Peter Bailis

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 106

Top Venues

CoRR

Proc. VLDB Endow.

Commun. ACM

ACM Queue

University of California, Berkeley, USA (PHD)

Education History

University of California, Berkeley, USA

PHD

Publications

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 (2024)
Yichao Fu, Peter Bailis, Ion Stoica, Hao Zhang
Break the Sequential Dependency of LLM Inference Using Lookahead Decoding. CoRR (2024)
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 (2024)
Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng, Alvin Cheung, Hao Zhang
Online Speculative Decoding. CoRR (2023)
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) (2023)
Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter Bailis, Matei Zaharia
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. CoRR (2022)
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 (2022)
Peter Kraft, Fiodar Kazhamiaka, Peter Bailis, Matei Zaharia
Data-Parallel Actors: A Programming Model for Scalable Query Serving Systems. NSDI (2022)
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)
Nirvik Baruah, Peter Kraft, Fiodar Kazhamiaka, Peter Bailis, Matei Zaharia
Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations. Proc. VLDB Endow. 16 (4) (2022)
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) (2022)
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) (2022)
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. ICML (2021)
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) (2021)
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. CoRR (2021)
Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia
Accelerating Approximate Aggregation Queries with Expensive Predicates. Proc. VLDB Endow. 14 (11) (2021)
Fiodar Kazhamiaka, Matei Zaharia, Peter Bailis
Challenges and Opportunities for Autonomous Vehicle Query Systems. CIDR (2021)
Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia
Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR (2021)
Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia
Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR (2021)
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) (2021)
Firas Abuzaid, Srikanth Kandula, Behnaz Arzani, Ishai Menache, Matei Zaharia, Peter Bailis
Contracting Wide-area Network Topologies to Solve Flow Problems Quickly. NSDI (2021)
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) (2020)
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 (2020)
Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
Approximate Selection with Guarantees using Proxies. Proc. VLDB Endow. 13 (11) (2020)
Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia
Model Assertions for Monitoring and Improving ML Models. MLSys (2020)
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) (2020)
Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Alexander Levis
Approximate Partition Selection for Big-Data Workloads using Summary Statistics. CoRR (2020)
Edward Gan, Peter Bailis, Moses Charikar
Storyboard: Optimizing Precomputed Summaries for Aggregation. CoRR (2020)
Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. CoRR (2020)
Edward Gan, Peter Bailis, Moses Charikar
CoopStore: Optimizing Precomputed Summaries for Aggregation. Proc. VLDB Endow. 13 (11) (2020)
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)
Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. CoRR (2020)
Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
Approximate Selection with Guarantees using Proxies. CoRR (2020)
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) (2020)
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 (2020)
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) (2020)
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)
Vladimir Feinberg, Peter Bailis
Chromatic Learning for Sparse Datasets. CoRR (2020)
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)
Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia
Model Assertions for Monitoring and Improving ML Models. CoRR (2020)
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) (2020)
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 (2019)
Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis
CrossTrainer: Practical Domain Adaptation with Loss Reweighting. CoRR (2019)
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) (2019)
Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search. ICDE (2019)
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 (2019)
Daniel Kang, Peter Bailis, Matei Zaharia
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. Proc. VLDB Endow. 13 (4) (2019)
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Equivariant Transformer Networks. ICML (2019)
Sahaana Suri, Peter Bailis
DROP: A Workload-Aware Optimizer for Dimensionality Reduction. DEEM@SIGMOD (2019)
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 (2019)
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. ICML (2019)
Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Alexander Levis
Rehashing Kernel Evaluation in High Dimensions. ICML (2019)
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Equivariant Transformer Networks. CoRR (2019)
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) (2019)
Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis
CrossTrainer: Practical Domain Adaptation with Loss Reweighting. DEEM@SIGMOD (2019)
Daniel Kang, Peter Bailis, Matei Zaharia
Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. CIDR (2019)
Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia
LIT: Learned Intermediate Representation Training for Model Compression. ICML (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)
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 (2018)
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 (2018)
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) (2018)
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) (2018)
Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
Sketching Linear Classifiers over Data Streams. SIGMOD Conference (2018)
Albert Kwon, James R. Wilcox, Peter Bailis
Research for practice: private online communication; highlights in systems verification. Commun. ACM 61 (2) (2018)
Shoumik Palkar, Firas Abuzaid, Peter Bailis, Matei Zaharia
Filter Before You Parse: Faster Analytics on Raw Data with Sparser. Proc. VLDB Endow. 11 (11) (2018)
Alexander Ratner, Christopher Ré, Peter Bailis
Research for practice: knowledge base construction in the machine-learning era. Commun. ACM 61 (11) (2018)
Daniel Kang, Peter Bailis, Matei Zaharia
BlazeIt: Fast Exploratory Video Queries using Neural Networks. CoRR (2018)
Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. CoRR (2018)
Malte Schwarzkopf, Peter Bailis
Research for practice: cluster scheduling for datacenters. Commun. ACM 61 (5) (2018)
Deepak Vasisht, Peter Bailis
Research for practice: toward a network of connected things. Commun. ACM 61 (7) (2018)
Daniel Crankshaw, Joseph Gonzalez, Peter Bailis
Research for practice: prediction-serving systems. Commun. ACM 61 (8) (2018)
Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia
LIT: Block-wise Intermediate Representation Training for Model Compression. CoRR (2018)
Gustavo Alonso, Peter Bailis
Research for practice: FPGAs in datacenters. Commun. ACM 61 (9) (2018)
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) (2018)
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) (2018)
Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri
MacroBase: Prioritizing Attention in Fast Data. SIGMOD Conference (2017)
Tawanna Dillahunt, Stefanie Mueller, Patrick Baudisch, Peter Bailis
Research for Practice: Technology for UnderservedCommunities; Personal Fabrication. ACM Queue 15 (2) (2017)
Todd Warszawski, Peter Bailis
ACIDRain: Concurrency-Related Attacks on Database-Backed Web Applications. SIGMOD Conference (2017)
Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia
Optimizing Deep CNN-Based Queries over Video Streams at Scale. CoRR (2017)
Kexin Rong, Peter Bailis
ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization. CoRR (2017)
Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia
SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations. CoRR (2017)
Peter Bailis, Arvind Narayanan, Andrew Miller, Song Han
Research for practice: cryptocurrencies, blockchains, and smart contracts; hardware for deep learning. Commun. ACM 60 (5) (2017)
Sahaana Suri, Peter Bailis
DROP: Dimensionality Reduction Optimization for Time Series. CoRR (2017)
Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri
Prioritizing Attention in Analytic Monitoring. CIDR (2017)
Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
Finding Heavily-Weighted Features in Data Streams. CoRR (2017)
Peter Bailis, Tawanna Dillahunt, Stefanie Mueller, Patrick Baudisch
Research for practice: technology for underserved communities; personal fabrication. Commun. ACM 60 (10) (2017)
Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri
Demonstration: MacroBase, A Fast Data Analysis Engine. SIGMOD Conference (2017)
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) (2017)
Edward Gan, Peter Bailis
Scalable Kernel Density Classification via Threshold-Based Pruning. SIGMOD Conference (2017)
Peter Bailis, Jean Yang, Vijay Janapa Reddi, Yuhao Zhu
Research for practice: web security and mobile web computing. Commun. ACM 60 (1) (2017)
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
There and Back Again: A General Approach to Learning Sparse Models. CoRR (2017)
Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia
Infrastructure for Usable Machine Learning: The Stanford DAWN Project. CoRR (2017)
Peter Bailis, Peter Alvaro, Sumit Gulwani
Research for practice: tracing and debugging distributed systems; programming by examples. Commun. ACM 60 (7) (2017)
Peter Bailis, Irene Zhang, Fadel Adib
Research for practice: distributed transactions and networks as physical sensors. Commun. ACM 60 (3) (2017)
Peter Alvaro, Sumit Gulwani, Peter Bailis
Research for Practice: Tracing and Debugging Distributed Systems; Programming by Examples. ACM Queue 15 (1) (2017)
John Regehr, Peter Bailis
Research for practice: vigorous public debates in academic computer science. Commun. ACM 60 (12) (2017)
Kexin Rong, Peter Bailis
ASAP: Prioritizing Attention via Time Series Smoothing. Proc. VLDB Endow. 10 (11) (2017)
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) (2016)
Peter Bailis, Arvind Narayanan, Andrew Miller, Song Han
Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning. ACM Queue 14 (6) (2016)
Peter Bailis, Joy Arulraj, Andrew Pavlo
Research for practice: distributed consensus and implications of NVM on database management systems. Commun. ACM 59 (11) (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Peter Bailis