Justin Emile Gottschlich

According to our database, Justin Emile Gottschlich authored at least 40 papers between 2008 and 2022.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2022

Machine Programming: Turning Data into Programmer Productivity.

Abdul Wasay, Nesime Tatbul, Justin Gottschlich

Proc. VLDB Endow., 2022

Synthesizing Programs with Continuous Optimization.

Shantanu Mandal, Todd A. Anderson, Javier Turek, Justin Gottschlich, Abdullah Muzahid

CoRR, 2022

MP-CodeCheck: Evolving Logical Expression Code Anomaly Learning with Iterative Self-Supervision.

Urs C. Muff, Celine Lee, Paul Gottschlich, Justin Gottschlich

CoRR, 2022

2021

Simulation Intelligence: Towards a New Generation of Scientific Methods.

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 H. Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer

CoRR, 2021

Toward Code Generation: A Survey and Lessons from Semantic Parsing.

Celine Lee, Justin Gottschlich, Dan Roth

CoRR, 2021

Predictive data locality optimization for higher-order tensor computations.

Tharindu Rusira Patabandi, Anand Venkat, Abhishek Kulkarni, Pushkar Ratnalikar, Mary W. Hall, Justin Gottschlich

Proceedings of the MAPS@PLDI 2021: Proceedings of the 5th ACM SIGPLAN International Symposium on Machine Programming, 2021

ControlFlag: a self-supervised idiosyncratic pattern detection system for software control structures.

Niranjan Hasabnis, Justin Gottschlich

Proceedings of the MAPS@PLDI 2021: Proceedings of the 5th ACM SIGPLAN International Symposium on Machine Programming, 2021

Learning Fitness Functions for Machine Programming.

Shantanu Mandal, Todd A. Anderson, Javier Turek, Justin Gottschlich, Shengtian Zhou, Abdullah Muzahid

Proceedings of Machine Learning and Systems 2021, 2021

AI programmer: autonomously creating software programs using genetic algorithms.

Kory Becker, Justin Gottschlich

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

2020

Class-Weighted Evaluation Metrics for Imbalanced Data Classification.

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

CoRR, 2020

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

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

CoRR, 2020

Software Language Comprehension using a Program-Derived Semantic Graph.

Roshni G. Iyer, Yizhou Sun, Wei Wang, Justin Gottschlich

CoRR, 2020

Context-Aware Parse Trees.

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

CoRR, 2020

Learned garbage collection.

Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska

Proceedings of the 4th ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, 2020

An Abstraction-Based Framework for Neural Network Verification.

Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz

Proceedings of the Computer Aided Verification - 32nd International Conference, 2020

2019

NetSyn: Neural Evolutionary Technique to Synthesize Programs.

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

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. 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

CoRR, 2019

A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions.

Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S. Turek, Tim Mattson, Abdullah Muzahid

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

The Three Pillars of Machine-Based Programming.

Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin C. Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson

CoRR, 2018

A New Model for Evaluating Range-Based Anomaly Detection Algorithms.

Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Justin Gottschlich

CoRR, 2018

Toward Scalable Verification for Safety-Critical Deep Networks.

Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark W. Barrett, Mykel J. Kochenderfer

CoRR, 2018

Precision and Recall for Range-Based Anomaly Detection.

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

CoRR, 2018

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

Tae-Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik

CoRR, 2018

Paranom: A Parallel Anomaly Dataset Generator.

Justin Gottschlich

CoRR, 2018

The three pillars of machine programming.

Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin C. Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson

Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, 2018

Precision and Recall for Time Series.

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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

AutoCon: Regression Testing for Detecting Cache Contention Anomalies Using Autoencoder.

Mohammad Mejbah Ul Alam, Justin Gottschlich, Md. Mostofa Ali Patwary, Saeef Ahmad, Abdullah Muzahid

CoRR, 2017

2015

TSXProf: Profiling Hardware Transactions.

Yujie Liu, Justin Gottschlich, Gilles Pokam, Michael F. Spear

Proceedings of the 2015 International Conference on Parallel Architectures and Compilation, 2015

2014

Towards Transactional Memory for OpenMP.

Michael Wong, Eduard Ayguadé, Justin Gottschlich, Victor Luchangco, Bronis R. de Supinski, Barna L. Bihari

Proceedings of the Using and Improving OpenMP for Devices, Tasks, and More, 2014

Invyswell: a hybrid transactional memory for haswell's restricted transactional memory.

Irina Calciu, Justin Gottschlich, Tatiana Shpeisman, Gilles Pokam, Maurice Herlihy

Proceedings of the International Conference on Parallel Architectures and Compilation, 2014

2013

QuickRec: prototyping an intel architecture extension for record and replay of multithreaded programs.

Gilles Pokam, Klaus Danne, Cristiano Pereira, Rolf Kassa, Tim Kranich, Shiliang Hu, Justin Emile Gottschlich, Nima Honarmand, Nathan Dautenhahn, Samuel T. King, Josep Torrellas

Proceedings of the 40th Annual International Symposium on Computer Architecture, 2013

But How Do We Really Debug Transactional Memory Programs?

Justin Emile Gottschlich, Rob C. Knauerhase, Gilles Pokam

Proceedings of the 5th USENIX Workshop on Hot Topics in Parallelism, 2013

Using Elimination and Delegation to Implement a Scalable NUMA-Friendly Stack.

Irina Calciu, Justin Emile Gottschlich, Maurice Herlihy

Proceedings of the 5th USENIX Workshop on Hot Topics in Parallelism, 2013

Concurrent predicates: A debugging technique for every parallel programmer.

Justin Emile Gottschlich, Gilles Pokam, Cristiano Pereira, Youfeng Wu

Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques, 2013

2012

Visualizing transactional memory.

Justin Emile Gottschlich, Maurice Herlihy, Gilles Pokam, Jeremy G. Siek

Proceedings of the International Conference on Parallel Architectures and Compilation Techniques, 2012

2011

CoreRacer: a practical memory race recorder for multicore x86 TSO processors.

Gilles Pokam, Cristiano Pereira, Shiliang Hu, Ali-Reza Adl-Tabatabai, Justin Emile Gottschlich, Jungwoo Ha, Youfeng Wu

Proceedings of the 44rd Annual IEEE/ACM International Symposium on Microarchitecture, 2011

Optimizing the Concurrent Execution of Locks and Transactions.

Justin Emile Gottschlich, JaeWoong Chung

Proceedings of the Languages and Compilers for Parallel Computing, 2011

2010

An efficient software transactional memory using commit-time invalidation.

Justin Emile Gottschlich, Manish Vachharajani, Jeremy G. Siek

Proceedings of the CGO 2010, 2010

2009

An efficient lock-aware transactional memory implementation.

Justin Emile Gottschlich, Jeremy G. Siek, Manish Vachharajani, Dwight Y. Winkler, Daniel A. Connors

Proceedings of the 4th workshop on the Implementation, 2009

2008

Optimizing consistency checking for memory-intensive transactions.

Justin Emile Gottschlich, Daniel A. Connors

Proceedings of the Twenty-Seventh Annual ACM Symposium on Principles of Distributed Computing, 2008