7 citations · 25 across the 8 of their papers we have counts for
8 papers
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
Izunna Okpala, Ashkan Golgoon, Arjun Ravi Kannan
The advent of large language models has ushered in a new era of agentic systems, where artificial intelligence programs exhibit remarkable autonomous decision-making capabilities a…
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings
Ashkan Golgoon, Ryan Franks, Khashayar Filom +1
In many industrial applications, it is common that the graph embeddings generated from training GNNs are used in an ensemble model where the embeddings are combined with other tabu…
Mechanistic interpretability of large language models with applications to the financial services industry
Ashkan Golgoon, Khashayar Filom, Arjun Ravi Kannan
Large Language Models such as GPTs (Generative Pre-trained Transformers) exhibit remarkable capabilities across a broad spectrum of applications. Nevertheless, due to their intrins…
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning
Shay Deutsch, Lionel Yelibi, Alex Tong Lin +1
Deriving meaningful representations from complex, high-dimensional data in unsupervised settings is crucial across diverse machine learning applications. This paper introduces a fr…
Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features
Konstandinos Kotsiopoulos, Alexey Miroshnikov, Khashayar Filom +1
In recent years, many Machine Learning (ML) explanation techniques have been designed using ideas from cooperative game theory. These game-theoretic explainers suffer from high com…
On marginal feature attributions of tree-based models
Khashayar Filom, Alexey Miroshnikov, Konstandinos Kotsiopoulos +1
Due to their power and ease of use, tree-based machine learning models, such as random forests and gradient-boosted tree ensembles, have become very popular. To interpret them, loc…
Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics
Alexey Miroshnikov, Konstandinos Kotsiopoulos, Ryan Franks +1
This article is a companion paper to our earlier work Miroshnikov et al. (2021) on fairness interpretability, which introduces bias explanations. In the current work, we propose a…
Stability theory of game-theoretic group feature explanations for machine learning models
Alexey Miroshnikov, Konstandinos Kotsiopoulos, Khashayar Filom +1
In this article, we study feature attributions of Machine Learning (ML) models originating from linear game values and coalitional values defined as operators on appropriate functi…