmetric contami…
A method for estimating nonlinear regression errors and their distributions without performing regression is presented. Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. For N data points the computational demand is N2. Comparing the predic…