activity
20172022
collaborators

5 papers

cs.LG2022

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design

Ali Behrouz, Mathias Lecuyer, Cynthia Rudin +1

Sparse decision trees are one of the most common forms of interpretable models. While recent advances have produced algorithms that fully optimize sparse decision trees for predict…

cs.LG2021

Sayer: Using Implicit Feedback to Optimize System Policies

Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati +4

We observe that many system policies that make threshold decisions involving a resource (e.g., time, memory, cores) naturally reveal additional, or implicit feedback. For example,…

stat.ML2019

Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform

Mathias Lecuyer, Riley Spahn, Kiran Vodrahalli +2

Companies increasingly expose machine learning (ML) models trained over sensitive user data to untrusted domains, such as end-user devices and wide-access model stores. We present…

stat.ML2018

Certified Robustness to Adversarial Examples with Differential Privacy

Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu +2

Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed…

cs.CR2017

Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization

Mathias Lecuyer, Riley Spahn, Roxana Geambasu +2

Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. The ability to distinguish data that…