4 citations · 6 across the 8 of their papers we have counts for
5 papers · 1 filter
ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
Andrew Y. Zhou, Sharvaree Vadgama, Sumanth Varambally +3
Advances in large language models (LLMs) have recently opened new and promising avenues for small-molecule drug discovery. Yet existing LLM-based approaches for molecular generatio…
Discovering Latent Causal Graphs from Spatiotemporal Data
Kun Wang, Sumanth Varambally, Duncan Watson-Parris +2
Many important phenomena in scientific fields like climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. Infer…
Discovering Mixtures of Structural Causal Models from Time Series Data
Sumanth Varambally, Yi-An Ma, Rose Yu
Discovering causal relationships from time series data is significant in fields such as finance, climate science, and neuroscience. However, contemporary techniques rely on the sim…
Domain Generalization via Inference-time Label-Preserving Target Projections
Prashant Pandey, Mrigank Raman, Sumanth Varambally +1
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem. While many approaches hav…
FROCC: Fast Random projection-based One-Class Classification
Arindam Bhattacharya, Sumanth Varambally, Amitabha Bagchi +1
We present Fast Random projection-based One-Class Classification (FROCC), an extremely efficient method for one-class classification. Our method is based on a simple idea of transf…