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2020 – today
- 2024
[j54]Audrey Cheng, Aaron N. Kabcenell, Jason Chan, Xiao Shi, Peter D. Bailis, Natacha Crooks, Ion Stoica:
Towards Optimal Transaction Scheduling. Proc. VLDB Endow. 17(11): 2694-2707 (2024)- [c41]Yichao Fu, Peter Bailis, Ion Stoica, Hao Zhang:
Break the Sequential Dependency of LLM Inference Using Lookahead Decoding. ICML 2024 - [c40]Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Alvin Cheung, Zhijie Deng, Ion Stoica, Hao Zhang:
Online Speculative Decoding. ICML 2024 - [i43]Yichao Fu, Peter Bailis, Ion Stoica, Hao Zhang:
Break the Sequential Dependency of LLM Inference Using Lookahead Decoding. CoRR abs/2402.02057 (2024) - [i42]Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei Zaharia, James Zou:
Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems. CoRR abs/2403.02419 (2024) - [i41]Jared Quincy Davis, Boris Hanin, Lingjiao Chen, Peter Bailis, Ion Stoica, Matei Zaharia:
Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design. CoRR abs/2407.16831 (2024) - 2023
- [j53]Peter Kraft, Qian Li, Xinjing Zhou, Peter Bailis, Michael Stonebraker, Xiangyao Yu, Matei Zaharia
:
Epoxy: ACID Transactions Across Diverse Data Stores. Proc. VLDB Endow. 16(11): 2742-2754 (2023) - [i40]Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng, Alvin Cheung, Hao Zhang:
Online Speculative Decoding. CoRR abs/2310.07177 (2023) - 2022
- [j52]Daniel Abadi, Anastasia Ailamaki, David G. Andersen, Peter Bailis, Magdalena Balazinska, Philip A. Bernstein, Peter A. Boncz, Surajit Chaudhuri, Alvin Cheung, AnHai Doan, Luna Dong, Michael J. Franklin, Juliana Freire, Alon Y. Halevy, Joseph M. Hellerstein, Stratos Idreos, Donald Kossmann, Tim Kraska, Sailesh Krishnamurthy, Volker Markl, Sergey Melnik, Tova Milo, C. Mohan, Thomas Neumann, Beng Chin Ooi, Fatma Ozcan, Jignesh M. Patel, Andrew Pavlo, Raluca A. Popa, Raghu Ramakrishnan, Christopher Ré, Michael Stonebraker, Dan Suciu:
The Seattle report on database research. Commun. ACM 65(8): 72-79 (2022) - [j51]Audrey Cheng, Xiao Shi, Aaron N. Kabcenell, Shilpa Lawande, Hamza Qadeer, Jason Chan, Harrison Tin, Ryan Zhao, Peter Bailis, Mahesh Balakrishnan, Nathan Bronson, Natacha Crooks, Ion Stoica:
TAOBench: An End-to-End Benchmark for Social Networking Workloads. Proc. VLDB Endow. 15(9): 1965-1977 (2022) - [j50]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) - [c39]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 - [c38]Daniel Kang, Francisco Romero, Peter D. Bailis, Christos Kozyrakis, Matei Zaharia:
VIVA: An End-to-End System for Interactive Video Analytics. CIDR 2022 - [c37]Peter Kraft, Fiodar Kazhamiaka, Peter Bailis, Matei Zaharia:
Data-Parallel Actors: A Programming Model for Scalable Query Serving Systems. NSDI 2022: 1059-1074 - [c36]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 - [c35]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 - [i39]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) - [i38]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) - 2021
- [j49]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) - [j48]Audrey Cheng, Xiao Shi, Lu Pan, Anthony Simpson, Neil Wheaton, Shilpa Lawande, Nathan Bronson, Peter Bailis, Natacha Crooks, Ion Stoica:
RAMP-TAO: Layering Atomic Transactions on Facebook's Online TAO Data Store. Proc. VLDB Endow. 14(12): 3014-3027 (2021) - [j47]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) - [c34]Fiodar Kazhamiaka, Matei Zaharia, Peter Bailis:
Challenges and Opportunities for Autonomous Vehicle Query Systems. CIDR 2021 - [c33]Kai Sheng Tai, Peter Bailis, Gregory Valiant:
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. ICML 2021: 10065-10075 - [c32]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 - [i37]Kai Sheng Tai, Peter Bailis, Gregory Valiant:
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. CoRR abs/2102.08622 (2021) - [i36]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) - [i35]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia:
Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR abs/2108.06313 (2021) - 2020
- [j46]Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Approximate Selection with Guarantees using Proxies. Proc. VLDB Endow. 13(11): 1990-2003 (2020) - [j45]Edward Gan, Peter Bailis, Moses Charikar:
CoopStore: Optimizing Precomputed Summaries for Aggregation. Proc. VLDB Endow. 13(11): 2174-2187 (2020) - [j44]Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Alexander Levis:
Approximate Partition Selection for Big-Data Workloads using Summary Statistics. Proc. VLDB Endow. 13(11): 2606-2619 (2020) - [j43]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) - [j42]Sahaana Suri, Abishek Sethi, Girija Narlikar, Neslihan Bulut, Raghuveer Chanda, Sugato Basu, Pradyumna Narayana, Peter Bailis, Christopher Ré, Yemao Zeng:
Leveraging Organizational Resources to Adapt Models to New Data Modalities. Proc. VLDB Endow. 13(12): 3396-3410 (2020) - [j41]Peter Bailis, Magda Balazinska, Xin Luna Dong, Juliana Freire, Raghu Ramakrishnan, Michael Stonebraker, Joseph M. Hellerstein:
Winds from Seattle: Database Research Directions. Proc. VLDB Endow. 13(12): 3516 (2020) - [j40]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) - [c31]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 - [c30]Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia:
Model Assertions for Monitoring and Improving ML Models. MLSys 2020 - [c29]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 - [c28]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 - [i34]Edward Gan, Peter Bailis, Moses Charikar:
Storyboard: Optimizing Precomputed Summaries for Aggregation. CoRR abs/2002.03063 (2020) - [i33]Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia:
Model Assertions for Monitoring and Improving ML Models. CoRR abs/2003.01668 (2020) - [i32]Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Approximate Selection with Guarantees using Proxies. CoRR abs/2004.00827 (2020) - [i31]Vladimir Feinberg, Peter Bailis:
Chromatic Learning for Sparse Datasets. CoRR abs/2006.03779 (2020) - [i30]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) - [i29]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) - [i28]Sahaana Suri, Raghuveer Chanda, Neslihan Bulut, Pradyumna Narayana, Yemao Zeng, Peter Bailis, Sugato Basu, Girija Narlikar, Christopher Ré, Abishek Sethi:
Leveraging Organizational Resources to Adapt Models to New Data Modalities. CoRR abs/2008.09983 (2020) - [i27]Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Alexander Levis:
Approximate Partition Selection for Big-Data Workloads using Summary Statistics. CoRR abs/2008.10569 (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
- [j39]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) - [j38]Daniel Abadi, Anastasia Ailamaki, David G. Andersen, Peter Bailis, Magdalena Balazinska, Philip A. Bernstein, Peter A. Boncz, Surajit Chaudhuri, Alvin Cheung, AnHai Doan, Luna Dong, Michael J. Franklin, Juliana Freire, Alon Y. Halevy, Joseph M. Hellerstein, Stratos Idreos, Donald Kossmann, Tim Kraska, Sailesh Krishnamurthy, Volker Markl, Sergey Melnik, Tova Milo, C. Mohan, Thomas Neumann, Beng Chin Ooi, Fatma Ozcan, Jignesh M. Patel, Andrew Pavlo, Raluca A. Popa, Raghu Ramakrishnan, Christopher Ré, Michael Stonebraker, Dan Suciu:
The Seattle Report on Database Research. SIGMOD Rec. 48(4): 44-53 (2019) - [j37]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) - [c27]Daniel Kang, Peter Bailis, Matei Zaharia:
Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. CIDR 2019 - [c26]Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia:
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search. ICDE 2019: 1250-1261 - [c25]Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Learned Intermediate Representation Training for Model Compression. ICML 2019: 3509-3518 - [c24]Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant:
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. ICML 2019: 5690-5700 - [c23]Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Alexander Levis:
Rehashing Kernel Evaluation in High Dimensions. ICML 2019: 5789-5798 - [c22]Kai Sheng Tai, Peter Bailis, Gregory Valiant:
Equivariant Transformer Networks. ICML 2019: 6086-6095 - [c21]Sahaana Suri, Peter Bailis:
DROP: A Workload-Aware Optimizer for Dimensionality Reduction. DEEM@SIGMOD 2019: 1:1-1:10 - [c20]Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis:
CrossTrainer: Practical Domain Adaptation with Loss Reweighting. DEEM@SIGMOD 2019: 2:1-2:10 - [i25]Kai Sheng Tai, Peter Bailis, Gregory Valiant:
Equivariant Transformer Networks. CoRR abs/1901.11399 (2019) - [i24]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 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:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i23]Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis:
CrossTrainer: Practical Domain Adaptation with Loss Reweighting. CoRR abs/1905.02304 (2019) - [i22]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) - [i21]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) - [i20]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) - 2018
- [j36]Albert Kwon, James R. Wilcox, Peter Bailis:
Research for practice: private online communication; highlights in systems verification. Commun. ACM 61(2): 46-49 (2018) - [j35]Malte Schwarzkopf, Peter Bailis:
Research for practice: cluster scheduling for datacenters. Commun. ACM 61(5): 50-53 (2018) - [j34]Deepak Vasisht, Peter Bailis:
Research for practice: toward a network of connected things. Commun. ACM 61(7): 52-54 (2018) - [j33]Daniel Crankshaw, Joseph Gonzalez, Peter Bailis:
Research for practice: prediction-serving systems. Commun. ACM 61(8): 45-49 (2018) - [j32]Gustavo Alonso, Peter Bailis:
Research for practice: FPGAs in datacenters. Commun. ACM 61(9): 48-49 (2018) - [j31]Alexander Ratner, Christopher Ré, Peter Bailis:
Research for practice: knowledge base construction in the machine-learning era. Commun. ACM 61(11): 95-97 (2018) - [j30]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) - [j29]Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis:
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. Proc. VLDB Endow. 11(11): 1647-1660 (2018) - [j28]Kexin Rong, Clara E. Yoon, Karianne J. Bergen, Hashem Elezabi, Peter Bailis, Philip Alexander Levis, Gregory C. Beroza:
Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science. Proc. VLDB Endow. 11(11): 1674-1687 (2018) - [j27]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) - [j26]Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri:
MacroBase: Prioritizing Attention in Fast Data. ACM Trans. Database Syst. 43(4): 15:1-15:45 (2018) - [c19]Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant:
Sketching Linear Classifiers over Data Streams. SIGMOD Conference 2018: 757-772 - [r1]Peter Bailis:
Multi-datacenter Consistency Properties. Encyclopedia of Database Systems (2nd ed.) 2018 - [i19]Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis:
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. CoRR abs/1803.01969 (2018) - [i18]Kexin Rong, Clara E. Yoon, Karianne J. Bergen, Hashem Elezabi, Peter Bailis, Philip Alexander Levis, Gregory C. Beroza:
Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science. CoRR abs/1803.09835 (2018) - [i17]Daniel Kang, Peter Bailis, Matei Zaharia:
BlazeIt: Fast Exploratory Video Queries using Neural Networks. CoRR abs/1805.01046 (2018) - [i16]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) - [i15]Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Block-wise Intermediate Representation Training for Model Compression. CoRR abs/1810.01937 (2018) - 2017
- [j25]Peter Bailis, Jean Yang, Vijay Janapa Reddi, Yuhao Zhu:
Research for practice: web security and mobile web computing. Commun. ACM 60(1): 50-53 (2017) - [j24]Peter Bailis, Irene Zhang, Fadel Adib:
Research for practice: distributed transactions and networks as physical sensors. Commun. ACM 60(3): 46-49 (2017) - [j23]Peter Bailis, Arvind Narayanan, Andrew Miller, Song Han:
Research for practice: cryptocurrencies, blockchains, and smart contracts; hardware for deep learning. Commun. ACM 60(5): 48-51 (2017) - [j22]Peter Bailis, Peter Alvaro, Sumit Gulwani:
Research for practice: tracing and debugging distributed systems; programming by examples. Commun. ACM 60(7): 46-49 (2017) - [j21]Peter Bailis, Tawanna Dillahunt, Stefanie Mueller, Patrick Baudisch:
Research for practice: technology for underserved communities; personal fabrication. Commun. ACM 60(10): 46-49 (2017) - [j20]John Regehr, Peter Bailis:
Research for practice: vigorous public debates in academic computer science. Commun. ACM 60(12): 48-50 (2017) - [j19]Kexin Rong, Peter Bailis:
ASAP: Prioritizing Attention via Time Series Smoothing. Proc. VLDB Endow. 10(11): 1358-1369 (2017) - [j18]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) - [j17]Peter Alvaro, Sumit Gulwani, Peter Bailis:
Research for Practice: Tracing and Debugging Distributed Systems; Programming by Examples. ACM Queue 15(1): 60 (2017) - [j16]Tawanna Dillahunt, Stefanie Mueller, Patrick Baudisch, Peter Bailis:
Research for Practice: Technology for UnderservedCommunities; Personal Fabrication. ACM Queue 15(2): 70 (2017) - [c18]Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri:
Prioritizing Attention in Analytic Monitoring. CIDR 2017 - [c17]Todd Warszawski, Peter Bailis:
ACIDRain: Concurrency-Related Attacks on Database-Backed Web Applications. SIGMOD Conference 2017: 5-20 - [c16]Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri:
MacroBase: Prioritizing Attention in Fast Data. SIGMOD Conference 2017: 541-556 - [c15]Edward Gan, Peter Bailis:
Scalable Kernel Density Classification via Threshold-Based Pruning. SIGMOD Conference 2017: 945-959 - [c14]Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri:
Demonstration: MacroBase, A Fast Data Analysis Engine. SIGMOD Conference 2017: 1699-1702 - [i14]Kexin Rong, Peter Bailis:
ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization. CoRR abs/1703.00983 (2017) - [i13]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) - [i12]Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Infrastructure for Usable Machine Learning: The Stanford DAWN Project. CoRR abs/1705.07538 (2017) - [i11]Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia:
SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations. CoRR abs/1706.01449 (2017) - [i10]Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant:
There and Back Again: A General Approach to Learning Sparse Models. CoRR abs/1706.08146 (2017) - [i9]Sahaana Suri, Peter Bailis:
DROP: Dimensionality Reduction Optimization for Time Series. CoRR abs/1708.00183 (2017) - [i8]Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant:
Finding Heavily-Weighted Features in Data Streams. CoRR abs/1711.02305 (2017) - 2016
- [j15]Peter Bailis, Simon Peter, Justine Sherry:
Introducing research for practice. Commun. ACM 59(9): 38-41 (2016) - [j14]Peter Bailis, Joy Arulraj, Andrew Pavlo:
Research for practice: distributed consensus and implications of NVM on database management systems. Commun. ACM 59(11): 52-55 (2016) - [j13]Peter Bailis, Justine Sherry, Simon Peter:
Introducing Research for Practice. ACM Queue 14(2): 70 (2016) - [j12]Peter Bailis, Camille Fournier, Joy Arulraj, Andy Pavlo:
Research for Practice: Distributed Consensus and Implications of NVM on Database Management Systems. ACM Queue 14(3): 40 (2016) - [j11]Jean Yang, Vijay Janapa Reddi, Yuhao Zhu, Peter Bailis:
Research for Practice: Web Security and Mobile Web Computing. ACM Queue 14(4): 80 (2016) - [j10]Peter Bailis, Irene Zhang, Fadel Adib:
Research for Practice: Distributed Transactions and Networks as Physical Sensors. ACM Queue 14(5): 130-141 (2016) - [j9]Peter Bailis, Arvind Narayanan, Andrew Miller, Song Han:
Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning. ACM Queue 14(6): 43-55 (2016) - [j8]Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica:
Scalable Atomic Visibility with RAMP Transactions. ACM Trans. Database Syst. 41(3): 15:1-15:45 (2016) - [i7]Peter Bailis, Deepak Narayanan, Samuel Madden:
MacroBase: Analytic Monitoring for the Internet of Things. CoRR abs/1603.00567 (2016) - 2015
- [b1]Peter Bailis:
Coordination Avoidance in Distributed Databases. University of California, Berkeley, USA, 2015 - [c13]Peter Bailis:
The Case for Invariant-Based Concurrency Control. CIDR 2015 - [c12]Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan:
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CIDR 2015 - [c11]Peter Bailis, Alan D. Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica:
Feral Concurrency Control: An Empirical Investigation of Modern Application Integrity. SIGMOD Conference 2015: 1327-1342 - [e1]Peter Bailis, Joseph M. Hellerstein, Michael Stonebraker:
Readings in Database Systems, 5th Edition. 2015 - [i6]Joseph E. Gonzalez, Peter Bailis, Michael I. Jordan, Michael J. Franklin, Joseph M. Hellerstein, Ali Ghodsi, Ion Stoica:
Asynchronous Complex Analytics in a Distributed Dataflow Architecture. CoRR abs/1510.07092 (2015) - 2014
- [j7]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
Quantifying eventual consistency with PBS. Commun. ACM 57(8): 93-102 (2014) - [j6]Peter Bailis, Kyle Kingsbury:
The network is reliable. Commun. ACM 57(9): 48-55 (2014) - [j5]Peter Bailis, Alan D. Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica:
Coordination Avoidance in Database Systems. Proc. VLDB Endow. 8(3): 185-196 (2014) - [j4]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
Quantifying eventual consistency with PBS. VLDB J. 23(2): 279-302 (2014) - [c10]Peter Bailis, Alan D. Fekete, Joseph M. Hellerstein, Ali Ghodsi, Ion Stoica:
Scalable atomic visibility with RAMP transactions. SIGMOD Conference 2014: 27-38 - [i5]Peter Bailis, Alan D. Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica:
Coordination-Avoiding Database Systems. CoRR abs/1402.2237 (2014) - [i4]Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan:
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CoRR abs/1409.3809 (2014) - 2013
- [j3]Peter Bailis, Ali Ghodsi:
Eventual consistency today: limitations, extensions, and beyond. Commun. ACM 56(5): 55-63 (2013) - [j2]Peter Bailis, Aaron Davidson, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica:
Highly Available Transactions: Virtues and Limitations. Proc. VLDB Endow. 7(3): 181-192 (2013) - [c9]Peter Bailis:
HAT, not CAP: Highly Available Transactions for Everybody. CIDR 2013 - [c8]Peter Alvaro, Peter Bailis, Neil Conway, Joseph M. Hellerstein:
Consistency without borders. SoCC 2013: 23:1-23:10 - [c7]Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica:
HAT, Not CAP: Towards Highly Available Transactions. HotOS 2013 - [c6]Peter Bailis, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica:
Bolt-on causal consistency. SIGMOD Conference 2013: 761-772 - [c5]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
PBS at work: advancing data management with consistency metrics. SIGMOD Conference 2013: 1113-1116 - [i3]Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica:
HAT, not CAP: Highly Available Transactions. CoRR abs/1302.0309 (2013) - 2012
- [j1]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
Probabilistically Bounded Staleness for Practical Partial Quorums. Proc. VLDB Endow. 5(8): 776-787 (2012) - [c4]Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica:
The potential dangers of causal consistency and an explicit solution. SoCC 2012: 22 - [i2]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
Probabilistically Bounded Staleness for Practical Partial Quorums. CoRR abs/1204.6082 (2012) - [i1]Peter Bailis, Justine Sherry:
TinyToCS Volume 1 Chairs' Note. Tiny Trans. Comput. Sci. 1 (2012) - 2011
- [c3]Peter Bailis, Vijay Janapa Reddi, Sanjay Gandhi, David M. Brooks, Margo I. Seltzer:
Dimetrodon: processor-level preventive thermal management via idle cycle injection. DAC 2011: 89-94 - [c2]Karthik Dantu, Bryan Kate, Jason Waterman, Peter Bailis, Matt Welsh:
Programming micro-aerial vehicle swarms with karma. SenSys 2011: 121-134 - 2010
- [c1]Peter Bailis, Radhika Nagpal, Justin Werfel:
Positional Communication and Private Information in Honeybee Foraging Models. ANTS Conference 2010: 263-274
Coauthor Index
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