Peter Bailis - RankMe

140 papers A* 12A 7Misc 3Journal 98Unranked 17

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Fair Transaction Processing For Multi-Tenant Databases. Proc. VLDB Endow. Audrey Cheng, Xiao Shi, Aaron N. Kabcenell, Jolene Huey, Peter Bailis, Natacha Crooks, Ion Stoica
2025	J	`jnl`	Optimizing Model Selection for Compound AI Systems. CoRR Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Matei Zaharia, James Zou, Ion Stoica
2024	J	`jnl`	Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems. CoRR Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei Zaharia, James Zou
2024	A*	`conf`	Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems. NeurIPS Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei A. Zaharia, James Y. Zou
2024	A*	`conf`	Break the Sequential Dependency of LLM Inference Using Lookahead Decoding. ICML Yichao Fu, Peter Bailis, Ion Stoica, Hao Zhang
2024	J	`jnl`	Break the Sequential Dependency of LLM Inference Using Lookahead Decoding. CoRR Yichao Fu, Peter Bailis, Ion Stoica, Hao Zhang
2024	J	`jnl`	Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design. CoRR Jared Quincy Davis, Boris Hanin, Lingjiao Chen, Peter Bailis, Ion Stoica, Matei Zaharia
2024	A*	`conf`	Online Speculative Decoding. ICML Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Alvin Cheung, Zhijie Deng, Ion Stoica, Hao Zhang
2023	J	`jnl`	Epoxy: ACID Transactions Across Diverse Data Stores. Proc. VLDB Endow. Peter Kraft, Qian Li, Xinjing Zhou, Peter Bailis, Michael Stonebraker, Xiangyao Yu, Matei Zaharia
2023	J	`jnl`	Online Speculative Decoding. CoRR Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng, Alvin Cheung, Hao Zhang
2022	J	`jnl`	Apiary: A DBMS-Backed Transactional Function-as-a-Service Framework. CoRR 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
2022	Misc	`conf`	Data-Parallel Actors: A Programming Model for Scalable Query Serving Systems. NSDI Peter Kraft, Fiodar Kazhamiaka, Peter Bailis, Matei Zaharia
2022	J	`jnl`	Finding Label and Model Errors in Perception Data With Learned Observation Assertions. CoRR Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter Bailis, Matei Zaharia
2022	J	`jnl`	Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations. Proc. VLDB Endow. Nirvik Baruah, Peter Kraft, Fiodar Kazhamiaka, Peter Bailis, Matei Zaharia
2022	A*	`conf`	Similarity Search for Efficient Active Learning and Search of Rare Concepts. AAAI Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter Bailis, Alexander C. Berg, Robert D. Nowak, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz
2022	J	`jnl`	TAOBench: An End-to-End Benchmark for Social Networking Workloads. Proc. VLDB Endow. 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
2022	J	`jnl`	The Seattle report on database research. Commun. ACM Daniel Abadi, Anastasia Ailamaki, David G. Andersen, Peter Bailis, Magdalena Balazinska, Philip A. Bernstein, Peter 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
2021	J	`jnl`	Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia
2021	J	`jnl`	Accelerating Approximate Aggregation Queries with Expensive Predicates. Proc. VLDB Endow. Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia
2021	A	`conf`	Challenges and Opportunities for Autonomous Vehicle Query Systems. CIDR Fiodar Kazhamiaka, Matei Zaharia, Peter Bailis
2021	Misc	`conf`	Contracting Wide-area Network Topologies to Solve Flow Problems Quickly. NSDI Firas Abuzaid, Srikanth Kandula, Behnaz Arzani, Ishai Menache, Matei Zaharia, Peter Bailis
2021	J	`jnl`	DIFF: a relational interface for large-scale data explanation. VLDB J. 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
2021	J	`jnl`	Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates. CoRR Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia
2021	J	`jnl`	RAMP-TAO: Layering Atomic Transactions on Facebook's Online TAO Data Store. Proc. VLDB Endow. Audrey Cheng, Xiao Shi, Lu Pan, Anthony Simpson, Neil Wheaton, Shilpa Lawande, Nathan Bronson, Peter Bailis, Natacha Crooks, Ion Stoica
2021	A*	`conf`	Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. ICML Kai Sheng Tai, Peter Bailis, Gregory Valiant
2021	J	`jnl`	Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. CoRR Kai Sheng Tai, Peter Bailis, Gregory Valiant
2020	J	`jnl`	A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. Proc. VLDB Endow. Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia
2020	J	`jnl`	Approximate Partition Selection for Big-Data Workloads using Summary Statistics. CoRR Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Alexander Levis
2020	J	`jnl`	Approximate Partition Selection for Big-Data Workloads using Summary Statistics. Proc. VLDB Endow. Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Alexander Levis
2020	J	`jnl`	Approximate Selection with Guarantees using Proxies. CoRR Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
2020	J	`jnl`	Approximate Selection with Guarantees using Proxies. Proc. VLDB Endow. Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
2020	J	`jnl`	Chromatic Learning for Sparse Datasets. CoRR Vladimir Feinberg, Peter Bailis
2020	J	`jnl`	CoopStore: Optimizing Precomputed Summaries for Aggregation. Proc. VLDB Endow. Edward Gan, Peter Bailis, Moses Charikar
2020	J	`jnl`	Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. CoRR Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia
2020	J	`jnl`	Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. Proc. VLDB Endow. Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia
2020	J	`jnl`	Leveraging Organizational Resources to Adapt Models to New Data Modalities. CoRR Sahaana Suri, Raghuveer Chanda, Neslihan Bulut, Pradyumna Narayana, Yemao Zeng, Peter Bailis, Sugato Basu, Girija Narlikar, Christopher Ré, Abishek Sethi
2020	J	`jnl`	Leveraging Organizational Resources to Adapt Models to New Data Modalities. Proc. VLDB Endow. Sahaana Suri, Abishek Sethi, Girija Narlikar, Neslihan Bulut, Raghuveer Chanda, Sugato Basu, Pradyumna Narayana, Peter Bailis, Christopher Ré, Yemao Zeng
2020	—	`conf`	MLPerf Training Benchmark. MLSys 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
2020	—	`conf`	Model Assertions for Monitoring and Improving ML Models. MLSys Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia
2020	J	`jnl`	Model Assertions for Monitoring and Improving ML Models. CoRR Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia
2020	A*	`conf`	Selection via Proxy: Efficient Data Selection for Deep Learning. ICLR Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia
2020	J	`jnl`	Similarity Search for Efficient Active Learning and Search of Rare Concepts. CoRR Cody Coleman, Edward Chou, Sean Culatana, Peter Bailis, Alexander C. Berg, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz
2020	J	`jnl`	Storyboard: Optimizing Precomputed Summaries for Aggregation. CoRR Edward Gan, Peter Bailis, Moses Charikar
2020	J	`jnl`	Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. CoRR Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
2020	—	`conf`	Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. MLSys Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia
2020	J	`jnl`	Winds from Seattle: Database Research Directions. Proc. VLDB Endow. Peter Bailis, Magda Balazinska, Xin Luna Dong, Juliana Freire, Raghu Ramakrishnan, Michael Stonebraker, Joseph M. Hellerstein
2019	J	`jnl`	Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. ACM SIGOPS Oper. Syst. Rev. Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia
2019	J	`jnl`	BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. Proc. VLDB Endow. Daniel Kang, Peter Bailis, Matei Zaharia
2019	A	`conf`	Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. CIDR Daniel Kang, Peter Bailis, Matei Zaharia
2019	A*	`conf`	Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. ICML Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
2019	—	`conf`	CrossTrainer: Practical Domain Adaptation with Loss Reweighting. DEEM@SIGMOD Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis
2019	J	`jnl`	CrossTrainer: Practical Domain Adaptation with Loss Reweighting. CoRR Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis
2019	—	`conf`	DROP: A Workload-Aware Optimizer for Dimensionality Reduction. DEEM@SIGMOD Sahaana Suri, Peter Bailis
2019	A*	`conf`	Equivariant Transformer Networks. ICML Kai Sheng Tai, Peter Bailis, Gregory Valiant
2019	J	`jnl`	Equivariant Transformer Networks. CoRR Kai Sheng Tai, Peter Bailis, Gregory Valiant
2019	A*	`conf`	LIT: Learned Intermediate Representation Training for Model Compression. ICML Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia
2019	J	`jnl`	MLPerf Training Benchmark. CoRR 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
2019	A*	`conf`	Rehashing Kernel Evaluation in High Dimensions. ICML Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Alexander Levis
2019	J	`jnl`	Selection Via Proxy: Efficient Data Selection For Deep Learning. CoRR Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR 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 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
2019	J	`jnl`	The Seattle Report on Database Research. SIGMOD Rec. Daniel Abadi, Anastasia Ailamaki, David G. Andersen, Peter Bailis, Magdalena Balazinska, Philip A. Bernstein, Peter 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
2019	A*	`conf`	To Index or Not to Index: Optimizing Exact Maximum Inner Product Search. ICDE Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia
2019	J	`jnl`	Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. CoRR Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia
2018	J	`jnl`	Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. CoRR Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia
2018	J	`jnl`	BlazeIt: Fast Exploratory Video Queries using Neural Networks. CoRR Daniel Kang, Peter Bailis, Matei Zaharia
2018	J	`jnl`	DIFF: A Relational Interface for Large-Scale Data Explanation. Proc. VLDB Endow. 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
2018	J	`jnl`	Filter Before You Parse: Faster Analytics on Raw Data with Sparser. Proc. VLDB Endow. Shoumik Palkar, Firas Abuzaid, Peter Bailis, Matei Zaharia
2018	J	`jnl`	LIT: Block-wise Intermediate Representation Training for Model Compression. CoRR Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia
2018	J	`jnl`	Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science. CoRR Kexin Rong, Clara E. Yoon, Karianne J. Bergen, Hashem Elezabi, Peter Bailis, Philip Alexander Levis, Gregory C. Beroza
2018	J	`jnl`	Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science. Proc. VLDB Endow. Kexin Rong, Clara E. Yoon, Karianne J. Bergen, Hashem Elezabi, Peter Bailis, Philip Alexander Levis, Gregory C. Beroza
2018	J	`jnl`	MacroBase: Prioritizing Attention in Fast Data. ACM Trans. Database Syst. Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri
2018	J	`jnl`	Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. CoRR Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis
2018	J	`jnl`	Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. Proc. VLDB Endow. Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis
2018	—	`ch.`	Multi-datacenter Consistency Properties. Encyclopedia of Database Systems (2nd ed.) Peter Bailis
2018	J	`jnl`	Research for practice: FPGAs in datacenters. Commun. ACM Gustavo Alonso, Peter Bailis
2018	J	`jnl`	Research for practice: cluster scheduling for datacenters. Commun. ACM Malte Schwarzkopf, Peter Bailis
2018	J	`jnl`	Research for practice: knowledge base construction in the machine-learning era. Commun. ACM Alexander Ratner, Christopher Ré, Peter Bailis
2018	J	`jnl`	Research for practice: prediction-serving systems. Commun. ACM Daniel Crankshaw, Joseph Gonzalez, Peter Bailis
2018	J	`jnl`	Research for practice: private online communication; highlights in systems verification. Commun. ACM Albert Kwon, James R. Wilcox, Peter Bailis
2018	J	`jnl`	Research for practice: toward a network of connected things. Commun. ACM Deepak Vasisht, Peter Bailis
2018	—	`conf`	Sketching Linear Classifiers over Data Streams. SIGMOD Conference Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
2017	—	`conf`	ACIDRain: Concurrency-Related Attacks on Database-Backed Web Applications. SIGMOD Conference Todd Warszawski, Peter Bailis
2017	J	`jnl`	ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization. CoRR Kexin Rong, Peter Bailis
2017	J	`jnl`	ASAP: Prioritizing Attention via Time Series Smoothing. Proc. VLDB Endow. Kexin Rong, Peter Bailis
2017	J	`jnl`	DROP: Dimensionality Reduction Optimization for Time Series. CoRR Sahaana Suri, Peter Bailis
2017	—	`conf`	Demonstration: MacroBase, A Fast Data Analysis Engine. SIGMOD Conference Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri
2017	J	`jnl`	Finding Heavily-Weighted Features in Data Streams. CoRR Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
2017	J	`jnl`	Infrastructure for Usable Machine Learning: The Stanford DAWN Project. CoRR Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia
2017	—	`conf`	MacroBase: Prioritizing Attention in Fast Data. SIGMOD Conference Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri
2017	J	`jnl`	NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale. Proc. VLDB Endow. Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia
2017	J	`jnl`	Optimizing Deep CNN-Based Queries over Video Streams at Scale. CoRR Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia
2017	A	`conf`	Prioritizing Attention in Analytic Monitoring. CIDR Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri
2017	J	`jnl`	Research for Practice: Technology for UnderservedCommunities; Personal Fabrication. ACM Queue Tawanna Dillahunt, Stefanie Mueller, Patrick Baudisch, Peter Bailis
2017	J	`jnl`	Research for Practice: Tracing and Debugging Distributed Systems; Programming by Examples. ACM Queue Peter Alvaro, Sumit Gulwani, Peter Bailis
2017	J	`jnl`	Research for practice: cryptocurrencies, blockchains, and smart contracts; hardware for deep learning. Commun. ACM Peter Bailis, Arvind Narayanan, Andrew Miller, Song Han
2017	J	`jnl`	Research for practice: distributed transactions and networks as physical sensors. Commun. ACM Peter Bailis, Irene Zhang, Fadel Adib
2017	J	`jnl`	Research for practice: technology for underserved communities; personal fabrication. Commun. ACM Peter Bailis, Tawanna Dillahunt, Stefanie Mueller, Patrick Baudisch
2017	J	`jnl`	Research for practice: tracing and debugging distributed systems; programming by examples. Commun. ACM Peter Bailis, Peter Alvaro, Sumit Gulwani
2017	J	`jnl`	Research for practice: vigorous public debates in academic computer science. Commun. ACM John Regehr, Peter Bailis
2017	J	`jnl`	Research for practice: web security and mobile web computing. Commun. ACM Peter Bailis, Jean Yang, Vijay Janapa Reddi, Yuhao Zhu
2017	—	`conf`	Scalable Kernel Density Classification via Threshold-Based Pruning. SIGMOD Conference Edward Gan, Peter Bailis
2017	J	`jnl`	SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations. CoRR Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia
2017	J	`jnl`	There and Back Again: A General Approach to Learning Sparse Models. CoRR Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
2016	J	`jnl`	Introducing Research for Practice. ACM Queue Peter Bailis, Justine Sherry, Simon Peter
2016	J	`jnl`	Introducing research for practice. Commun. ACM Peter Bailis, Simon Peter, Justine Sherry
2016	J	`jnl`	MacroBase: Analytic Monitoring for the Internet of Things. CoRR Peter Bailis, Deepak Narayanan, Samuel Madden
2016	J	`jnl`	Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning. ACM Queue Peter Bailis, Arvind Narayanan, Andrew Miller, Song Han
2016	J	`jnl`	Research for Practice: Distributed Consensus and Implications of NVM on Database Management Systems. ACM Queue Peter Bailis, Camille Fournier, Joy Arulraj, Andy Pavlo
2016	J	`jnl`	Research for Practice: Distributed Transactions and Networks as Physical Sensors. ACM Queue Peter Bailis, Irene Zhang, Fadel Adib
2016	J	`jnl`	Research for Practice: Web Security and Mobile Web Computing. ACM Queue Jean Yang, Vijay Janapa Reddi, Yuhao Zhu, Peter Bailis
2016	J	`jnl`	Research for practice: distributed consensus and implications of NVM on database management systems. Commun. ACM Peter Bailis, Joy Arulraj, Andrew Pavlo
2016	J	`jnl`	Scalable Atomic Visibility with RAMP Transactions. ACM Trans. Database Syst. Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2015	J	`jnl`	Asynchronous Complex Analytics in a Distributed Dataflow Architecture. CoRR Joseph E. Gonzalez, Peter Bailis, Michael I. Jordan, Michael J. Franklin, Joseph M. Hellerstein, Ali Ghodsi, Ion Stoica
2015	—	`—`	Coordination Avoidance in Distributed Databases. Peter Bailis
2015	—	`conf`	Feral Concurrency Control: An Empirical Investigation of Modern Application Integrity. SIGMOD Conference Peter Bailis, Alan D. Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2015	—	`book`	Readings in Database Systems, 5th Edition. Peter Bailis, Joseph M. Hellerstein, Michael Stonebraker
2015	A	`conf`	The Case for Invariant-Based Concurrency Control. CIDR Peter Bailis
2015	A	`conf`	The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CIDR Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan
2014	J	`jnl`	Coordination Avoidance in Database Systems. Proc. VLDB Endow. Peter Bailis, Alan D. Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2014	J	`jnl`	Coordination-Avoiding Database Systems. CoRR Peter Bailis, Alan D. Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2014	J	`jnl`	Quantifying eventual consistency with PBS. Commun. ACM Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2014	J	`jnl`	Quantifying eventual consistency with PBS. VLDB J. Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2014	—	`conf`	Scalable atomic visibility with RAMP transactions. SIGMOD Conference Peter Bailis, Alan D. Fekete, Joseph M. Hellerstein, Ali Ghodsi, Ion Stoica
2014	J	`jnl`	The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CoRR Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan
2014	J	`jnl`	The network is reliable. Commun. ACM Peter Bailis, Kyle Kingsbury
2013	—	`conf`	Bolt-on causal consistency. SIGMOD Conference Peter Bailis, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2013	—	`conf`	Consistency without borders. SoCC Peter Alvaro, Peter Bailis, Neil Conway, Joseph M. Hellerstein
2013	J	`jnl`	Eventual consistency today: limitations, extensions, and beyond. Commun. ACM Peter Bailis, Ali Ghodsi
2013	A	`conf`	HAT, Not CAP: Towards Highly Available Transactions. HotOS Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2013	J	`jnl`	HAT, not CAP: Highly Available Transactions CoRR Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2013	A	`conf`	HAT, not CAP: Highly Available Transactions for Everybody. CIDR Peter Bailis
2013	J	`jnl`	Highly Available Transactions: Virtues and Limitations. Proc. VLDB Endow. Peter Bailis, Aaron Davidson, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2013	—	`conf`	PBS at work: advancing data management with consistency metrics. SIGMOD Conference Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2012	J	`jnl`	Probabilistically Bounded Staleness for Practical Partial Quorums CoRR Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2012	J	`jnl`	Probabilistically Bounded Staleness for Practical Partial Quorums. Proc. VLDB Endow. Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2012	—	`conf`	The potential dangers of causal consistency and an explicit solution. SoCC Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2012	J	`jnl`	TinyToCS Volume 1 Chairs' Note. Tiny Trans. Comput. Sci. Peter Bailis, Justine Sherry
2011	A*	`conf`	Dimetrodon: processor-level preventive thermal management via idle cycle injection. DAC Peter Bailis, Vijay Janapa Reddi, Sanjay Gandhi, David M. Brooks, Margo I. Seltzer
2011	Misc	`conf`	Programming micro-aerial vehicle swarms with karma. SenSys Karthik Dantu, Bryan Kate, Jason Waterman, Peter Bailis, Matt Welsh
2010	—	`conf`	Positional Communication and Private Information in Honeybee Foraging Models. ANTS Conference Peter Bailis, Radhika Nagpal, Justin Werfel