3 papers
cs.LG2024
Minimising changes to audit when updating decision trees
Anj Simmons, Scott Barnett, Anupam Chaudhuri +2
Interpretable models are important, but what happens when the model is updated on new training data? We propose an algorithm for updating a decision tree while minimising the numbe…
cs.LG2024
Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold
Anupam Chaudhuri, Anj Simmons, Mohamed Abdelrazek
This paper presents our experiments to quantify the manifolds learned by ML models (in our experiment, we use a GAN model) as they train. We compare the manifolds learned at each e…
cs.CL2023
Detecting out-of-distribution text using topological features of transformer-based language models
Andres Pollano, Anupam Chaudhuri, Anj Simmons
To safeguard machine learning systems that operate on textual data against out-of-distribution (OOD) inputs that could cause unpredictable behaviour, we explore the use of topologi…