4 citations · 12 across the 17 of their papers we have counts for
24 papers
Finding Icebergs in Language-Model Workflow: Diagnosing Latent Structural Fragility with Stochastic Semantic Evidence Graphs
Matthew F. Dixon, Matthew Francis Dixon, Bertrand Nortier +1
AI-workflow governance cannot be reduced to checking the final answer: an apparently safe answer may rest on a fragile evidence path that ordinary evaluation cannot see, localize o…
Foundations of Stochastic Lexical Calculus: Semantic Descent and Random Dynamics on Probability Simplices
Matthew F Dixon
Large language models produce prompt-dependent probabilities over words, whereas scientific systems require uncertainty over meaningful states that can be updated as evidence arriv…
Identification and Honest Recovery from Semantic Observation Kernels: Operator Error, Coarsening, and Stability
Matthew Francis Dixon
Probabilistic text generators, such as large language models, assign probabilities to phrases, but consequential decisions require posterior uncertainty over meaningful states. The…
Successive Schur-Riesz Analysis for Approximation
Matthew Francis Dixon
Many approximation methods enlarge a trial space by adjoining function blocks generated by different operators. Exact redundancy and strong cross-level interaction can make coeffic…
Calibrating Semantic Uncertainty from Observable Language-Model Probabilities
Matthew F. Dixon
As generative artificial intelligence enters scientific and professional work, its uncertainty must be defined on the states that matter for inference and decision-making. Language…
Adaptive AI Delegation under Uncertainty: A Bayesian Governance Policy for Sequential Decision Authority
Matthew Francis Dixon
Organizations increasingly use large language models and agentic AI systems to generate probabilistic assessments and candidate actions in high-consequence settings. This creates a…
Model Validation of Agentic AI Systems: A POMDP-Based Framework for Belief-State, Forecast, and Policy Validation
Matthew Francis Dixon
Agentic artificial intelligence systems introduce a new class of model risk. Unlike traditional predictive models, autonomous agents continuously acquire information, form beliefs…
Belief at Risk: Quantifying Agentic AI Model Risk with LLM-Inferred Bayesian State Filters
Matthew Francis Dixon
Agentic AI systems create model risk because uncertain beliefs are coupled to autonomous actions. This paper develops a mathematical framework for quantifying agentic AI risk by re…
Beyond Surrogate Modeling: Learning the Local Volatility Via Shape Constraints
Marc Chataigner, Areski Cousin, Stéphane Crépey +2
We explore the abilities of two machine learning approaches for no-arbitrage interpolation of European vanilla option prices, which jointly yield the corresponding local volatility…
Deep Partial Least Squares for Empirical Asset Pricing
Matthew F. Dixon, Nicholas G. Polson, Kemen Goicoechea
We use deep partial least squares (DPLS) to estimate an asset pricing model for individual stock returns that exploits conditioning information in a flexible and dynamic way while…
A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives
Dixon Domfeh, Arpita Chatterjee, Matthew Dixon
Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertain…
Deep Local Volatility
Marc Chataigner, Stéphane Crépey, Matthew Dixon
Deep learning for option pricing has emerged as a novel methodology for fast computations with applications in calibration and computation of Greeks. However, many of these approac…
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks
Matthew F Dixon
Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches. While many new general purpose machine lea…
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning
Matthew Dixon, Igor Halperin
We present a reinforcement learning approach to goal based wealth management problems such as optimization of retirement plans or target dated funds. In such problems, an investor…
Deep Fundamental Factor Models
Matthew F. Dixon, Nicholas G. Polson
Deep fundamental factor models are developed to automatically capture non-linearity and interaction effects in factor modeling. Uncertainty quantification provides interpretability…
Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations
Stéphane Crépey, Matthew Dixon
Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk…
"Quantum Equilibrium-Disequilibrium": Asset Price Dynamics, Symmetry Breaking, and Defaults as Dissipative Instantons
Igor Halperin, Matthew Dixon
We propose a simple non-equilibrium model of a financial market as an open system with a possible exchange of money with an outside world and market frictions (trade impacts) incor…
Bitcoin Risk Modeling with Blockchain Graphs
Cuneyt Akcora, Matthew Dixon, Yulia Gel +1
A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin…
Information-Corrected Estimation: A Generalization Error Reducing Parameter Estimation Method
Matthew Dixon, Tyler Ward
Modern computational models in supervised machine learning are often highly parameterized universal approximators. As such, the value of the parameters is unimportant, and only the…
OSTSC: Over Sampling for Time Series Classification in R
Matthew Dixon, Diego Klabjan, Lan Wei
The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the oversampling…
A High Frequency Trade Execution Model for Supervised Learning
Matthew F Dixon
This paper introduces a high frequency trade execution model to evaluate the economic impact of supervised machine learners. Extending the concept of a confusion matrix, we present…
Sequence Classification of the Limit Order Book using Recurrent Neural Networks
Matthew F Dixon
Recurrent neural networks (RNNs) are types of artificial neural networks (ANNs) that are well suited to forecasting and sequence classification. They have been applied extensively…
Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading
Matthew F. Dixon, Nicholas G. Polson, Vadim O. Sokolov
Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors. Our goal is to develop and train deep learning architectures for s…
Classification-based Financial Markets Prediction using Deep Neural Networks
Matthew Dixon, Diego Klabjan, Jin Hoon Bang
Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech t…