Stop the war!Остановите войну!
for scientists:
Google
Google Scholar
Semantic Scholar
IA Scholar
CiteSeerX
ORCIDMatei Zaharia
Matei A. Zaharia
Person information
- affiliation: Stanford University, CA, USA
- award (2019): Presidential Early Career Award for Scientists and Engineers
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2022
[i55]Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter Bailis, Matei Zaharia:
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. CoRR abs/2201.05797 (2022)- [i54]Gina Yuan, David Mazières, Matei Zaharia:
Extricating IoT Devices from Vendor Infrastructure with Karl. CoRR abs/2204.13737 (2022) - [i53]Keshav Santhanam, Omar Khattab, Christopher Potts, Matei Zaharia:
PLAID: An Efficient Engine for Late Interaction Retrieval. CoRR abs/2205.09707 (2022) - 2021
- [j29]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia:
Accelerating Approximate Aggregation Queries with Expensive Predicates. Proc. VLDB Endow. 14(11): 2341-2354 (2021) - [j28]Matei Zaharia:
Designing Production-Friendly Machine Learning. Proc. VLDB Endow. 14(13): 3420 (2021) - [j27]Athinagoras Skiadopoulos, Qian Li, Peter Kraft, Kostis Kaffes, Daniel Hong, Shana Mathew, David Bestor, Michael J. Cafarella, Vijay Gadepally, Goetz Graefe, Jeremy Kepner, Christos Kozyrakis, Tim Kraska, Michael Stonebraker, Lalith Suresh, Matei Zaharia:
DBOS: A DBMS-oriented Operating System. Proc. VLDB Endow. 15(1): 21-30 (2021) - [j26]Firas Abuzaid
, Peter Kraft, Sahaana Suri, Edward Gan, Eric Xu, Atul Shenoy, Asvin Ananthanarayan, John Sheu, Erik Meijer, Xi Wu, Jeffrey F. Naughton, Peter Bailis, Matei Zaharia:
DIFF: a relational interface for large-scale data explanation. VLDB J. 30(1): 45-70 (2021) - [c78]Fiodar Kazhamiaka, Matei Zaharia, Peter Bailis:
Challenges and Opportunities for Autonomous Vehicle Query Systems. CIDR 2021 - [c77]Matei Zaharia, Ali Ghodsi, Reynold Xin, Michael Armbrust:
Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics. CIDR 2021 - [c76]Pratiksha Thaker, Hudson Ayers, Deepti Raghavan, Ning Niu, Philip Alexander Levis, Matei Zaharia:
Clamor: Extending Functional Cluster Computing Frameworks with Fine-Grained Remote Memory Access. SoCC 2021: 654-669 - [c75]Pratiksha Thaker, Matei Zaharia, Tatsunori Hashimoto:
Don't Hate the Player, Hate the Game: Safety and Utility in Multi-Agent Congestion Control. HotNets 2021: 140-146 - [c74]Deepti Raghavan, Philip Alexander Levis, Matei Zaharia, Irene Zhang:
Breakfast of champions: towards zero-copy serialization with NIC scatter-gather. HotOS 2021: 199-205 - [c73]Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia:
Memory-Efficient Pipeline-Parallel DNN Training. ICML 2021: 7937-7947 - [c72]Omar Khattab, Christopher Potts, Matei A. Zaharia:
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval. NeurIPS 2021: 27670-27682 - [c71]Firas Abuzaid, Srikanth Kandula, Behnaz Arzani, Ishai Menache, Matei Zaharia, Peter Bailis:
Contracting Wide-area Network Topologies to Solve Flow Problems Quickly. NSDI 2021: 175-200 - [c70]Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia:
Efficient large-scale language model training on GPU clusters using megatron-LM. SC 2021: 58:1-58:15 - [c69]Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Akshay Agrawal, Srikanth Kandula, Stephen P. Boyd, Matei Zaharia:
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. SOSP 2021: 521-537 - [c68]Saba Eskandarian, Henry Corrigan-Gibbs, Matei Zaharia, Dan Boneh:
Express: Lowering the Cost of Metadata-hiding Communication with Cryptographic Privacy. USENIX Security Symposium 2021: 1775-1792 - [i52]Omar Khattab, Christopher Potts, Matei Zaharia:
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval. CoRR abs/2101.00436 (2021) - [i51]Lingjiao Chen, Matei Zaharia, James Zou:
FrugalMCT: Efficient Online ML API Selection for Multi-Label Classification Tasks. CoRR abs/2102.09127 (2021) - [i50]Akshay Agrawal, Stephen P. Boyd, Deepak Narayanan, Fiodar Kazhamiaka, Matei Zaharia:
Allocation of Fungible Resources via a Fast, Scalable Price Discovery Method. CoRR abs/2104.00282 (2021) - [i49]Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia:
Efficient Large-Scale Language Model Training on GPU Clusters. CoRR abs/2104.04473 (2021) - [i48]Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Matei Zaharia:
Don't Give Up on Large Optimization Problems; POP Them! CoRR abs/2104.06513 (2021) - [i47]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia:
Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR abs/2107.12525 (2021) - [i46]Lingjiao Chen, Tracy Cai, Matei Zaharia, James Zou:
Did the Model Change? Efficiently Assessing Machine Learning API Shifts. CoRR abs/2107.14203 (2021) - [i45]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia:
Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR abs/2108.06313 (2021) - [i44]Ashwin Paranjape, Omar Khattab, Christopher Potts, Matei Zaharia, Christopher D. Manning:
Hindsight: Posterior-guided training of retrievers for improved open-ended generation. CoRR abs/2110.07752 (2021) - [i43]Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Akshay Agrawal, Srikanth Kandula, Stephen P. Boyd, Matei Zaharia:
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. CoRR abs/2110.11927 (2021) - [i42]Keshav Santhanam, Siddharth Krishna, Ryota Tomioka, Tim Harris, Matei Zaharia:
DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution. CoRR abs/2111.05426 (2021) - [i41]Yuezhou Sun, Wenlong Zhao, Lijun Zhang, Xiao Liu, Hui Guan, Matei Zaharia:
Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression. CoRR abs/2111.10320 (2021) - [i40]Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, Matei Zaharia:
ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction. CoRR abs/2112.01488 (2021) - [i39]Neoklis Polyzotis, Matei Zaharia:
What can Data-Centric AI Learn from Data and ML Engineering? CoRR abs/2112.06439 (2021) - 2020
- [j25]Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Approximate Selection with Guarantees using Proxies. Proc. VLDB Endow. 13(11): 1990-2003 (2020) - [j24]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. Proc. VLDB Endow. 13(12): 2833-2836 (2020) - [j23]Michael Armbrust, Tathagata Das, Sameer Paranjpye, Reynold Xin, Shixiong Zhu, Ali Ghodsi, Burak Yavuz, Mukul Murthy, Joseph Torres, Liwen Sun, Peter A. Boncz, Mostafa Mokhtar, Herman Van Hovell, Adrian Ionescu, Alicja Luszczak, Michal Switakowski, Takuya Ueshin, Xiao Li, Michal Szafranski, Pieter Senster, Matei Zaharia:
Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores. Proc. VLDB Endow. 13(12): 3411-3424 (2020) - [j22]Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia:
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. Proc. VLDB Endow. 14(2): 87-100 (2020) - [j21]Deepti Raghavan, Sadjad Fouladi, Philip Alexander Levis, Matei Zaharia:
Posh: A Data-Aware Shell. login Usenix Mag. 45(4) (2020) - [c67]James J. Thomas, Pat Hanrahan, Matei Zaharia:
Fleet: A Framework for Massively Parallel Streaming on FPGAs. ASPLOS 2020: 639-651 - [c66]Benyu Zhang, Matei Zaharia, Shouling Ji, Raluca Ada Popa, Guofei Gu:
PPMLP 2020: Workshop on Privacy-Preserving Machine Learning In Practice. CCS 2020: 2139-2140 - [c65]Michael J. Cafarella, David J. DeWitt, Vijay Gadepally, Jeremy Kepner, Christos Kozyrakis, Tim Kraska, Michael Stonebraker, Matei Zaharia:
A Polystore Based Database Operating System (DBOS). Poly/DMAH@VLDB 2020: 3-24 - [c64]Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia:
Selection via Proxy: Efficient Data Selection for Deep Learning. ICLR 2020 - [c63]Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, Alex Aiken:
Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc. MLSys 2020 - [c62]Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia:
Model Assertions for Monitoring and Improving ML Models. MLSys 2020 - [c61]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. MLSys 2020 - [c60]Peter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. MLSys 2020 - [c59]Lingjiao Chen, Matei Zaharia, James Y. Zou:
FrugalML: How to use ML Prediction APIs more accurately and cheaply. NeurIPS 2020 - [c58]Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia:
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. OSDI 2020: 481-498 - [c57]Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen:
Sparse GPU kernels for deep learning. SC 2020: 17 - [c56]Omar Khattab, Matei Zaharia:
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. SIGIR 2020: 39-48 - [c55]Andrew Chen, Andy Chow, Aaron Davidson, Arjun DCunha, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Clemens Mewald, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe, Avesh Singh, Fen Xie, Matei Zaharia, Richard Zang, Juntai Zheng, Corey Zumar:
Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle. DEEM@SIGMOD 2020: 5:1-5:4 - [c54]Saachi Jain, Matei Zaharia:
Spectral Lower Bounds on the I/O Complexity of Computation Graphs. SPAA 2020: 329-338 - [c53]Gina Yuan, Shoumik Palkar, Deepak Narayanan, Matei Zaharia:
Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads. USENIX Annual Technical Conference 2020: 293-306 - [c52]Deepti Raghavan, Sadjad Fouladi, Philip Alexander Levis, Matei Zaharia:
POSH: A Data-Aware Shell. USENIX Annual Technical Conference 2020: 617-631 - [e2]Benyu Zhang, Raluca Ada Popa, Matei Zaharia, Guofei Gu, Shouling Ji:
PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, Virtual Event, USA, November, 2020. ACM 2020, ISBN 978-1-4503-8088-1 [contents] - [i38]Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia:
Model Assertions for Monitoring and Improving ML Models. CoRR abs/2003.01668 (2020) - [i37]Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Approximate Selection with Guarantees using Proxies. CoRR abs/2004.00827 (2020) - [i36]Omar Khattab, Matei Zaharia:
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. CoRR abs/2004.12832 (2020) - [i35]Lingjiao Chen, Matei Zaharia, James Zou:
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply. CoRR abs/2006.07512 (2020) - [i34]Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia:
Memory-Efficient Pipeline-Parallel DNN Training. CoRR abs/2006.09503 (2020) - [i33]Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen:
Sparse GPU Kernels for Deep Learning. CoRR abs/2006.10901 (2020) - [i32]Pratiksha Thaker, Mihai Budiu, Parikshit Gopalan, Udi Wieder, Matei Zaharia:
Overlook: Differentially Private Exploratory Visualization for Big Data. CoRR abs/2006.12018 (2020) - [i31]Cody Coleman, Edward Chou, Sean Culatana, Peter Bailis, Alexander C. Berg, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz:
Similarity Search for Efficient Active Learning and Search of Rare Concepts. CoRR abs/2007.00077 (2020) - [i30]Omar Khattab, Christopher Potts, Matei Zaharia:
Relevance-guided Supervision for OpenQA with ColBERT. CoRR abs/2007.00814 (2020) - [i29]Michael J. Cafarella, David J. DeWitt, Vijay Gadepally, Jeremy Kepner, Christos Kozyrakis, Tim Kraska, Michael Stonebraker, Matei Zaharia:
DBOS: A Proposal for a Data-Centric Operating System. CoRR abs/2007.11112 (2020) - [i28]Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia:
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. CoRR abs/2007.13005 (2020) - [i27]Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia:
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. CoRR abs/2008.09213 (2020) - [i26]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. CoRR abs/2009.04540 (2020)
2010 – 2019
- 2019
- [j20]Saba Eskandarian, Matei Zaharia:
ObliDB: Oblivious Query Processing for Secure Databases. Proc. VLDB Endow. 13(2): 169-183 (2019) - [j19]Daniel Kang, Peter Bailis, Matei Zaharia:
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. Proc. VLDB Endow. 13(4): 533-546 (2019) - [j18]Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. ACM SIGOPS Oper. Syst. Rev. 53(1): 14-25 (2019) - [j17]Sadjad Fouladi, Francisco Romero, Dan Iter, Qian Li, Alex Ozdemir, Shuvo Chatterjee, Matei Zaharia, Christos Kozyrakis, Keith Winstein:
Outsourcing Everyday Jobs to Thousands of Cloud Functions with gg. login Usenix Mag. 44(3) (2019) - [c51]Daniel Kang, Peter Bailis, Matei Zaharia:
Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. CIDR 2019 - [c50]Matei Zaharia:
Lessons from Large-Scale Software as a Service at Databricks. SoCC 2019: 101 - [c49]Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia:
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search. ICDE 2019: 1250-1261 - [c48]Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Learned Intermediate Representation Training for Model Compression. ICML 2019: 3509-3518 - [c47]Zhihao Jia, James J. Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, Alex Aiken:
Optimizing DNN Computation with Relaxed Graph Substitutions. MLSys 2019 - [c46]Zhihao Jia, Matei Zaharia, Alex Aiken:
Beyond Data and Model Parallelism for Deep Neural Networks. MLSys 2019 - [c45]Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, Matei Zaharia:
PipeDream: generalized pipeline parallelism for DNN training. SOSP 2019: 1-15 - [c44]Zhihao Jia, Oded Padon, James J. Thomas, Todd Warszawski, Matei Zaharia, Alex Aiken:
TASO: optimizing deep learning computation with automatic generation of graph substitutions. SOSP 2019: 47-62 - [c43]Shoumik Palkar, Matei Zaharia:
Optimizing data-intensive computations in existing libraries with split annotations. SOSP 2019: 291-305 - [c42]Sadjad Fouladi, Francisco Romero, Dan Iter, Qian Li, Shuvo Chatterjee, Christos Kozyrakis, Matei Zaharia, Keith Winstein:
From Laptop to Lambda: Outsourcing Everyday Jobs to Thousands of Transient Functional Containers. USENIX Annual Technical Conference 2019: 475-488 - [e1]Ameet Talwalkar, Virginia Smith, Matei Zaharia:
Proceedings of Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA, March 31 - April 2, 2019. mlsys.org 2019 [contents] - [i25]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 abs/1904.03257 (2019) - [i24]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. CoRR abs/1906.01974 (2019) - [i23]Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia:
Selection Via Proxy: Efficient Data Selection For Deep Learning. CoRR abs/1906.11829 (2019) - [i22]Saachi Jain, Matei Zaharia:
Automated Lower Bounds on the I/O Complexity of Computation Graphs. CoRR abs/1909.09791 (2019) - [i21]Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim M. Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. CoRR abs/1910.01500 (2019) - [i20]Saba Eskandarian, Henry Corrigan-Gibbs, Matei Zaharia, Dan Boneh:
Express: Lowering the Cost of Metadata-hiding Communication with Cryptographic Privacy. CoRR abs/1911.09215 (2019) - 2018
- [j16]Matei Zaharia, Andrew Chen, Aaron Davidson, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe, Fen Xie, Corey Zumar:
Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Eng. Bull. 41(4): 39-45 (2018) - [j15]Shoumik Palkar, James J. Thomas, Deepak Narayanan, Pratiksha Thaker, Rahul Palamuttam, Parimarjan Negi, Anil Shanbhag, Malte Schwarzkopf, Holger Pirk, Saman P. Amarasinghe, Samuel Madden, Matei Zaharia:
Evaluating End-to-End Optimization for Data Analytics Applications in Weld. Proc. VLDB Endow. 11(9): 1002-1015 (2018) - [j14]Shoumik Palkar, Firas Abuzaid, Peter Bailis, Matei Zaharia:
Filter Before You Parse: Faster Analytics on Raw Data with Sparser. Proc. VLDB Endow. 11(11): 1576-1589 (2018) - [j13]Firas Abuzaid, Peter Kraft, Sahaana Suri, Edward Gan, Eric Xu, Atul Shenoy, Asvin Anathanaraya, John Sheu, Erik Meijer, Xi Wu, Jeffrey F. Naughton, Peter Bailis, Matei Zaharia:
DIFF: A Relational Interface for Large-Scale Data Explanation. Proc. VLDB Endow. 12(4): 419-432 (2018) - [c41]Michael Armbrust, Tathagata Das, Joseph Torres, Burak Yavuz, Shixiong Zhu, Reynold Xin, Ali Ghodsi, Ion Stoica, Matei Zaharia:
Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark. SIGMOD Conference 2018: 601-613 - [c40]Manasi Vartak, Joana M. F. da Trindade, Samuel Madden, Matei Zaharia:
MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis. SIGMOD Conference 2018: 1285-1300 - [r1]Volker Markl, Vinayak R. Borkar, Matei Zaharia, Till Westmann, Alexander Alexandrov:
Big Data Platforms for Data Analytics. Encyclopedia of Database Systems (2nd ed.) 2018 - [i19]Daniel Kang, Peter Bailis, Matei Zaharia:
BlazeIt: Fast Exploratory Video Queries using Neural Networks. CoRR abs/1805.01046 (2018) - [i18]Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. CoRR abs/1806.01427 (2018) - [i17]Zhihao Jia, Matei Zaharia, Alex Aiken:
Beyond Data and Model Parallelism for Deep Neural Networks. CoRR abs/1807.05358 (2018) - [i16]Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Block-wise Intermediate Representation Training for Model Compression. CoRR abs/1810.01937 (2018) - [i15]Shoumik Palkar, Matei Zaharia:
Splitability Annotations: Optimizing Black-Box Function Composition in Existing Libraries. CoRR abs/1810.12297 (2018) - 2017
- [j12]Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia:
NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale. Proc. VLDB Endow. 10(11): 1586-1597 (2017) - [c39]Yunming Zhang, Vladimir Kiriansky, Charith Mendis, Saman P. Amarasinghe, Matei Zaharia:
Making caches work for graph analytics. IEEE BigData 2017: 293-302 - [c38]Shoumik Palkar, James J. Thomas, Anil Shanbhag, Malte Schwarzkopf, Saman P. Amarasinghe, Matei Zaharia:
A Common Runtime for High Performance Data Analysis. CIDR 2017 - [c37]Shoumik Palkar, Matei Zaharia:
DIY Hosting for Online Privacy. HotNets 2017: 1-7 - [c36]Frank Wang, Catherine Yun, Shafi Goldwasser, Vinod Vaikuntanathan, Matei Zaharia:
Splinter: Practical Private Queries on Public Data. NSDI 2017: 299-313 - [c35]Nirvan Tyagi, Yossi Gilad, Derek Leung, Matei Zaharia, Nickolai Zeldovich:
Stadium: A Distributed Metadata-Private Messaging System. SOSP 2017: 423-440 - [i14]Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia:
Optimizing Deep CNN-Based Queries over Video Streams at Scale. CoRR abs/1703.02529 (2017) - [i13]Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Infrastructure for Usable Machine Learning: The Stanford DAWN Project. CoRR abs/1705.07538 (2017) - [i12]Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia:
SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations. CoRR abs/1706.01449 (2017) - [i11]Shoumik Palkar, James J. Thomas, Deepak Narayanan, Anil Shanbhag, Rahul Palamuttam, Holger Pirk, Malte Schwarzkopf, Saman P. Amarasinghe, Samuel Madden, Matei Zaharia:
Weld: Rethinking the Interface Between Data-Intensive Applications. CoRR abs/1709.06416 (2017) - [i10]Saba Eskandarian, Matei Zaharia:
An Oblivious General-Purpose SQL Database for the Cloud. CoRR abs/1710.00458 (2017) - 2016
- [j11]Matei Zaharia, Reynold S. Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, Michael J. Franklin, Ali Ghodsi, Joseph Gonzalez, Scott Shenker, Ion Stoica:
Apache Spark: a unified engine for big data processing. Commun. ACM 59(11): 56-65 (2016) - [j10]Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan R. Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar:
MLlib: Machine Learning in Apache Spark. J. Mach. Learn. Res. 17: 34:1-34:7 (2016) - [j9]Holger Pirk, Oscar R. Moll, Matei Zaharia, Sam Madden:
Voodoo - A Vector Algebra for Portable Database Performance on Modern Hardware. Proc. VLDB Endow. 9(14): 1707-1718 (2016) - [c34]Ankur Dave, Alekh Jindal,