Positional Communication and Private Information in Honeybee Foraging Models. - Peter Bailis; Radhika Nagpal; Justin Werfel (ANTS Conference, 2010)

Dimetrodon: processor-level preventive thermal management via idle cycle injection. - Peter Bailis; Vijay Janapa Reddi; Sanjay Gandhi; David M. Brooks; Margo I. Seltzer (DAC, 2011)

Programming micro-aerial vehicle swarms with karma. - Karthik Dantu; Bryan Kate; Jason Waterman; Peter Bailis; Matt Welsh (SenSys, 2011)

The potential dangers of causal consistency and an explicit solution. - Peter Bailis; Alan D. Fekete; Ali Ghodsi 002; Joseph M. Hellerstein; Ion Stoica (SoCC, 2012)

Probabilistically Bounded Staleness for Practical Partial Quorums. - Peter Bailis; Shivaram Venkataraman; Michael J. Franklin; Joseph M. Hellerstein; Ion Stoica (Proc. VLDB Endow., 2012)

TinyToCS Volume 1 Chairs' Note. - Peter Bailis; Justine Sherry (Adv. Math. Commun., 2012)

HAT, not CAP: Highly Available Transactions for Everybody. - Peter Bailis (CIDR, 2013)

Consistency without borders. - Peter Alvaro; Peter Bailis; Neil Conway; Joseph M. Hellerstein (SoCC, 2013)

HAT, Not CAP: Towards Highly Available Transactions. - Peter Bailis; Alan D. Fekete; Ali Ghodsi 002; Joseph M. Hellerstein; Ion Stoica (HotOS, 2013)

Bolt-on causal consistency. - Peter Bailis; Ali Ghodsi 002; Joseph M. Hellerstein; Ion Stoica (SIGMOD Conference, 2013)

PBS at work: advancing data management with consistency metrics. - Peter Bailis; Shivaram Venkataraman; Michael J. Franklin; Joseph M. Hellerstein; Ion Stoica (SIGMOD Conference, 2013)

Eventual consistency today: limitations, extensions, and beyond. - Peter Bailis; Ali Ghodsi 002 (Commun. ACM, 2013)

HAT, not CAP: Highly Available Transactions - Peter Bailis; Alan D. Fekete; Ali Ghodsi 002; Joseph M. Hellerstein; Ion Stoica (CoRR, 2013)

Highly Available Transactions: Virtues and Limitations. - Peter Bailis; Aaron Davidson; Alan D. Fekete; Ali Ghodsi 002; Joseph M. Hellerstein; Ion Stoica (Proc. VLDB Endow., 2013)

The network is reliable. - Peter Bailis; Kyle Kingsbury (Commun. ACM, 2014)

Quantifying eventual consistency with PBS. - Peter Bailis; Shivaram Venkataraman; Michael J. Franklin; Joseph M. Hellerstein; Ion Stoica (Commun. ACM, 2014)

Coordination-Avoiding Database Systems. - Peter Bailis; Alan D. Fekete; Michael J. Franklin; Ali Ghodsi 002; Joseph M. Hellerstein; Ion Stoica (CoRR, 2014)

Coordination Avoidance in Database Systems. - Peter Bailis; Alan D. Fekete; Michael J. Franklin; Ali Ghodsi 002; Joseph M. Hellerstein; Ion Stoica (Proc. VLDB Endow., 2014)

The Case for Invariant-Based Concurrency Control. - Peter Bailis (CIDR, 2015)

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. - Daniel Crankshaw; Peter Bailis; Joseph E. Gonzalez; Haoyuan Li 001; Zhao Zhang 007; Michael J. Franklin; Ali Ghodsi 002; Michael I. Jordan (CIDR, 2015)

Feral Concurrency Control: An Empirical Investigation of Modern Application Integrity. - Peter Bailis; Alan D. Fekete; Michael J. Franklin; Ali Ghodsi 002; Joseph M. Hellerstein; Ion Stoica (SIGMOD Conference, 2015)

Asynchronous Complex Analytics in a Distributed Dataflow Architecture. - Joseph E. Gonzalez; Peter Bailis; Michael I. Jordan; Michael J. Franklin; Joseph M. Hellerstein; Ali Ghodsi 002; Ion Stoica (CoRR, 2015)

Coordination Avoidance in Distributed Databases. - Peter Bailis (2015)

Research for practice: distributed consensus and implications of NVM on database management systems. - Peter Bailis; Joy Arulraj; Andrew Pavlo (Commun. ACM, 2016)

MacroBase: Analytic Monitoring for the Internet of Things. - Peter Bailis; Deepak Narayanan; Samuel Madden 001 (CoRR, 2016)

Research for Practice: Distributed Consensus and Implications of NVM on Database Management Systems. - Peter Bailis; Camille Fournier; Joy Arulraj; Andy Pavlo (ACM Queue, 2016)

Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning. - Peter Bailis; Arvind Narayanan; Andrew Miller 001; Song Han 003 (ACM Queue, 2016)

Introducing Research for Practice. - Peter Bailis; Justine Sherry; Simon Peter 001 (ACM Queue, 2016)

Research for Practice: Distributed Transactions and Networks as Physical Sensors. - Peter Bailis; Irene Zhang; Fadel Adib (ACM Queue, 2016)

Research for Practice: Web Security and Mobile Web Computing. - Jean Yang 001; Vijay Janapa Reddi; Yuhao Zhu 001; Peter Bailis (ACM Queue, 2016)

Scalable Atomic Visibility with RAMP Transactions. - Peter Bailis; Alan D. Fekete; Ali Ghodsi 002; Joseph M. Hellerstein; Ion Stoica (ACM Trans. Database Syst., 2016)

Prioritizing Attention in Analytic Monitoring. - Peter Bailis; Edward Gan; Kexin Rong 001; Sahaana Suri (CIDR, 2017)

MacroBase: Prioritizing Attention in Fast Data. - Peter Bailis; Edward Gan; Samuel Madden 001; Deepak Narayanan; Kexin Rong 001; Sahaana Suri (SIGMOD Conference, 2017)

Demonstration: MacroBase, A Fast Data Analysis Engine. - Peter Bailis; Edward Gan; Kexin Rong 001; Sahaana Suri (SIGMOD Conference, 2017)

Scalable Kernel Density Classification via Threshold-Based Pruning. - Edward Gan; Peter Bailis (SIGMOD Conference, 2017)

ACIDRain: Concurrency-Related Attacks on Database-Backed Web Applications. - Todd Warszawski; Peter Bailis (SIGMOD Conference, 2017)

Research for practice: technology for underserved communities; personal fabrication. - Peter Bailis; Tawanna Dillahunt; Stefanie Mueller 001; Patrick Baudisch (Commun. ACM, 2017)

Research for practice: vigorous public debates in academic computer science. - John Regehr; Peter Bailis (Commun. ACM, 2017)

SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations. - Firas Abuzaid; Geet Sethi; Peter Bailis; Matei Zaharia (CoRR, 2017)

Infrastructure for Usable Machine Learning: The Stanford DAWN Project. - Peter Bailis; Kunle Olukotun; Christopher Ré; Matei Zaharia (CoRR, 2017)

Optimizing Deep CNN-Based Queries over Video Streams at Scale. - Daniel Kang; John Emmons; Firas Abuzaid; Peter Bailis; Matei Zaharia (CoRR, 2017)

ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization. - Kexin Rong 001; Peter Bailis (CoRR, 2017)

There and Back Again: A General Approach to Learning Sparse Models. - Vatsal Sharan; Kai Sheng Tai; Peter Bailis; Gregory Valiant (CoRR, 2017)

DROP: Dimensionality Reduction Optimization for Time Series. - Sahaana Suri; Peter Bailis (CoRR, 2017)

Finding Heavily-Weighted Features in Data Streams. - Kai Sheng Tai; Vatsal Sharan; Peter Bailis; Gregory Valiant (CoRR, 2017)

NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale. - Daniel Kang; John Emmons; Firas Abuzaid; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2017)

ASAP: Prioritizing Attention via Time Series Smoothing. - Kexin Rong 001; Peter Bailis (Proc. VLDB Endow., 2017)

Research for Practice: Tracing and Debugging Distributed Systems; Programming by Examples. - Peter Alvaro; Sumit Gulwani; Peter Bailis (ACM Queue, 2017)

Research for Practice: Technology for UnderservedCommunities; Personal Fabrication. - Tawanna Dillahunt; Stefanie Mueller 001; Patrick Baudisch; Peter Bailis (ACM Queue, 2017)

Sketching Linear Classifiers over Data Streams. - Kai Sheng Tai; Vatsal Sharan; Peter Bailis; Gregory Valiant (SIGMOD Conference, 2018)

Research for practice: FPGAs in datacenters. - Gustavo Alonso; Peter Bailis (Commun. ACM, 2018)

Research for practice: prediction-serving systems. - Daniel Crankshaw; Joseph Gonzalez 001; Peter Bailis (Commun. ACM, 2018)

Research for practice: private online communication; highlights in systems verification. - Albert Kwon; James R. Wilcox; Peter Bailis (Commun. ACM, 2018)

Research for practice: knowledge base construction in the machine-learning era. - Alexander Ratner; Christopher Ré; Peter Bailis (Commun. ACM, 2018)

Research for practice: cluster scheduling for datacenters. - Malte Schwarzkopf; Peter Bailis (Commun. ACM, 2018)

Research for practice: toward a network of connected things. - Deepak Vasisht; Peter Bailis (Commun. ACM, 2018)

BlazeIt: Fast Exploratory Video Queries using Neural Networks. - Daniel Kang; Peter Bailis; Matei Zaharia (CoRR, 2018)

LIT: Block-wise Intermediate Representation Training for Model Compression. - Animesh Koratana; Daniel Kang; Peter Bailis; Matei Zaharia (CoRR, 2018)

DIFF: A Relational Interface for Large-Scale Data Explanation. - Firas Abuzaid; Peter Kraft; Sahaana Suri; Edward Gan; Eric Xu; Atul Shenoy; Asvin Anathanaraya; John Sheu; Erik Meijer 001; Xi Wu 001; Jeffrey F. Naughton; Peter Bailis; et al. (Proc. VLDB Endow., 2018)

Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. - Edward Gan; Jialin Ding 001; Kai Sheng Tai; Vatsal Sharan; Peter Bailis (Proc. VLDB Endow., 2018)

Filter Before You Parse: Faster Analytics on Raw Data with Sparser. - Shoumik Palkar; Firas Abuzaid; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2018)

Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science. - Kexin Rong 001; Clara E. Yoon; Karianne J. Bergen; Hashem Elezabi; Peter Bailis; Philip Alexander Levis; Gregory C. Beroza (Proc. VLDB Endow., 2018)

MacroBase: Prioritizing Attention in Fast Data. - Firas Abuzaid; Peter Bailis; Jialin Ding 001; Edward Gan; Samuel Madden 001; Deepak Narayanan; Kexin Rong 001; Sahaana Suri (ACM Trans. Database Syst., 2018)

Multi-datacenter Consistency Properties. - Peter Bailis (Encyclopedia of Database Systems (2nd ed.), 2018)

Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. - Daniel Kang; Peter Bailis; Matei Zaharia (CIDR, 2019)

To Index or Not to Index: Optimizing Exact Maximum Inner Product Search. - Firas Abuzaid; Geet Sethi; Peter Bailis; Matei Zaharia (ICDE, 2019)

LIT: Learned Intermediate Representation Training for Model Compression. - Animesh Koratana; Daniel Kang; Peter Bailis; Matei Zaharia (ICML, 2019)

Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. - Vatsal Sharan; Kai Sheng Tai; Peter Bailis; Gregory Valiant (ICML, 2019)

Rehashing Kernel Evaluation in High Dimensions. - Paris Siminelakis; Kexin Rong 001; Peter Bailis; Moses Charikar; Philip Alexander Levis (ICML, 2019)

Equivariant Transformer Networks. - Kai Sheng Tai; Peter Bailis; Gregory Valiant (ICML, 2019)

CrossTrainer: Practical Domain Adaptation with Loss Reweighting. - Justin Chen; Edward Gan; Kexin Rong 001; Sahaana Suri; Peter Bailis (DEEM@SIGMOD, 2019)

DROP: A Workload-Aware Optimizer for Dimensionality Reduction. - Sahaana Suri; Peter Bailis (DEEM@SIGMOD, 2019)

SysML: The New Frontier of Machine Learning Systems. - 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; et al. (CoRR, 2019)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Cody Coleman; Greg Diamos; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (CoRR, 2019)

BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. - Daniel Kang; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2019)

The Seattle Report on Database Research. - 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; et al. (SIGMOD Rec., 2019)

Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. - Cody Coleman; Daniel Kang; Deepak Narayanan; Luigi Nardi; Tian Zhao 001; Jian Zhang 049; Peter Bailis; Kunle Olukotun; Christopher Ré; Matei Zaharia (ACM SIGOPS Oper. Syst. Rev., 2019)

Selection via Proxy: Efficient Data Selection for Deep Learning. - Cody Coleman; Christopher Yeh; Stephen Mussmann; Baharan Mirzasoleiman; Peter Bailis; Percy Liang; Jure Leskovec; Matei Zaharia (ICLR, 2020)

Model Assertions for Monitoring and Improving ML Models. - Daniel Kang; Deepti Raghavan; Peter Bailis; Matei Zaharia (MLSys, 2020)

Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. - Peter Kraft; Daniel Kang; Deepak Narayanan; Shoumik Palkar; Peter Bailis; Matei Zaharia (MLSys, 2020)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Gregory F. Diamos; Cody Coleman; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (MLSys, 2020)

Storyboard: Optimizing Precomputed Summaries for Aggregation. - Edward Gan; Peter Bailis; Moses Charikar (CoRR, 2020)

Chromatic Learning for Sparse Datasets. - Vladimir Feinberg; Peter Bailis (CoRR, 2020)

Similarity Search for Efficient Active Learning and Search of Rare Concepts. - Cody Coleman; Edward Chou; Sean Culatana; Peter Bailis; Alexander C. Berg; Roshan Sumbaly; Matei Zaharia; I. Zeki Yalniz (CoRR, 2020)

Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. - Daniel Kang; John Guibas; Peter Bailis; Tatsunori Hashimoto; Matei Zaharia (CoRR, 2020)

Winds from Seattle: Database Research Directions. - Peter Bailis; Magda Balazinska; Xin Luna Dong; Juliana Freire; Raghu Ramakrishnan 001; Michael Stonebraker; Joseph M. Hellerstein (Proc. VLDB Endow., 2020)

CoopStore: Optimizing Precomputed Summaries for Aggregation. - Edward Gan; Peter Bailis; Moses Charikar (Proc. VLDB Endow., 2020)

Approximate Selection with Guarantees using Proxies. - Daniel Kang; Edward Gan; Peter Bailis; Tatsunori Hashimoto; Matei Zaharia (Proc. VLDB Endow., 2020)

Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. - Daniel Kang; Ankit Mathur; Teja Veeramacheneni; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2020)

A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. - Peter Kraft; Daniel Kang; Deepak Narayanan; Shoumik Palkar; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2020)

Approximate Partition Selection for Big-Data Workloads using Summary Statistics. - Kexin Rong 001; Yao Lu; Peter Bailis; Srikanth Kandula; Philip Alexander Levis (Proc. VLDB Endow., 2020)

Leveraging Organizational Resources to Adapt Models to New Data Modalities. - Sahaana Suri; Abishek Sethi; Girija Narlikar; Neslihan Bulut; Raghuveer Chanda; Sugato Basu; Pradyumna Narayana; Peter Bailis; Christopher Ré; Yemao Zeng (Proc. VLDB Endow., 2020)

Challenges and Opportunities for Autonomous Vehicle Query Systems. - Fiodar Kazhamiaka; Matei Zaharia; Peter Bailis (CIDR, 2021)

Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. - Kai Sheng Tai; Peter Bailis; Gregory Valiant (ICML, 2021)

Contracting Wide-area Network Topologies to Solve Flow Problems Quickly. - Firas Abuzaid; Srikanth Kandula; Behnaz Arzani; Ishai Menache; Matei Zaharia; Peter Bailis (NSDI, 2021)

Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates. - Daniel Kang; John Guibas; Peter Bailis; Tatsunori Hashimoto; Yi Sun 010; Matei Zaharia (CoRR, 2021)

RAMP-TAO: Layering Atomic Transactions on Facebook's Online TAO Data Store. - Audrey Cheng; Xiao Shi; Lu Pan; Anthony Simpson; Neil Wheaton; Shilpa Lawande; Nathan Bronson; Peter Bailis; Natacha Crooks; Ion Stoica (Proc. VLDB Endow., 2021)

Accelerating Approximate Aggregation Queries with Expensive Predicates. - Daniel Kang; John Guibas; Peter Bailis; Tatsunori Hashimoto; Yi Sun 010; Matei Zaharia (Proc. VLDB Endow., 2021)

DIFF: a relational interface for large-scale data explanation. - Firas Abuzaid; Peter Kraft; Sahaana Suri; Edward Gan; Eric Xu; Atul Shenoy; Asvin Ananthanarayan; John Sheu; Erik Meijer 001; Xi Wu 001; Jeffrey F. Naughton; Peter Bailis; et al. (VLDB J., 2021)

Similarity Search for Efficient Active Learning and Search of Rare Concepts. - Cody Coleman; Edward Chou; Julian Katz-Samuels; Sean Culatana; Peter Bailis; Alexander C. Berg; Robert D. Nowak; Roshan Sumbaly; Matei Zaharia; I. Zeki Yalniz (AAAI, 2022)

VIVA: An End-to-End System for Interactive Video Analytics. - Daniel Kang; Francisco Romero; Peter D. Bailis; Christos Kozyrakis; Matei Zaharia (CIDR, 2022)

Data-Parallel Actors: A Programming Model for Scalable Query Serving Systems. - Peter Kraft; Fiodar Kazhamiaka; Peter Bailis; Matei Zaharia (NSDI, 2022)

Finding Label and Model Errors in Perception Data With Learned Observation Assertions. - Daniel Kang; Nikos Aréchiga; Sudeep Pillai; Peter D. Bailis; Matei Zaharia (SIGMOD Conference, 2022)

TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data. - Daniel Kang; John Guibas; Peter D. Bailis; Tatsunori Hashimoto; Matei Zaharia (SIGMOD Conference, 2022)

Apiary: A DBMS-Backed Transactional Function-as-a-Service Framework. - Peter Kraft; Qian Li 027; Kostis Kaffes; Athinagoras Skiadopoulos; Deeptaanshu Kumar; Danny Cho; Jason Li; Robert Redmond; Nathan W. Weckwerth; Brian S. Xia; Peter Bailis; Michael J. Cafarella; et al. (CoRR, 2022)

Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations. - Nirvik Baruah; Peter Kraft; Fiodar Kazhamiaka; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2022)

TAOBench: An End-to-End Benchmark for Social Networking Workloads. - Audrey Cheng; Xiao Shi; Aaron N. Kabcenell; Shilpa Lawande; Hamza Qadeer; Jason Chan; Harrison Tin; Ryan Zhao; Peter Bailis; Mahesh Balakrishnan 001; Nathan Bronson; Natacha Crooks; et al. (Proc. VLDB Endow., 2022)

Epoxy: ACID Transactions Across Diverse Data Stores. - Peter Kraft; Qian Li 027; Xinjing Zhou; Peter Bailis; Michael Stonebraker; Xiangyao Yu; Matei Zaharia (Proc. VLDB Endow., 2023)

Break the Sequential Dependency of LLM Inference Using Lookahead Decoding. - Yichao Fu; Peter Bailis; Ion Stoica; Hao Zhang 025 (ICML, 2024)

Online Speculative Decoding. - Xiaoxuan Liu; Lanxiang Hu; Peter Bailis; Alvin Cheung; Zhijie Deng; Ion Stoica; Hao Zhang 025 (ICML, 2024)

Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems. - Lingjiao Chen; Jared Quincy Davis; Boris Hanin; Peter Bailis; Ion Stoica; Matei A. Zaharia; James Y. Zou (NeurIPS, 2024)

Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems. - Lingjiao Chen; Jared Quincy Davis; Boris Hanin; Peter Bailis; Ion Stoica; Matei Zaharia; James Zou 001 (CoRR, 2024)

Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design. - Jared Quincy Davis; Boris Hanin; Lingjiao Chen; Peter Bailis; Ion Stoica; Matei Zaharia (CoRR, 2024)

Towards Optimal Transaction Scheduling. - Audrey Cheng; Aaron N. Kabcenell; Jason Chan; Xiao Shi; Peter D. Bailis; Natacha Crooks; Ion Stoica (Proc. VLDB Endow., 2024)

Optimizing Model Selection for Compound AI Systems. - Lingjiao Chen; Jared Quincy Davis; Boris Hanin; Peter Bailis; Matei Zaharia; James Zou 001; Ion Stoica (CoRR, 2025)

Fair Transaction Processing For Multi-Tenant Databases. - Audrey Cheng; Xiao Shi; Aaron N. Kabcenell; Jolene Huey; Peter Bailis; Natacha Crooks; Ion Stoica (Proc. VLDB Endow., 2025)