1 code implementation • 20 Dec 2022 • Patrick Emami, Aidan Perreault, Jeffrey Law, David Biagioni, Peter C. St. John
We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence.
no code implementations • ICLR 2022 • Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
Measuring the discrepancy between two probability distributions is a fundamental problem in machine learning and statistics.
no code implementations • 1 Jan 2021 • Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
Based on ideas from decision theory, we investigate a new class of discrepancies that are based on the optimal decision loss.
no code implementations • 29 Nov 2018 • Aditya Bhaskara, Aidao Chen, Aidan Perreault, Aravindan Vijayaraghavan
Smoothed analysis is a powerful paradigm in overcoming worst-case intractability in unsupervised learning and high-dimensional data analysis.