2 code implementations • 11 Jul 2018 • Rebecca L. Russell, Louis Kim, Lei H. Hamilton, Tomo Lazovich, Jacob A. Harer, Onur Ozdemir, Paul M. Ellingwood, Marc W. McConley
The labeled dataset is available at: https://osf. io/d45bw/.
no code implementations • NeurIPS 2018 • Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin
Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections.
no code implementations • 14 Feb 2018 • Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell, Onur Ozdemir, Leonard R. Kosta, Akshay Rangamani, Lei H. Hamilton, Gabriel I. Centeno, Jonathan R. Key, Paul M. Ellingwood, Erik Antelman, Alan Mackay, Marc W. McConley, Jeffrey M. Opper, Peter Chin, Tomo Lazovich
We then compare methods applied directly to source code with methods applied to artifacts extracted from the build process, finding that source-based models perform better.
no code implementations • 3 Feb 2022 • Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszar, Rumman Chowdhury
We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.
no code implementations • 12 Sep 2022 • Amanda Bower, Kristian Lum, Tomo Lazovich, Kyra Yee, Luca Belli
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user.
no code implementations • 27 Feb 2023 • Vanessa Cai, Pradeep Prabakar, Manuel Serrano Rebuelta, Lucas Rosen, Federico Monti, Katarzyna Janocha, Tomo Lazovich, Jeetu Raj, Yedendra Shrinivasan, Hao Li, Thomas Markovich
We focus on the candidate generation phase of a large-scale ads recommendation problem in this paper, and present a machine learning first heterogeneous re-architecture of this stage which we term TwERC.
no code implementations • 31 Oct 2023 • Tomo Lazovich
In this work, we explore how prompting a leading large language model, ChatGPT-3. 5, with a user's political affiliation prior to asking factual questions about public figures and organizations leads to differing results.