Justin Gottschlich

Publication Activity (10 Years)

Years Active: 2014-2022
Publications (10 Years): 32

Top Topics

Precision And Recall

Continuous Optimization

Garbage Collection

Anomaly Detection

Top Venues

Publications

Urs C. Muff, Celine Lee, Paul Gottschlich, Justin Gottschlich
MP-CodeCheck: Evolving Logical Expression Code Anomaly Learning with Iterative Self-Supervision. CoRR (2022)
Abdul Wasay, Nesime Tatbul, Justin Gottschlich
Machine Programming: Turning Data into Programmer Productivity. Proc. VLDB Endow. 15 (12) (2022)
Shantanu Mandal, Todd A. Anderson, Javier Turek, Justin Gottschlich, Abdullah Muzahid
Synthesizing Programs with Continuous Optimization. CoRR (2022)
Celine Lee, Justin Gottschlich, Dan Roth
Toward Code Generation: A Survey and Lessons from Semantic Parsing. CoRR (2021)
Alexander Lavin, Hector Zenil, Brooks Paige, David Krakauer, Justin Gottschlich, Tim Mattson, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atilim Günes Baydin, Carina Prunkl, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR (2021)
Shantanu Mandal, Todd A. Anderson, Javier Turek, Justin Gottschlich, Shengtian Zhou, Abdullah Muzahid
Learning Fitness Functions for Machine Programming. MLSys (2021)
Tharindu Rusira Patabandi, Anand Venkat, Abhishek Kulkarni, Pushkar Ratnalikar, Mary W. Hall, Justin Gottschlich
Predictive data locality optimization for higher-order tensor computations. MAPS@PLDI (2021)
Niranjan Hasabnis, Justin Gottschlich
ControlFlag: a self-supervised idiosyncratic pattern detection system for software control structures. MAPS@PLDI (2021)
Kory Becker, Justin Gottschlich
AI programmer: autonomously creating software programs using genetic algorithms. GECCO Companion (2021)
Roshni G. Iyer, Yizhou Sun, Wei Wang, Justin Gottschlich
Software Language Comprehension using a Program-Derived Semantic Graph. CoRR (2020)
Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz
An Abstraction-Based Framework for Neural Network Verification. CAV (1) (2020)
Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Paul Petersen, Timothy G. Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
MISIM: An End-to-End Neural Code Similarity System. CoRR (2020)
Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Paul Petersen, Jesmin Jahan Tithi, Tim Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
Context-Aware Parse Trees. CoRR (2020)
Akhilesh Gupta, Nesime Tatbul, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich
Class-Weighted Evaluation Metrics for Imbalanced Data Classification. CoRR (2020)
Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska
Learned Garbage Collection. CoRR (2020)
Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska
Learned garbage collection. MAPL@PLDI (2020)
Niranjan Hasabnis, Justin Gottschlich
ControlFlag: A Self-supervised Idiosyncratic Pattern Detection System for Software Control Structures. CoRR (2020)
Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz
An Abstraction-Based Framework for Neural Network Verification. CoRR (2019)
Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S. Turek, Tim Mattson, Abdullah Muzahid
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. NeurIPS (2019)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. 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 M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
SysML: The New Frontier of Machine Learning Systems. CoRR (2019)
Shantanu Mandal, Todd A. Anderson, Mejbah Alam, Justin Gottschlich, Abdullah Muzahid
NetSyn: Neural Evolutionary Technique to Synthesize Programs. CoRR (2019)
Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich
Precision and Recall for Time Series. NeurIPS (2018)
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
The three pillars of machine programming. MAPL@PLDI (2018)
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
The Three Pillars of Machine-Based Programming. CoRR (2018)
Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark W. Barrett, Mykel J. Kochenderfer
Toward Scalable Verification for Safety-Critical Deep Networks. CoRR (2018)
Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Justin Gottschlich
A New Model for Evaluating Range-Based Anomaly Detection Algorithms. CoRR (2018)
Tae-Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection. CoRR (2018)
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik
Precision and Recall for Range-Based Anomaly Detection. CoRR (2018)
Justin Gottschlich
Paranom: A Parallel Anomaly Dataset Generator. CoRR (2018)
Mohammad Mejbah Ul Alam, Justin Gottschlich, Md. Mostofa Ali Patwary, Saeef Ahmad, Abdullah Muzahid
AutoCon: Regression Testing for Detecting Cache Contention Anomalies Using Autoencoder. CoRR (2017)
Kory Becker, Justin Gottschlich
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms. CoRR (2017)
Yujie Liu, Justin Gottschlich, Gilles Pokam, Michael F. Spear
TSXProf: Profiling Hardware Transactions. PACT (2015)
Michael Wong, Eduard Ayguadé, Justin Gottschlich, Victor Luchangco, Bronis R. de Supinski, Barna L. Bihari
Towards Transactional Memory for OpenMP. IWOMP (2014)
Irina Calciu, Justin Gottschlich, Tatiana Shpeisman, Gilles Pokam, Maurice Herlihy
Invyswell: a hybrid transactional memory for haswell's restricted transactional memory. PACT (2014)