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Mansi Sakarvadia

2 accepted papers

2026

The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators

ICLR 2026poster

A core challenge in scientific machine learning, and scientific computing more generally, is modeling continuous phenomena which (in practice) are represented discretely. Machine-learned operators (MLO) have been introduced as a means to achieve this modeling goal, as this class of architecture can…

Cited by 0SourcecodeScholar
2025

Mitigating Memorization in Language Models

ICLR 2025spotlight

Language models (LMs) can “memorize” information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training data can be problematic, for example, when data are private or sensitive. In this…