6 citations · 12 across the 14 of their papers we have counts for
14 papers
EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection
Lei Jiang, Ye Wei, Xinyu Xi +7
Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsupervised algorithm performs c…
When to Review: Spaced Repetition for Continual Pre-Training of Language Models
Alankar Atreya, Devesh Batra, Yoages Kumar Mantri +3
Continual pre-training of large language models must acquire new information without erasing old knowledge. Existing replay methods often choose a global old/new mixture and sample…
Large-Scale ChatBot Validation Through Customer Digital Twin Simulations
Cristovao Iglesias, Devesh Batra, Alankar Atreya +7
LLM-based chatbots are transforming customer service in regulated domains such as banking, but scalable and cost-effective validation remains a critical barrier to safe deployment.…
MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition
Yifan Bao, Xinyu Xi, Xinyu Liu +8
Automated data science is a structured model-selection problem. A solution must choose data transformations, feature representations, architecture, training procedure, evaluation p…
Helping Customers in Distress: An LLM-powered Agent that Converses, Probes, and Routes
Alankar Atreya, Stefan Sylvius Wagner, Stefan Sylvius Wanger +8
Banks receive millions of reports of fraud, scams, and disputed transactions every year, making it challenging to accurately direct customers to the appropriate specialist teams fo…
Evaluating Performance Drift from Model Switching in Multi-Turn LLM Systems
Raad Khraishi, Iman Zafar, Katie Myles +1
Deployed multi-turn LLM systems routinely switch models mid-interaction due to upgrades, cross-provider routing, and fallbacks. Such handoffs create a context mismatch: the model g…
Evaluating the Sensitivity of LLMs to Prior Context
Robert Hankache, Kingsley Nketia Acheampong, Liang Song +3
As large language models (LLMs) are increasingly deployed in multi-turn dialogue and other sustained interactive scenarios, it is essential to understand how extended context affec…
How Personality Traits Shape LLM Risk-Taking Behaviour
John Hartley, Conor Hamill, Devesh Batra +3
Large Language Models (LLMs) are increasingly deployed as autonomous agents, necessitating a deeper understanding of their decision-making behaviour under risk. This study investig…
Agent-based Modelling of Credit Card Promotions
Conor B. Hamill, Raad Khraishi, Simona Gherghel +4
Interest-free promotions are a prevalent strategy employed by credit card lenders to attract new customers, yet the research exploring their effects on both consumers and lenders r…
Conformal Predictions for Longitudinal Data
Devesh Batra, Salvatore Mercuri, Raad Khraishi
We introduce Longitudinal Predictive Conformal Inference (LPCI), a novel distribution-free conformal prediction algorithm for longitudinal data. Current conformal prediction approa…
Simple Noisy Environment Augmentation for Reinforcement Learning
Raad Khraishi, Ramin Okhrati
Data augmentation is a widely used technique for improving model performance in machine learning, particularly in computer vision and natural language processing. Recently, there h…
Modelling customer lifetime-value in the retail banking industry
Greig Cowan, Salvatore Mercuri, Raad Khraishi
Understanding customer lifetime value is key to nurturing long-term customer relationships, however, estimating it is far from straightforward. In the retail banking industry, comm…
An Introduction to Machine Unlearning
Salvatore Mercuri, Raad Khraishi, Ramin Okhrati +4
Removing the influence of a specified subset of training data from a machine learning model may be required to address issues such as privacy, fairness, and data quality. Retrainin…
Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit
Raad Khraishi, Ramin Okhrati
We introduce a method for pricing consumer credit using recent advances in offline deep reinforcement learning. This approach relies on a static dataset and requires no assumptions…