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Class-Weighted Evaluation Metrics for Imbalanced Data Classification

Oct 12, 2020
Akhilesh Gupta, Nesime Tatbul, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich

* 11 pages 

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MISIM: An End-to-End Neural Code Similarity System

Jun 15, 2020
Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Paul Petersen, Timothy Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich

* arXiv admin note: text overlap with arXiv:2003.11118 

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Software Language Comprehension using a Program-Derived Semantic Graph

Apr 03, 2020
Roshni G. Iyer, Yizhou Sun, Wei Wang, Justin Gottschlich


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Context-Aware Parse Trees

Mar 24, 2020
Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Paul Petersen, Jesmin Jahan Tithi, Tim Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich


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An Abstraction-Based Framework for Neural Network Verification

Oct 31, 2019
Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz


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Learning Fitness Functions for Genetic Algorithms

Sep 10, 2019
Shantanu Mandal, Todd A. Anderson, Justin Gottschlich, Shengtian Zhou, Abdullah Muzahid


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NetSyn: Neural Evolutionary Technique to Synthesize Programs

Aug 22, 2019
Shantanu Mandal, Todd A. Anderson, Mejbah Alam, Justin Gottschlich, Abdullah Muzahid


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SysML: The New Frontier of Machine Learning Systems

May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar


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Precision and Recall for Time Series

Oct 28, 2018
Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich

* 11 pages, 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montreal, Canada 

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The Three Pillars of Machine Programming

May 08, 2018
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman Amarasinghe, Joshua B Tenenbaum, Tim Mattson


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Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection

Feb 11, 2018
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik


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Precision and Recall for Range-Based Anomaly Detection

Feb 11, 2018
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik


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Toward Scalable Verification for Safety-Critical Deep Networks

Feb 02, 2018
Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark Barrett, Mykel Kochenderfer

* Accepted for presentation at SysML 2018 

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Paranom: A Parallel Anomaly Dataset Generator

Jan 09, 2018
Justin Gottschlich


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AutoPerf: A Generalized Zero-Positive Learning System to Detect Software Performance Anomalies

Nov 19, 2017
Mohammad Mejbah ul Alam, Justin Gottschlich, Abdullah Muzahid


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AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms

Sep 17, 2017
Kory Becker, Justin Gottschlich


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