Arun Kumar

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 55

Top Topics

Model Selection

Machine Learning

Top Venues

SIGMOD Conference

Proc. VLDB Endow.

Publications

Vraj Shah, Thomas J. Parashos, Arun Kumar
How do Categorical Duplicates Affect ML? A New Benchmark and Empirical Analyses. Proc. VLDB Endow. 17 (6) (2024)
Kabir Nagrecha, Arun Kumar
Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads. Proc. VLDB Endow. 17 (4) (2023)
Yuhao Zhang, Arun Kumar
Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines. Proc. VLDB Endow. 16 (11) (2023)
Kabir Nagrecha, Arun Kumar
Saturn: An Optimized Data System for Large Model Deep Learning Workloads. CoRR (2023)
Kabir Nagrecha, Arun Kumar
Saturn: Efficient Multi-Large-Model Deep Learning. CoRR (2023)
Yutong Shao, Arun Kumar, Ndapa Nakashole
Database-Aware ASR Error Correction for Speech-to-SQL Parsing. ICASSP (2023)
Arun Kumar, Alin Deutsch, Amarnath Gupta, Yannis Papakonstantinou, Babak Salimi, Victor Vianu
Database Education at UC San Diego. SIGMOD Rec. 51 (3) (2022)
Yutong Shao, Arun Kumar, Ndapa Nakashole
Structured Data Representation in Natural Language Interfaces. IEEE Data Eng. Bull. 45 (3) (2022)
Supun Nakandala, Arun Kumar
Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets. SIGMOD Conference (2022)
Kabir Nagrecha, Arun Kumar
Hydra: A System for Large Multi-Model Deep Learning. CoRR (2021)
Liangde Li, Supun Chathuranga Nakandala, Arun Kumar
Intermittent Human-in-the-Loop Model Selection using Cerebro: A Demonstration. Proc. VLDB Endow. 14 (12) (2021)
Arun Kumar
Automation of Data Prep, ML, and Data Science: New Cure or Snake Oil? SIGMOD Conference (2021)
Yuhao Zhang, Frank Mcquillan, Nandish Jayaram, Nikhil Kak, Ekta Khanna, Orhan Kislal, Domino Valdano, Arun Kumar
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches. Proc. VLDB Endow. 14 (10) (2021)
Vraj Shah, Jonathan Lacanlale, Premanand Kumar, Kevin Yang, Arun Kumar
Towards Benchmarking Feature Type Inference for AutoML Platforms. SIGMOD Conference (2021)
Side Li, Arun Kumar
Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning. Proc. VLDB Endow. 14 (11) (2021)
Arun Kumar, Supun Nakandala, Yuhao Zhang, Side Li, Advitya Gemawat, Kabir Nagrecha
Cerebro: A Layered Data Platform for Scalable Deep Learning. CIDR (2021)
Supun Nakandala, Yuhao Zhang, Arun Kumar
Errata for "Cerebro: A Data System for Optimized Deep Learning Model Selection". Proc. VLDB Endow. 14 (6) (2021)
Arun Kumar
Letter from the Rising Star Award Winner. IEEE Data Eng. Bull. 44 (2) (2021)
Supun Nakandala, Kabir Nagrecha, Arun Kumar, Yannis Papakonstantinou
Incremental and Approximate Computations for Accelerating Deep CNN Inference. ACM Trans. Database Syst. 45 (4) (2020)
Supreeth Shastri, Vinay Banakar, Melissa Wasserman, Arun Kumar, Vijay Chidambaram
Understanding and Benchmarking the Impact of GDPR on Database Systems. Proc. VLDB Endow. 13 (7) (2020)
Supun Nakandala, Arun Kumar
Vista: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale. SIGMOD Conference (2020)
Supun Nakandala, Yuhao Zhang, Arun Kumar
Cerebro: A Data System for Optimized Deep Learning Model Selection. Proc. VLDB Endow. 13 (11) (2020)
Vraj Shah, Side Li, Arun Kumar, Lawrence K. Saul
SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data. SIGMOD Conference (2020)
Supun Nakandala, Arun Kumar, Yannis Papakonstantinou
Query Optimization for Faster Deep CNN Explanations. SIGMOD Rec. 49 (1) (2020)
Side Li, Lingjiao Chen, Arun Kumar
Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra. SIGMOD Conference (2019)
Supreeth Shastri, Vinay Banakar, Melissa Wasserman, Arun Kumar, Vijay Chidambaram
Understanding and Benchmarking the Impact of GDPR on Database Systems. CoRR (2019)
Allen Ordookhanians, Xin Li, Supun Nakandala, Arun Kumar
Demonstration of Krypton: Optimized CNN Inference for Occlusion-based Deep CNN Explanations. Proc. VLDB Endow. 12 (12) (2019)
Jiue-An Yang, Jiayi Wang, Supun Nakandala, Arun Kumar, Marta M. Jankowska
Predicting Eating Events in Free Living Individuals. eScience (2019)
Jiayi Wang, Jiue-An Yang, Supun Nakandala, Arun Kumar, Marta M. Jankowska
Predicting Eating Events in Free Living Individuals - A Technical Report. CoRR (2019)
Supun Nakandala, Yuhao Zhang, Arun Kumar
Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems. DEEM@SIGMOD (2019)
Yuhao Zhang, Arun Kumar
Panorama: A Data System for Unbounded Vocabulary Querying over Video. Proc. VLDB Endow. 13 (4) (2019)
Jiue-An Yang, Jiayi Wang, Supun Nakandala, Arun Kumar, Marta M. Jankowska
Predicting Eating Events in Free Living Individuals. eScience (2019)
Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Xi Wu, Jeffrey F. Naughton, Jignesh M. Patel
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent. SIGMOD Conference (2019)
Wolfgang Gatterbauer, Arun Kumar
Guest Editors' Introduction to the Special Section on the 33rd International Conference on Data Engineering (ICDE 2017). IEEE Trans. Knowl. Data Eng. 31 (7) (2019)
Lingjiao Chen, Paraschos Koutris, Arun Kumar
Towards Model-based Pricing for Machine Learning in a Data Marketplace. SIGMOD Conference (2019)
Supun Nakandala, Arun Kumar, Yannis Papakonstantinou
Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations. SIGMOD Conference (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)
Vraj Shah, Side Li, Kevin Yang, Arun Kumar, Lawrence K. Saul
Demonstration of SpeakQL: Speech-driven Multimodal Querying of Structured Data. SIGMOD Conference (2019)
Vraj Shah, Arun Kumar
The ML Data Prep Zoo: Towards Semi-Automatic Data Preparation for ML. DEEM@SIGMOD (2019)
Lingjiao Chen, Hongyi Wang, Leshang Chen, Paraschos Koutris, Arun Kumar
Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace. SIGMOD Conference (2019)
Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, Hadi Esmaeilzadeh
In-RDBMS Hardware Acceleration of Advanced Analytics. Proc. VLDB Endow. 11 (11) (2018)
Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, Hadi Esmaeilzadeh
In-RDBMS Hardware Acceleration of Advanced Analytics. CoRR (2018)
Lingjiao Chen, Paraschos Koutris, Arun Kumar
Model-based Pricing for Machine Learning in a Data Marketplace. CoRR (2018)
Lingjiao Chen, Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel
Towards Linear Algebra over Normalized Data. Proc. VLDB Endow. 10 (11) (2017)
Vraj Shah, Arun Kumar, Xiaojin Zhu
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? Proc. VLDB Endow. 11 (3) (2017)
Dharmil Chandarana, Vraj Shah, Arun Kumar, Lawrence K. Saul
SpeakQL: Towards Speech-driven Multi-modal Querying. HILDA@SIGMOD (2017)
Fengan Li, Lingjiao Chen, Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel, Xi Wu
When Lempel-Ziv-Welch Meets Machine Learning: A Case Study of Accelerating Machine Learning using Coding. CoRR (2017)
Vraj Shah, Arun Kumar, Xiaojin Zhu
Stop That Join! Discarding Dimension Tables when Learning High Capacity Classifiers. CoRR (2017)
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics. SIGMOD Conference (2017)
Lingjiao Chen, Paraschos Koutris, Arun Kumar
Model-based Pricing: Do Not Pay for More than What You Learn! DEEM@SIGMOD (2017)
Arun Kumar, Matthias Boehm, Jun Yang
Data Management in Machine Learning: Challenges, Techniques, and Systems. SIGMOD Conference (2017)
Xi Wu, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton
Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics. CoRR (2016)
Ce Zhang, Arun Kumar, Christopher Ré
Materialization Optimizations for Feature Selection Workloads. ACM Trans. Database Syst. 41 (1) (2016)
Lingjiao Chen, Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel
Towards Linear Algebra over Normalized Data. CoRR (2016)
Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel, Xiaojin Zhu
To Join or Not to Join?: Thinking Twice about Joins before Feature Selection. SIGMOD Conference (2016)
Arun Kumar, Robert McCann, Jeffrey F. Naughton, Jignesh M. Patel
Model Selection Management Systems: The Next Frontier of Advanced Analytics. SIGMOD Rec. 44 (4) (2015)
Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel
Learning Generalized Linear Models Over Normalized Data. SIGMOD Conference (2015)
Arun Kumar, Mona Jalal, Boqun Yan, Jeffrey F. Naughton, Jignesh M. Patel
Demonstration of Santoku: Optimizing Machine Learning over Normalized Data. Proc. VLDB Endow. 8 (12) (2015)
Ce Zhang, Arun Kumar, Christopher Ré
Materialization optimizations for feature selection workloads. SIGMOD Conference (2014)
Michael R. Anderson, Dolan Antenucci, Victor Bittorf, Matthew Burgess, Michael J. Cafarella, Arun Kumar, Feng Niu, Yongjoo Park, Christopher Ré, Ce Zhang
Brainwash: A Data System for Feature Engineering. CIDR (2013)
Arun Kumar, Feng Niu, Christopher Ré
Hazy: making it easier to build and maintain big-data analytics. Commun. ACM 56 (3) (2013)
Arun Kumar, Feng Niu, Christopher Ré
Hazy: Making it Easier to Build and Maintain Big-data Analytics. ACM Queue 11 (1) (2013)
Pradap Konda, Arun Kumar, Christopher Ré, Vaishnavi Sashikanth
Feature Selection in Enterprise Analytics: A Demonstration using an R-based Data Analytics System. Proc. VLDB Endow. 6 (12) (2013)
Joseph M. Hellerstein, Christopher Ré, Florian Schoppmann, Daisy Zhe Wang, Eugene Fratkin, Aleksander Gorajek, Kee Siong Ng, Caleb Welton, Xixuan Feng, Kun Li, Arun Kumar
The MADlib Analytics Library or MAD Skills, the SQL CoRR (2012)
Xixuan Feng, Arun Kumar, Benjamin Recht, Christopher Ré
Towards a unified architecture for in-RDBMS analytics. SIGMOD Conference (2012)
Joseph M. Hellerstein, Christopher Ré, Florian Schoppmann, Daisy Zhe Wang, Eugene Fratkin, Aleksander Gorajek, Kee Siong Ng, Caleb Welton, Xixuan Feng, Kun Li, Arun Kumar
The MADlib Analytics Library or MAD Skills, the SQL. Proc. VLDB Endow. 5 (12) (2012)
Xixuan Feng, Arun Kumar, Ben Recht, Christopher Ré
Towards a Unified Architecture for in-RDBMS Analytics CoRR (2012)
Arun Kumar, Christopher Ré
Probabilistic Management of OCR Data using an RDBMS. Proc. VLDB Endow. 5 (4) (2011)
Arun Kumar, Christopher Ré
Probabilistic Management of OCR Data using an RDBMS CoRR (2011)