activity
20212026
most citedCertifiable Machine Unlearning for Linear Models

11 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Matching Meaning at Scale: Evaluating Semantic Search for 18th-Century Intellectual History through the Case of Locke

Yu Wu, Ananth Mahadevan, Filip Ginter +2

While digitized corpora have transformed the study of intellectual transmission, current methods rely heavily on lexical text reuse detection, capturing verbatim quotations but fun…

cs.DB2024★ 2 cited

Optimizing a Data Science System for Text Reuse Analysis

Ananth Mahadevan, Michael Mathioudakis, Eetu Mäkelä +1

Text reuse is a methodological element of fundamental importance in humanities research: pieces of text that re-appear across different documents, verbatim or paraphrased, provide…

cs.LG2023★ 1 cited

Cost-Effective Retraining of Machine Learning Models

Ananth Mahadevan, Michael Mathioudakis

It is important to retrain a machine learning (ML) model in order to maintain its performance as the data changes over time. However, this can be costly as it usually requires proc…

cs.DL2023★ 6 cited

Reception Reader: Exploring Text Reuse in Early Modern British Publications

David Rosson, Eetu Mäkelä, Ville Vaara +3

The Reception Reader is a web tool for studying text reuse in the Early English Books Online (EEBO-TCP) and Eighteenth Century Collections Online (ECCO) data. Users can: 1) explore…

cs.LG2021★ 11 cited

Certifiable Machine Unlearning for Linear Models

Ananth Mahadevan, Michael Mathioudakis

Machine unlearning is the task of updating machine learning (ML) models after a subset of the training data they were trained on is deleted. Methods for the task are desired to com…