5 papers
A Unified Framework for Continual Learning and Unlearning
Romit Chatterjee, Vikram Chundawat, Ayush Tarun +2
Continual learning and machine unlearning are crucial challenges in machine learning, typically addressed separately. Continual learning focuses on adapting to new knowledge while…
Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models
Aakash Sen Sharma, Niladri Sarkar, Vikram Chundawat +2
Recent research has seen significant interest in methods for concept removal and targeted forgetting in text-to-image diffusion models. In this paper, we conduct a comprehensive wh…
ConDa: Fast Federated Unlearning with Contribution Dampening
Vikram S Chundawat, Pushkar Niroula, Prasanna Dhungana +3
Federated learning (FL) has enabled collaborative model training across decentralized data sources or clients. While adding new participants to a shared model does not pose great t…
EcoVal: An Efficient Data Valuation Framework for Machine Learning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal +3
Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley va…
TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data
Vikram S Chundawat, Ayush K Tarun, Murari Mandal +2
Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality…