1 citations · 1 across the 10 of their papers we have counts for
10 papers
Is There an AI Bubble? Robust Date-Stamping for Periods of Exuberance
Abir Sarkar, Martin T. Wells
The recent surge in valuations among AI related firms has renewed concerns that markets may be entering a new phase of speculative exuberance, especially in the technology and semi…
Double Local-to-Unity: Inference under Nearly Nonstationary Volatility
Abir Sarkar, Martin T. Wells
This article develops a moderate-deviation limit theory for autoregressive models with jointly persistent mean and volatility dynamics. The autoregressive coefficient is allowed to…
Constructing Bayes Minimax Estimators through Integral Transformations
Dominique Fourdrinier, William E. Strawderman, Martin T. Wells
The problem of Bayes minimax estimation for the mean of a multivariate normal distribution under quadratic loss has attracted significant attention recently. These estimators have…
Enhancing the Tensor Normal via Geometrically Parameterized Cholesky Factors
Quinn Simonis, Martin T. Wells
In this article, we explore Bayesian extensions of the tensor normal model through a geometric expansion of the multi-way covariance's Cholesky factor inspired by the Fréchet mean…
Foreclassing: A new machine learning perspective on human decision making with temporal data
Daniel Andrew Coulson, Martin T. Wells
Time series forecasts are widely used to inform decisions. Human decision-makers interpret these forecasts, incorporate prior experience and uncertainty about future outcomes, and…
Geodesic Variational Bayes for Multiway Covariances
Quinn Simonis, Martin T. Wells
This article explores the optimization of variational approximations for posterior covariances of Gaussian multiway arrays. To achieve this, we establish a natural differential geo…
Separable Geodesic Lagrangian Monte Carlo for Inference in 2-Way Covariance Models
Quinn Simonis, Martin T. Wells
Matrix normal models have an associated 4-tensor for their covariance representation. The covariance array associated with a matrix normal model is naturally represented as a Krone…
Generative Models, Humans, Predictive Models: Who Is Worse at High-Stakes Decision Making?
Keri Mallari, Julius Adebayo, Kori Inkpen +3
Despite strong advisory against it, large generative models (LMs) are already being used for decision making tasks that were previously done by predictive models or humans. We put…
Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets
Tejas Ramdas, Martin T. Wells
In this study, we leverage powerful non-linear machine learning methods to identify the characteristics of trades that contain valuable information. First, we demonstrate the effec…
A Scalable Empirical Bayes Approach to Variable Selection
Haim Y. Bar, James G. Booth, Martin T. Wells
We develop a model-based empirical Bayes approach to variable selection problems in which the number of predictors is very large, possibly much larger than the number of responses…