57 citations · 96 across the 12 of their papers we have counts for
12 papers
Memory Efficient Tabular Foundation Models
Shuting Luo, Monika Mikhail Kanaan, Cameron Gordon +2
Tabular Foundation Models, such as TabPFN, have received a large amount of recent attention due to their performance on in-context tabular machine learning tasks, which often excee…
Beyond Generalist LLMs: Specialist Agentic Systems for Structured Code Workflow Execution
Harris Borman, Herman Wandabwa, Fusun Yu +4
Large Language Models (LLMs) have accelerated the adoption of software development agents, now widely available as Integrated Development Environment (IDE) extensions and standalon…
EmbGen: Teaching with Reassembled Corpora
Arun K Lenin, Kai Rouse, Andrea Nicastro +1
Adapting small instruction-tuned models to specialized domains often relies on supervised fine-tuning (SFT) on curated instruction-response examples, which is expensive to collect…
Prompt Segmentation and Annotation Optimisation: Controlling LLM Behaviour via Optimised Segment-Level Annotations
Devika Prasad, Luke Gerschwitz, Tong Li +5
Prompt engineering is crucial for effective interaction with generative artificial intelligence systems, yet existing optimisation methods often operate over an unstructured and va…
BOND: License to Train with Black-Box Functions
Andrew Clark, Jack Moursounidis, Osmaan Rasouli +3
We introduce Bounded Numerical Differentiation (BOND), a perturbative method for estimating the gradients of black-box functions. BOND is distinguished by its formulation, which ad…
A Robust and Efficient Pipeline for Enterprise-Level Large-Scale Entity Resolution
Sandeepa Kannangara, Arman Abrahamyan, Daniel Elias +5
Entity resolution (ER) remains a significant challenge in data management, especially when dealing with large datasets. This paper introduces MERAI (Massive Entity Resolution using…
FeatureCuts: Feature Selection for Large Data by Optimizing the Cutoff
Andy Hu, Devika Prasad, Luiz Pizzato +5
In machine learning, the process of feature selection involves finding a reduced subset of features that captures most of the information required to train an accurate and efficien…
Learning Adapter Rank via Symmetry Breaking
Cooper Doyle, Andy Hu, Rebecca Chan +1
Low-rank adaptation is effective partly because downstream updates lie in a low-dimensional subspace, but the latent rank coordinates of LoRA are not identifiable: any invertible r…
Do LLM Personas Dream of Bull Markets? Comparing Human and AI Investment Strategies Through the Lens of the Five-Factor Model
Harris Borman, Anna Leontjeva, Luiz Pizzato +2
Large Language Models (LLMs) have demonstrated the ability to adopt a personality and behave in a human-like manner. There is a large body of research that investigates the behavio…
Detection of Abuse in Financial Transaction Descriptions Using Machine Learning
Anna Leontjeva, Genevieve Richards, Kaavya Sriskandaraja +2
Since introducing changes to the New Payments Platform (NPP) to include longer messages as payment descriptions, it has been identified that people are now using it for communicati…
Combining Static and Dynamic Features for Multivariate Sequence Classification
Anna Leontjeva, Ilya Kuzovkin
Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dict…
Temporal Stability in Predictive Process Monitoring
Irene Teinemaa, Marlon Dumas, Anna Leontjeva +1
Predictive process monitoring is concerned with the analysis of events produced during the execution of a business process in order to predict as early as possible the final outcom…