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 improves evolutionary computation by optimizing selection rates.
Wireless communication systems operate in complex time-varying environments. Therefore, selecting the optimal configuration parameters in these systems is a challenging problem. For wireless links, \emph{rate selection} is used to select the optimal data transmission rate that maximizes the link throughput subject to a…
New method improves CATE model selection with optimal regret rates.
This paper uses PCA and FA for feature selection in credit rating.
Private variable selection method controls FDR with simulations showing reasonable power.
There has been significant recent work on the theory and application of randomized coordinate descent algorithms, beginning with the work of Nesterov [SIAM J. Optim., 22(2), 2012], who showed that a random-coordinate selection rule achieves the same convergence rate as the Gauss-Southwell selection rule. This result su…
Forward regression is a statistical model selection and estimation procedure which inductively selects covariates that add predictive power into a working statistical regression model. Once a model is selected, unknown regression parameters are estimated by least squares. This paper analyzes forward regression in high-…
Develops methods to select informative conformal prediction sets with FCR control.
The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.
Optimal number of voters for a voting ensemble can be estimated from the distribution of classifier errors.
New algorithm reduces adaptation lag in online model selection.
OnlineSCI extends ACI for adaptive selective inference with improved coverage and IER control.
Sparse PCA selects variables with FDR control for improved performance.
As known, attribute selection is a method that is used before the classification of data mining. In this study, a new data set has been created by using attributes expressing overall satisfaction in Turkey Statistical Institute (TSI) Life Satisfaction Survey dataset. Attributes are sorted by Ranking search method using…
Selective inference framework for CART trees to control error rates and coverage.
SFS-DA method statistically tests FS reliability under domain adaptation.
In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new ite…
AutoSGD automatically adjusts learning rates for SGD.
Master algorithm selects best contextual bandit from a collection.
Paper tackles ESG rating disagreement in sustainable investing portfolios.
New method controls false edge detections in Gaussian graphical models.
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In part…
ACS is an interactive framework for model-free selection with guaranteed error control.
Proposes selective inference for testing differences in means between clusters.
Paper tackles action selection in deep RL, proposing a data-driven approach.
New study reveals surprising adaptive rates in model selection for transfer learning.
We consider a heterogeneous agent-based economic model where economic agents have strictly bounded rationality and where income allocation strategies evolve through selective imitation. Income is calculated by a Cobb-Douglas type production function, and selection of strategies for imitation depends on the income growt…
AutoGD automatically adjusts learning rates for gradient descent.
DSDE improves OoD detection by estimating model library proportions.
In this paper, we propose new listwise learning-to-rank models that mitigate the shortcomings of existing ones. Existing listwise learning-to-rank models are generally derived from the classical Plackett-Luce model, which has three major limitations. (1) Its permutation probabilities overlook ties, i.e., a situation wh…
Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. We propose a backw…
Minimizes indecisions in selective classification to control misclassification rates.
Online method selects candidates from data streams, ensuring irreversible decisions.
We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-adjusted …
Practical or scientific considerations often lead to selecting a subset of parameters as ``important.'' Inferences about those parameters often are based on the same data used to select them in the first place. That can make the reported uncertainties deceptively optimistic: confidence intervals that ignore selection g…
The paper proposes a method to test features selected by SeqFS-DA with controlled FPR.
Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of the available items, so that most of the data of potential interest is actually missing. Current ap…
In markets for online advertising, some advertisers pay only when users respond to ads. So publishers estimate ad response rates and multiply by advertiser bids to estimate expected revenue for showing ads. Since these estimates may be inaccurate, the publisher risks not selecting the ad for each ad call that would max…
Optimal Bayesian feature selection (OBFS) is a multivariate supervised screening method designed from the ground up for biomarker discovery. In this work, we prove that Gaussian OBFS is strongly consistent under mild conditions, and provide rates of convergence for key posteriors in the framework. These results are of …
Unified framework for SGMoE resolves estimation and selection issues.
We develop methods to estimate lag and parameters for multiple stable autoregressive processes.
We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by the agent) and provides a rating of the selected item. In this paper, we propose a novel algorithm …
New method selects best HTE estimator without ground-truth treatment effects.
Purpose: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge…
To estimate a sparse linear model from data with Gaussian noise, consilience from lasso and compressed sensing literatures is that thresholding estimators like lasso and the Dantzig selector have the ability in some situations to identify with high probability part of the significant covariates asymptotically, and are …
Proposes a method to select features for deep learning in noisy, high-dimensional data.
PH-CS selects test inputs with reliability guarantees, adapting FDR to data.
A new knockoff statistic using conditional prediction function improves variable selection in complex models.