arXiv research

Enhances index selection for databases with task-specific inductive biases.

Paper improves Lasso for S&P500 index tracking with post-selection inference.

The paper optimizes asset selection for index trackers and enhanced trackers with varying cardinality constraints.

New algorithm finds best subset in high-dimensional data models.

DFR models dynamic distributional data with weighted Fréchet means.

Estimates linear models from self-selected data, addressing econometric challenges.

We develop a simple stock selection model to explain why active equity managers tend to underperform a benchmark index. We motivate our model with the empirical observation that the best performing stocks in a broad market index often perform much better than the other stocks in the index. Randomly selecting a subset o…

The so-called Pareto-Levy or power-law distribution has been successfully used as a model to describe probabilities associated to extreme variations of worldwide stock markets indexes data and it has the form $Pr(X>x) ~ x**(-alpha) for gamma< x <infinity. The selection of the threshold parameter gamma$ from empirical d…

In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …

New EI strategies using OWA and SSD for excess return.

We report the results of fifteen sets of portfolio selection simulations using stocks in the ASX200 index for the period May 2000 to December 2013. We investigated five portfolio selection methods, randomly and from within industrial groups, and three based on neighbor-Net phylogenetic networks. We report that using ra…

Gini index needs auto-calibration for consistent decision-making.

We presented Bayesian portfolio selection strategy, via the $k$ factor asset pricing model. If the market is information efficient, the proposed strategy will mimic the market; otherwise, the strategy will outperform the market. The strategy depends on the selection of a portfolio via Bayesian multiple testing methodol…

For massive data sets, efficient computation commonly relies on distributed algorithms that store and process subsets of the data on different machines, minimizing communication costs. Our focus is on regression and classification problems involving many features. A variety of distributed algorithms have been proposed …

A stability metric compares feature selection algorithms in machine learning.

Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation

The successive projection algorithm (SPA) is a fast algorithm to tackle separable nonnegative matrix factorization (NMF). Given a nonnegative data matrix $X$, SPA identifies an index set $\mathcal{K}$ such that there exists a nonnegative matrix $H$ with $X \approx X(:,\mathcal{K})H$. SPA has been successfully used as a…

New method finds minimum in noisy data, useful for model selection.

The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.

ARGEN method improves variable selection and regularization in high-dimensional sparse models.

Convolutional neural networks (CNNs) achieve state-of-the-art performance in a wide variety of tasks in computer vision. However, interpreting CNNs still remains a challenge. This is mainly due to the large number of parameters in these networks. Here, we investigate the role of compression and particularly pruning fil…

The paper studies implicit regularization in over-parameterized models for high-dimensional data.

This paper selects features in deep neural networks with theoretical guarantees.

Paper compares feature selection methods using GCM and LOCO, showing GCM methods generally outperform LOCO.

This paper proposes an active sampling method for meta-learning using MDPs.

Proposes new random models for fuzzy clustering similarity measures.

NETpred uses graph models to predict multiple market indices.

Paper explores Rashomon set models for more trustworthy medical conclusions.

The paper introduces a new model selection criterion for various time series models.

In this paper we propose a computationally efficient algorithm for on-line variable selection in multivariate regression problems involving high dimensional data streams. The algorithm recursively extracts all the latent factors of a partial least squares solution and selects the most important variables for each facto…

The paper challenges the validity of cluster validity measures in unsupervised learning.

Proposes an online model for LLM cascading with adaptive API selection.

A new FL algorithm reduces communication overhead by selectively updating model parameters.

This paper improves volatility forecasting using dynamic subset selection in genetic programming.

Introduces BCVI, a Bayesian cluster validity index for better cluster selection.

This study compares Markowitz and Single-Index models for Malaysian stocks.

This paper uses SARIMA models to forecast Nifty 50 index.

A new neural network approach reduces tracking error in index replication.

Flexible deep learning framework controls FDR for feature selection.

Decision tree classifiers are a widely used tool in data stream mining. The use of confidence intervals to estimate the gain associated with each split leads to very effective methods, like the popular Hoeffding tree algorithm. From a statistical viewpoint, the analysis of decision tree classifiers in a streaming setti…

Paper proposes SCQ and P-TAMS for structured OOD testing in high-stakes ML.

The problem of recovering a structured signal $\mathbf{x} \in \mathbb{C}^p$ from a set of dimensionality-reduced linear measurements $\mathbf{b} = \mathbf {A}\mathbf {x}$ arises in a variety of applications, such as medical imaging, spectroscopy, Fourier optics, and computerized tomography. Due to computational and sto…

Proposes a new semi-parametric framework for batched bandits with covariates.

We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learning paradigm popular within Cognitive Network related topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the rewards are exponentially distributed, which is common when dealing with Rayleigh fading c…

Improved FDR control for sparse financial index tracking.

We propose a novel class of time-varying nonparanormal graphical models, which allows us to model high dimensional heavy-tailed systems and the evolution of their latent network structures. Under this model, we develop statistical tests for presence of edges both locally at a fixed index value and globally over a range…

The 3D index of Dimofte-Gaiotto-Gukov a partially defined function on the set of ideal triangulations of 3-manifolds with $r$ torii boundary components. For a fixed $2r$ tuple of integers, the index takes values in the set of $q$-series with integer coefficients. Our goal is to give an axiomatic definition of the tetra…

Proposes a method to improve stock index prediction using cointegration and quantile loss.