Justin Gottschlich

37 papers A* 2B 2Journal 22Unranked 9

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	Agentic AI for Enterprise: Emerging Applications and Real-world Challenges. KDD (2) Anbang Xu, Min Du, Tan Yu, Meghana Puvvadi, Tao Yu, Yufan Guo, Xinyun Chen, Justin Gottschlich
2022	J	`jnl`	MP-CodeCheck: Evolving Logical Expression Code Anomaly Learning with Iterative Self-Supervision. CoRR Urs C. Muff, Celine Lee, Paul Gottschlich, Justin Gottschlich
2022	J	`jnl`	Machine Programming: Turning Data into Programmer Productivity. Proc. VLDB Endow. Abdul Wasay, Nesime Tatbul, Justin Gottschlich
2022	J	`jnl`	Synthesizing Programs with Continuous Optimization. CoRR Shantanu Mandal, Todd A. Anderson, Javier Turek, Justin Gottschlich, Abdullah Muzahid
2021	—	`conf`	AI programmer: autonomously creating software programs using genetic algorithms. GECCO Companion Kory Becker, Justin Gottschlich
2021	—	`conf`	ControlFlag: a self-supervised idiosyncratic pattern detection system for software control structures. MAPS@PLDI Niranjan Hasabnis, Justin Gottschlich
2021	—	`conf`	Learning Fitness Functions for Machine Programming. MLSys Shantanu Mandal, Todd A. Anderson, Javier Turek, Justin Gottschlich, Shengtian Zhou, Abdullah Muzahid
2021	—	`conf`	Predictive data locality optimization for higher-order tensor computations. MAPS@PLDI Tharindu Rusira Patabandi, Anand Venkat, Abhishek Kulkarni, Pushkar Ratnalikar, Mary W. Hall, Justin Gottschlich
2021	J	`jnl`	Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR 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
2021	J	`jnl`	Toward Code Generation: A Survey and Lessons from Semantic Parsing. CoRR Celine Lee, Justin Gottschlich, Dan Roth
2020	—	`conf`	An Abstraction-Based Framework for Neural Network Verification. CAV (1) Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz
2020	J	`jnl`	Class-Weighted Evaluation Metrics for Imbalanced Data Classification. CoRR Akhilesh Gupta, Nesime Tatbul, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich
2020	J	`jnl`	Context-Aware Parse Trees. CoRR Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Paul Petersen, Jesmin Jahan Tithi, Tim Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
2020	J	`jnl`	ControlFlag: A Self-supervised Idiosyncratic Pattern Detection System for Software Control Structures. CoRR Niranjan Hasabnis, Justin Gottschlich
2020	J	`jnl`	Learned Garbage Collection. CoRR Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska
2020	—	`conf`	Learned garbage collection. MAPL@PLDI Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska
2020	J	`jnl`	MISIM: An End-to-End Neural Code Similarity System. CoRR 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
2020	J	`jnl`	Software Language Comprehension using a Program-Derived Semantic Graph. CoRR Roshni G. Iyer, Yizhou Sun, Wei Wang, Justin Gottschlich
2019	A*	`conf`	A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. NeurIPS Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S. Turek, Tim Mattson, Abdullah Muzahid
2019	J	`jnl`	An Abstraction-Based Framework for Neural Network Verification. CoRR Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz
2019	J	`jnl`	NetSyn: Neural Evolutionary Technique to Synthesize Programs. CoRR Shantanu Mandal, Todd A. Anderson, Mejbah Alam, Justin Gottschlich, Abdullah Muzahid
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR 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 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 Randall 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
2018	J	`jnl`	A New Model for Evaluating Range-Based Anomaly Detection Algorithms. CoRR Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Justin Gottschlich
2018	J	`jnl`	Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection. CoRR Tae-Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik
2018	J	`jnl`	Paranom: A Parallel Anomaly Dataset Generator. CoRR Justin Gottschlich
2018	J	`jnl`	Precision and Recall for Range-Based Anomaly Detection. CoRR Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik
2018	A*	`conf`	Precision and Recall for Time Series. NeurIPS Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich
2018	—	`ed.`	Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, MAPL@PLDI 2018, Philadelphia, PA, USA, June 18-22, 2018 MAPL@PLDI Justin Gottschlich, Alvin Cheung
2018	J	`jnl`	The Three Pillars of Machine-Based Programming. CoRR Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin C. Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
2018	—	`conf`	The three pillars of machine programming. MAPL@PLDI Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin C. Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
2018	J	`jnl`	Toward Scalable Verification for Safety-Critical Deep Networks. CoRR Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark W. Barrett, Mykel J. Kochenderfer
2017	J	`jnl`	AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms. CoRR Kory Becker, Justin Gottschlich
2017	J	`jnl`	AutoCon: Regression Testing for Detecting Cache Contention Anomalies Using Autoencoder. CoRR Mohammad Mejbah Ul Alam, Justin Gottschlich, Md. Mostofa Ali Patwary, Saeef Ahmad, Abdullah Muzahid
2017	—	`ed.`	Proceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, MAPL@PLDI 2017, Barcelona, Spain, June 18, 2017 MAPL@PLDI Tatiana Shpeisman, Justin Gottschlich
2015	B	`conf`	TSXProf: Profiling Hardware Transactions. PACT Yujie Liu, Justin Gottschlich, Gilles Pokam, Michael F. Spear
2014	B	`conf`	Invyswell: a hybrid transactional memory for haswell's restricted transactional memory. PACT Irina Calciu, Justin Gottschlich, Tatiana Shpeisman, Gilles Pokam, Maurice Herlihy
2014	—	`conf`	Towards Transactional Memory for OpenMP. IWOMP Michael Wong, Eduard Ayguadé, Justin Gottschlich, Victor Luchangco, Bronis R. de Supinski, Barna L. Bihari