Akshit Sinha

My current research focuses on understanding, evaluating and improving capabilities of AI systems (mainly large language models). My eventual goal is to help build AI that can reliably and autonomously perform complex tasks for extremely long periods of time, without human intervention.
During my undergraduate studies, I worked on problems in interpretability and machine unlearning, with a focus on graph neural networks.
(* = equal contribution)

A Cognac Shot To Forget Bad Memories: Corrective Unlearning for GNNs

We introduce Cognac, a novel framework for corrective unlearning in Graph Neural Networks (GNNs). We show that existing state-of-the-art unlearning methods for GNNs are not effective in removing the influence of training data on model predictions. Cognac addresses this by leveraging a contrastive learning approach to learn a new representation of the graph that is less influenced by the data to be unlearned.

Topo Goes Political: TDA-Based Controversy Detection in Imbalanced Reddit Political Data

We present a novel approach to controversy detection in imbalanced Reddit political data using Topological Data Analysis (TDA). We release a new dataset focused on Indian political context and introduce topological features based on Persistent Homology that significantly improve performance on class-imbalanced controversy detection tasks.

Higher Order Structures for Graph Explanations

We introduce Forge, a novel framework for generating explanations for Graph Neural Networks (GNNs) that leverages higher-order structures in graphs. Forge is designed to provide more comprehensive and interpretable explanations by considering relationships between groups of nodes rather than just pairwise interactions.

Sanity Checks for Graph Unlearning

We introduce a set of sanity checks for evaluating the effectiveness of unlearning methods in Graph Neural Networks (GNNs).