Search Results for author: Martin T. Wells

Found 15 papers, 3 papers with code

K-ARMA Models for Clustering Time Series Data

no code implementations30 Jun 2022 Derek O. Hoare, David S. Matteson, Martin T. Wells

We then apply our method first with an AR($p$) clustering example and show how the clustering algorithm can be made robust to outliers using a least-absolute deviations criteria.

Outlier Detection Time Series +1

Interpretable Latent Variables in Deep State Space Models

no code implementations3 Mar 2022 Haoxuan Wu, David S. Matteson, Martin T. Wells

We introduce a new version of deep state-space models (DSSMs) that combines a recurrent neural network with a state-space framework to forecast time series data.

Time Series

Clustering Structure of Microstructure Measures

no code implementations5 Jul 2021 Liao Zhu, Ningning Sun, Martin T. Wells

This paper builds the clustering model of measures of market microstructure features which are popular in predicting stock returns.

A News-based Machine Learning Model for Adaptive Asset Pricing

no code implementations13 Jun 2021 Liao Zhu, Haoxuan Wu, Martin T. Wells

The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news.

Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model

no code implementations9 Nov 2020 Liao Zhu, Robert A. Jarrow, Martin T. Wells

We show that for nearly all time periods with length less than 6 years, the beta coefficients are time-invariant for the AMF model, but not for the FF5 model.

HALO: Learning to Prune Neural Networks with Shrinkage

1 code implementation24 Aug 2020 Skyler Seto, Martin T. Wells, Wenyu Zhang

Deep neural networks achieve state-of-the-art performance in a variety of tasks by extracting a rich set of features from unstructured data, however this performance is closely tied to model size.

Network Pruning

Robust Matrix Completion with Mixed Data Types

no code implementations25 May 2020 Daqian Sun, Martin T. Wells

Vast majority of the solutions have proposed computationally feasible estimators with strong statistical guarantees for the case where the underlying distribution of data in the matrix is continuous.

Matrix Completion

Fairness criteria through the lens of directed acyclic graphical models

no code implementations26 Jun 2019 Benjamin R. Baer, Daniel E. Gilbert, Martin T. Wells

A substantial portion of the literature on fairness in algorithms proposes, analyzes, and operationalizes simple formulaic criteria for assessing fairness.


High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model

1 code implementation23 Apr 2018 Liao Zhu, Sumanta Basu, Robert A. Jarrow, Martin T. Wells

The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small.

A Double Parametric Bootstrap Test for Topic Models

no code implementations19 Nov 2017 Skyler Seto, Sarah Tan, Giles Hooker, Martin T. Wells

Non-negative matrix factorization (NMF) is a technique for finding latent representations of data.

Topic Models

Penalized versus constrained generalized eigenvalue problems

no code implementations22 Oct 2014 Irina Gaynanova, James Booth, Martin T. Wells

We investigate the difference between using an $\ell_1$ penalty versus an $\ell_1$ constraint in generalized eigenvalue problems, such as principal component analysis and discriminant analysis.

Variable Selection

Simultaneous sparse estimation of canonical vectors in the p>>N setting

no code implementations24 Mar 2014 Irina Gaynanova, James G. Booth, Martin T. Wells

Secondly, we propose an extension of this form to the $p\gg N$ setting and achieve feature selection by using a group penalty.

Classification Consistency feature selection +1

Supervised Classification Using Sparse Fisher's LDA

no code implementations21 Jan 2013 Irina Gaynanova, James G. Booth, Martin T. Wells

We apply a lasso-type penalty to the discriminant vector to ensure sparsity of the solution and use a shrinkage type estimator for the covariance matrix.

Classification General Classification +1

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