Search Results for author:
Found 5 papers, 3 papers with code
Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models
\texttt{Mixture-Models} is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants, such as Parsimonious GMMs, Mixture of Factor Analyzers, MClust models, Mixture of Student's t distributions, etc.
How Robust are LLMs to In-Context Majority Label Bias?
In the In-Context Learning (ICL) setup, various forms of label biases can manifest.
Tackling Concept Shift in Text Classification using Entailment-style Modeling
Pre-trained language models (PLMs) have seen tremendous success in text classification (TC) problems in the context of Natural Language Processing (NLP).
Multi-way Spectral Clustering of Augmented Multi-view Data through Deep Collective Matrix Tri-factorization
We present the first deep learning based architecture for collective matrix tri-factorization (DCMTF) of arbitrary collections of matrices, also known as augmented multi-view data.
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data
Our simulation studies show that EM has better clustering performance, measured by Adjusted Rand Index, compared to GD in cases of misspecification, whereas on high dimensional data GD outperforms EM.
