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ORCIDMatei Zaharia
Matei A. Zaharia
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- affiliation: Stanford University, CA, USA
- award (2019): Presidential Early Career Award for Scientists and Engineers
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2020 – today
- 2023
[i65]Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, Tatsunori Hashimoto:
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. CoRR abs/2302.05733 (2023)- [i64]Francisco Romero, Caleb Winston, Johann Hauswald, Matei Zaharia, Christos Kozyrakis:
Zelda: Video Analytics using Vision-Language Models. CoRR abs/2305.03785 (2023) - [i63]Lingjiao Chen, Matei Zaharia, James Zou:
FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance. CoRR abs/2305.05176 (2023) - 2022
- [j37]Mihai Budiu, Pratiksha Thaker
, Parikshit Gopalan, Udi Wieder, Matei Zaharia:
Overlook: Differentially Private Exploratory Visualization for Big Data. J. Priv. Confidentiality 12(1) (2022) - [j36]Akshay Agrawal, Stephen P. Boyd, Deepak Narayanan, Fiodar Kazhamiaka, Matei Zaharia:
Allocation of fungible resources via a fast, scalable price discovery method. Math. Program. Comput. 14(3): 593-622 (2022) - [j35]Weixin Liang, Girmaw Abebe Tadesse, Daniel E. Ho, Li Fei-Fei, Matei Zaharia, Ce Zhang, James Zou:
Advances, challenges and opportunities in creating data for trustworthy AI. Nat. Mach. Intell. 4(8): 669-677 (2022) - [j34]Weixin Liang, Girmaw Abebe Tadesse, Daniel E. Ho, Li Fei-Fei, Matei Zaharia, Ce Zhang, James Zou:
Author Correction: Advances, challenges and opportunities in creating data for trustworthy AI. Nat. Mac. Intell. 4(10): 904 (2022) - [j33]Magdalena Balazinska, Surajit Chaudhuri, AnHai Doan, Joseph M. Hellerstein, Hanuma Kodavalla, Ippokratis Pandis, Matei Zaharia:
Cloud Data Systems: What are the Opportunities for the Database Research Community? Proc. VLDB Endow. 15(12): 3826-3827 (2022) - [j32]Francisco Romero, Johann Hauswald, Aditi Partap, Daniel Kang, Matei Zaharia, Christos Kozyrakis:
Optimizing Video Analytics with Declarative Model Relationships. Proc. VLDB Endow. 16(3): 447-460 (2022) - [j31]Nirvik Baruah, Peter Kraft, Fiodar Kazhamiaka, Peter Bailis, Matei Zaharia:
Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations. Proc. VLDB Endow. 16(4): 760-771 (2022) - [c92]Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter Bailis, Alexander C. Berg, Robert D. Nowak, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz:
Similarity Search for Efficient Active Learning and Search of Rare Concepts. AAAI 2022: 6402-6410 - [c91]Qian Li, Peter Kraft, Kostis Kaffes, Athinagoras Skiadopoulos, Deeptaanshu Kumar, Jason Li, Michael J. Cafarella, Goetz Graefe, Jeremy Kepner, Christos Kozyrakis, Michael Stonebraker, Lalith Suresh, Matei Zaharia:
A Progress Report on DBOS: A Database-oriented Operating System. CIDR 2022 - [c90]Daniel Kang, Francisco Romero, Peter D. Bailis, Christos Kozyrakis, Matei Zaharia:
VIVA: An End-to-End System for Interactive Video Analytics. CIDR 2022 - [c89]Keshav Santhanam, Omar Khattab, Christopher Potts, Matei Zaharia:
PLAID: An Efficient Engine for Late Interaction Retrieval. CIKM 2022: 1747-1756 - [c88]Lingjiao Chen, Matei Zaharia, James Zou:
How Did the Model Change? Efficiently Assessing Machine Learning API Shifts. ICLR 2022 - [c87]Ashwin Paranjape, Omar Khattab, Christopher Potts, Matei Zaharia, Christopher D. Manning:
Hindsight: Posterior-guided training of retrievers for improved open-ended generation. ICLR 2022 - [c86]Lingjiao Chen, Matei Zaharia, James Zou:
Efficient Online ML API Selection for Multi-Label Classification Tasks. ICML 2022: 3716-3746 - [c85]Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, Matei Zaharia:
ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction. NAACL-HLT 2022: 3715-3734 - [c84]Lingjiao Chen, Zhihua Jin, Sabri Eyuboglu, Christopher Ré, Matei Zaharia, James Y. Zou:
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions. NeurIPS 2022 - [c83]Lingjiao Chen, Matei Zaharia, James Y. Zou:
Estimating and Explaining Model Performance When Both Covariates and Labels Shift. NeurIPS 2022 - [c82]Peter Kraft, Fiodar Kazhamiaka, Peter Bailis, Matei Zaharia:
Data-Parallel Actors: A Programming Model for Scalable Query Serving Systems. NSDI 2022: 1059-1074 - [c81]Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter D. Bailis, Matei Zaharia:
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. SIGMOD Conference 2022: 496-505 - [c80]Daniel Kang, John Guibas, Peter D. Bailis, Tatsunori Hashimoto, Matei Zaharia:
TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data. SIGMOD Conference 2022: 1934-1947 - [c79]Alexander Behm, Shoumik Palkar, Utkarsh Agarwal, Timothy Armstrong, David Cashman, Ankur Dave, Todd Greenstein, Shant Hovsepian, Ryan Johnson, Arvind Sai Krishnan, Paul Leventis, Ala Luszczak, Prashanth Menon, Mostafa Mokhtar, Gene Pang, Sameer Paranjpye, Greg Rahn, Bart Samwel, Tom van Bussel, Herman Van Hovell, Maryann Xue, Reynold Xin, Matei Zaharia:
Photon: A Fast Query Engine for Lakehouse Systems. SIGMOD Conference 2022: 2326-2339 - [i62]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) - [i61]Gina Yuan, David Mazières, Matei Zaharia:
Extricating IoT Devices from Vendor Infrastructure with Karl. CoRR abs/2204.13737 (2022) - [i60]Keshav Santhanam, Omar Khattab, Christopher Potts, Matei Zaharia:
PLAID: An Efficient Engine for Late Interaction Retrieval. CoRR abs/2205.09707 (2022) - [i59]Peter Kraft, Qian Li, Kostis Kaffes, Athinagoras Skiadopoulos, Deeptaanshu Kumar, Danny Cho, Jason Li, Robert Redmond, Nathan W. Weckwerth, Brian S. Xia, Peter Bailis, Michael J. Cafarella, Goetz Graefe, Jeremy Kepner, Christos Kozyrakis, Michael Stonebraker, Lalith Suresh, Xiangyao Yu, Matei Zaharia:
Apiary: A DBMS-Backed Transactional Function-as-a-Service Framework. CoRR abs/2208.13068 (2022) - [i58]Lingjiao Chen, Matei Zaharia, James Zou:
Estimating and Explaining Model Performance When Both Covariates and Labels Shift. CoRR abs/2209.08436 (2022) - [i57]Lingjiao Chen, Zhihua Jin, Sabri Eyuboglu, Christopher Ré, Matei Zaharia, James Zou:
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions. CoRR abs/2209.08443 (2022) - [i56]Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia:
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts. CoRR abs/2211.15841 (2022) - [i55]Keshav Santhanam, Jon Saad-Falcon, Martin Franz, Omar Khattab, Avirup Sil, Radu Florian, Md. Arafat Sultan, Salim Roukos, Matei Zaharia, Christopher Potts:
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking. CoRR abs/2212.01340 (2022) - [i54]Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, Matei Zaharia:
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP. CoRR abs/2212.14024 (2022) - [i53]Qian Li, Peter Kraft, Michael J. Cafarella, Çagatay Demiralp, Goetz Graefe, Christos Kozyrakis, Michael Stonebraker, Lalith Suresh, Matei Zaharia:
Transactions Make Debugging Easy. CoRR abs/2212.14161 (2022) - 2021
- [j30]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) - [j29]Matei Zaharia:
Designing Production-Friendly Machine Learning. Proc. VLDB Endow. 14(13): 3420 (2021) - [j28]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) - [j27]Omar Khattab, Christopher Potts, Matei Zaharia:
Relevance-guided Supervision for OpenQA with ColBERT. Trans. Assoc. Comput. Linguistics 9: 929-944 (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 - [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 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 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 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]