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.
Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the selection bias, i.e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases. In …
One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. We propose automated mode…
Adaptive cascade submodular maximization tackles sequential selection under uncertainty.
Thompson Sampling tackles USS, a sequential selection problem without feedback.
Comprehensive review of robust portfolio selection models.
A novel feature selection method for SVM improves model accuracy and interpretability.
Efficient algorithm solves best subset selection problem.
We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence. A method for detecting those CPs can be formulated as a two-stage method: one for selecting relevant dimensions, and another for selecting CPs. It has been difficult to properly contro…
It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, the…
Improves algorithm selection for thousands of candidates using dyadic features.
Solves selecting the best optimizing system problems.
Optimizes subset selection in sparse learning problems.
Feature selection is an important task in many problems occurring in pattern recognition, bioinformatics, machine learning and data mining applications. The feature selection approach enables us to reduce the computation burden and the falling accuracy effect of dealing with huge number of features in typical learning …
Quantum computing improves feature selection in machine learning.
New heuristics improve genetic programming's parent selection for classification problems.
Study on selecting between base algorithms in stochastic bandit problems.
Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture…
PS framework selects best policy from library for CSO problems.
Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group…
Study time-inconsistent control problems with model uncertainty, solving portfolio selection.
A new framework selects information sources to test hypotheses robustly, even with misclassifications.
New approach uses secants to improve sensor placement and feature selection for nonlinear systems.
There are many problems in machine learning and data mining which are equivalent to selecting a non-redundant, high "quality" set of objects. Recommender systems, feature selection, and data summarization are among many applications of this. In this paper, we consider this problem as an optimization problem that seeks …
Mutual Information (MI) is often used for feature selection when developing classifier models. Estimating the MI for a subset of features is often intractable. We demonstrate, that under the assumptions of conditional independence, MI between a subset of features can be expressed as the Conditional Mutual Information (…
This paper defines a generalized column subset selection problem which is concerned with the selection of a few columns from a source matrix A that best approximate the span of a target matrix B. The paper then proposes a fast greedy algorithm for solving this problem and draws connections to different problems that ca…
Paper proposes a novel unsupervised feature selection method using K-means and ADMM.
New methods optimize experiment selection for sequential data, improving model accuracy.
Study solves optimal portfolio selection using HJB equation.
Unified framework for clustering and portfolio optimization.
New suboptimal algorithm for best subset selection in high-dimensional data.
Optimal subset selection for hypothesis testing with penalties.
Feature selection methods are widely used in order to solve the 'curse of dimensionality' problem. Many proposed feature selection frameworks, treat all data points equally; neglecting their different representation power and importance. In this paper, we propose an unsupervised hypergraph feature selection method via …
Feature Selection (FS) plays an important role in learning and classification tasks. The object of FS is to select the relevant and non-redundant features. Considering the huge amount number of features in real-world applications, FS methods using batch learning technique can't resolve big data problem especially when …
One of the classical problems in machine learning and data mining is feature selection. A feature selection algorithm is expected to be quick, and at the same time it should show high performance. MeLiF algorithm effectively solves this problem using ensembles of ranking filters. This article describes two different wa…
GOLFS selects features for clustering by combining global and local information.
Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source policies during the learning process is of great importance yet challenging. There has been little theoretical analysis of this problem. In…
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, rangin…
A new method for dynamic feature selection outperforms existing approaches.
A new metric for stable model selection in CATE prediction.
A new method selects the best ensemble for concept drift detection.
New algorithms for model selection in linear bandits adapt to instance complexity.
We frame the problem of selecting an optimal audio encoding scheme as a supervised learning task. Through uniform convergence theory, we guarantee approximately optimal codec selection while controlling for selection bias. We present rigorous statistical guarantees for the codec selection problem that hold for arbitrar…
Active learning optimizes algorithm selection for hard problems.
New algorithms for model selection in linear contextual bandits without feature diversity conditions.
Algorithm learns optimal arm selection in unsupervised sequential selection with contextual information.
The Column Subset Selection Problem provides a natural framework for unsupervised feature selection. Despite being a hard combinatorial optimization problem, there exist efficient algorithms that provide good approximations. The drawback of the problem formulation is that it incorporates no form of regularization, and …
abess efficiently solves various machine learning problems quickly.