Ryan Marcus

92 papers A* 2A 3B 2Misc 1Journal 61Unranked 22
YearRankTypeTitle / Venue / Authors
2026 J jnl
Adversarial Query Synthesis via Bayesian Optimization.
CoRR
Jeffrey Tao, Yimeng Zeng, Haydn Thomas Jones, Natalie Maus, Osbert Bastani, Jacob R. Gardner, Ryan Marcus
2026 J jnl
Piece of CAKE: Adaptive Execution Engines via Microsecond-Scale Learning.
CoRR
Zijie Zhao, Ryan Marcus
2026 A conf
Survivorship Bias in Industrial Database Workloads.
CIDR
Ryan Marcus, Jeffrey Tao, Peizhi Wu, Zijie Zhao
2025 J jnl
A Practical Theory of Generalization in Selectivity Learning.
Proc. VLDB Endow.
Peizhi Wu, Haoshu Xu, Ryan Marcus, Zack Ives
2025 J jnl
Adaptive Sharding in Untrusted Environments.
Proc. ACM Manag. Data
Bhavana Mehta, Nupur Baghel, Mohammad Javad Amiri, Boon Thau Loo, Ryan Marcus
2025 Misc conf
BFTBrain: Adaptive BFT Consensus with Reinforcement Learning.
NSDI
Chenyuan Wu, Haoyun Qin, Mohammad Javad Amiri, Boon Thau Loo, Dahlia Malkhi, Ryan Marcus
2025 J jnl
Data-Agnostic Cardinality Learning from Imperfect Workloads.
CoRR
Peizhi Wu, Rong Kang, Tieying Zhang, Jianjun Chen, Ryan Marcus, Zachary G. Ives
2025 J jnl
Data-Agnostic Cardinality Learning from Imperfect Workloads.
Proc. VLDB Endow.
Peizhi Wu, Rong Kang, Tieying Zhang, Jianjun Chen, Ryan Marcus, Zack Ives
2025 — conf
Eighth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM).
SIGMOD Conference Companion
Renata Borovica-Gajic, Manisha Luthra, Ryan Marcus, Rajesh Bordawekar, Oded Shmueli
2025 J jnl
Global Hash Tables Strike Back! An Analysis of Parallel GROUP BY Aggregation.
CoRR
Daniel Xue, Ryan Marcus
2025 J jnl
Global Hash Tables Strike Back! An Analysis of Parallel GROUP BY Aggregation.
Proc. VLDB Endow.
Daniel Xue, Ryan Marcus
2025 J jnl
Is it Bigger than a Breadbox: Efficient Cardinality Estimation for Real World Workloads.
CoRR
Zixuan Yi, Sami Abu-El-Haija, Yawen Wang, Teja Vemparala, Yannis Chronis, Yu Gan, Michael Burrows, Carsten Binnig, Bryan Perozzi, Ryan Marcus, Fatma Özcan
2025 J jnl
Large Scale Multi-Task Bayesian Optimization with Large Language Models.
CoRR
Yimeng Zeng, Natalie Maus, Haydn Thomas Jones, Jeffrey Tao, Fangping Wan, Marcelo Der Torossian Torres, Cesar de la Fuente-Nunez, Ryan Marcus, Osbert Bastani, Jacob R. Gardner
2025 J jnl
Learned Offline Query Planning via Bayesian Optimization.
CoRR
Jeffrey Tao, Natalie Maus, Haydn Thomas Jones, Yimeng Zeng, Jacob R. Gardner, Ryan Marcus
2025 J jnl
Learned Offline Query Planning via Bayesian Optimization.
Proc. ACM Manag. Data
Jeffrey Tao, Natalie Maus, Haydn Thomas Jones, Yimeng Zeng, Jacob R. Gardner, Ryan Marcus
2025 J jnl
Low Rank Learning for Offline Query Optimization.
CoRR
Zixuan Yi, Yao Tian, Zachary G. Ives, Ryan Marcus
2025 J jnl
Low Rank Learning for Offline Query Optimization.
Proc. ACM Manag. Data
Zixuan Yi, Yao Tian, Zachary G. Ives, Ryan Marcus
2025 J jnl
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer.
CoRR
Hanwen Liu, Qihan Zhang, Ryan Marcus, Ibrahim Sabek
2025 J jnl
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer.
Proc. ACM Manag. Data
Hanwen Liu, Qihan Zhang, Ryan Marcus, Ibrahim Sabek
2025 — conf
ScaleLLM: A Technique for Scalable LLM-augmented Data Systems.
SIGMOD Conference Companion
Ashwin Alaparthi, Paul Loh, Ryan Marcus
2024 J jnl
A Practical Theory of Generalization in Selectivity Learning.
CoRR
Peizhi Wu, Haoshu Xu, Ryan Marcus, Zachary G. Ives
2024 J jnl
BFTBrain: Adaptive BFT Consensus with Reinforcement Learning.
CoRR
Chenyuan Wu, Haoyun Qin, Mohammad Javad Amiri, Boon Thau Loo, Dahlia Malkhi, Ryan Marcus
2024 J jnl
BFTGym: An Interactive Playground for BFT Protocols.
Proc. VLDB Endow.
Haoyun Qin, Chenyuan Wu, Mohammad Javad Amiri, Ryan Marcus, Boon Thau Loo
2024 — conf
Low Rank Approximation for Learned Query Optimization.
aiDM@SIGMOD
Zixuan Yi, Yao Tian, Zachary G. Ives, Ryan Marcus
2024 — conf
Stage: Query Execution Time Prediction in Amazon Redshift.
SIGMOD Conference Companion
Ziniu Wu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi, Vikram Nathan, Pascal Pfeil, Gaurav Saxena, Mohammad Rahman, Balakrishnan Narayanaswamy, Tim Kraska
2024 J jnl
Stage: Query Execution Time Prediction in Amazon Redshift.
CoRR
Ziniu Wu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi, Vikram Nathan, Pascal Pfeil, Gaurav Saxena, Mohammad Rahman, Balakrishnan Narayanaswamy, Tim Kraska
2024 J jnl
The Unreasonable Effectiveness of LLMs for Query Optimization.
CoRR
Peter Akioyamen, Zixuan Yi, Ryan Marcus
2024 J jnl
Towards Full Stack Adaptivity in Permissioned Blockchains.
Proc. VLDB Endow.
Chenyuan Wu, Mohammad Javad Amiri, Haoyun Qin, Bhavana Mehta, Ryan Marcus, Boon Thau Loo
2024 J jnl
Towards Truly Adaptive Byzantine Fault-Tolerant Consensus.
ACM SIGOPS Oper. Syst. Rev.
Chenyuan Wu, Haoyun Qin, Mohammad Javad Amiri, Boon Thau Loo, Dahlia Malkhi, Ryan Marcus
2023 J jnl
AdaChain: A Learned Adaptive Blockchain.
Proc. VLDB Endow.
Chenyuan Wu, Bhavana Mehta, Mohammad Javad Amiri, Ryan Marcus, Boon Thau Loo
2023 J jnl
Adding Domain Knowledge to Query-Driven Learned Databases.
CoRR
Peizhi Wu, Ryan Marcus, Zachary G. Ives
2023 — conf
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift.
SIGMOD Conference Companion
Gaurav Saxena, Mohammad Rahman, Naresh Chainani, Chunbin Lin, George Caragea, Fahim Chowdhury, Ryan Marcus, Tim Kraska, Ippokratis Pandis, Balakrishnan (Murali) Narayanaswamy
2023 J jnl
AutoSteer: Learned Query Optimization for Any SQL Database.
Proc. VLDB Endow.
Christoph Anneser, Nesime Tatbul, David E. Cohen, Zhenggang Xu, Prithviraj Pandian, Nikolay Laptev, Ryan Marcus
2023 J jnl
Kepler: Robust Learning for Faster Parametric Query Optimization.
CoRR
Lyric Doshi, Vincent Zhuang, Gaurav Jain, Ryan Marcus, Haoyu Huang, Deniz Altinbüken, Eugene Brevdo, Campbell Fraser
2023 J jnl
Kepler: Robust Learning for Parametric Query Optimization.
Proc. ACM Manag. Data
Lyric Doshi, Vincent Zhuang, Gaurav Jain, Ryan Marcus, Haoyu Huang, Deniz Altinbüken, Eugene Brevdo, Campbell Fraser
2023 — conf
Learned Query Superoptimization.
VLDB Workshops
Ryan Marcus
2023 J jnl
Learned Query Superoptimization.
CoRR
Ryan Marcus
2023 J jnl
QO-Insight: Inspecting Steered Query Optimizers.
Proc. VLDB Endow.
Christoph Anneser, Mario Petruccelli, Nesime Tatbul, David E. Cohen, Zhenggang Xu, Prithviraj Pandian, Nikolay Laptev, Ryan Marcus, Alfons Kemper
2023 J jnl
Robust Query Driven Cardinality Estimation under Changing Workloads.
Proc. VLDB Endow.
Parimarjan Negi, Ziniu Wu, Andreas Kipf, Nesime Tatbul, Ryan Marcus, Sam Madden, Tim Kraska, Mohammad Alizadeh
2023 — conf
Towards Adaptive Fault-Tolerant Sharded Databases.
VLDB Workshops
Bhavana Mehta, Neelesh C. A, Prashanth S. Iyer, Mohammad Javad Amiri, Boon Thau Loo, Ryan Marcus
2022 J jnl
AdaChain: A Learned Adaptive Blockchain.
CoRR
Chenyuan Wu, Bhavana Mehta, Mohammad Javad Amiri, Ryan Marcus, Boon Thau Loo
2022 J jnl
Bao: Making Learned Query Optimization Practical.
SIGMOD Rec.
Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska
2022 J jnl
LSI: A Learned Secondary Index Structure.
CoRR
Andreas Kipf, Dominik Horn, Pascal Pfeil, Ryan Marcus, Tim Kraska
2022 — conf
LSI: a learned secondary index structure.
aiDM@SIGMOD
Andreas Kipf, Dominik Horn, Pascal Pfeil, Ryan Marcus, Tim Kraska
2022 J jnl
SageDB: An Instance-Optimized Data Analytics System.
Proc. VLDB Endow.
Jialin Ding, Ryan Marcus, Andreas Kipf, Vikram Nathan, Aniruddha Nrusimha, Kapil Vaidya, Alexander van Renen, Tim Kraska
2022 — ed.
aiDM '22: Proceedings of the Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, Philadelphia, Pennsylvania, USA, 17 June 2022
aiDM@SIGMOD
Rajesh Bordawekar, Oded Shmueli, Yael Amsterdamer, Donatella Firmani, Ryan Marcus
2022 — conf
aiDM'22: Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management.
SIGMOD Conference
Rajesh Bordawekar, Yael Amsterdamer, Donatella Firmani, Ryan Marcus, Oded Shmueli
2021 — conf
Bao: Making Learned Query Optimization Practical.
SIGMOD Conference
Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska
2021 J jnl
Flow-Loss: Learning Cardinality Estimates That Matter.
CoRR
Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh
2021 J jnl
Flow-Loss: Learning Cardinality Estimates That Matter.
Proc. VLDB Endow.
Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh
2021 — conf
LEA: A Learned Encoding Advisor for Column Stores.
aiDM@SIGMOD
Lujing Cen, Andreas Kipf, Ryan Marcus, Tim Kraska
2021 J jnl
LEA: A Learned Encoding Advisor for Column Stores.
CoRR
Lujing Cen, Andreas Kipf, Ryan Marcus, Tim Kraska
2021 J jnl
PLEX: Towards Practical Learned Indexing.
CoRR
Mihail Stoian, Andreas Kipf, Ryan Marcus, Tim Kraska
2021 — conf
Steering Query Optimizers: A Practical Take on Big Data Workloads.
SIGMOD Conference
Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc T. Friedman, Alekh Jindal
2021 — conf
Towards a Benchmark for Learned Systems.
ICDE Workshops
Laurent Bindschaedler, Andreas Kipf, Tim Kraska, Ryan Marcus, Umar Farooq Minhas
2020 J jnl
ARDA: Automatic Relational Data Augmentation for Machine Learning.
CoRR
Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen, Raul Castro Fernandez, Tim Kraska, David R. Karger
2020 J jnl
ARDA: Automatic Relational Data Augmentation for Machine Learning.
Proc. VLDB Endow.
Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen, Raul Castro Fernandez, Tim Kraska, David R. Karger
2020 J jnl
Bao: Learning to Steer Query Optimizers.
CoRR
Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska
2020 J jnl
Benchmarking Learned Indexes.
CoRR
Ryan Marcus, Andreas Kipf, Alexander van Renen, Mihail Stoian, Sanchit Misra, Alfons Kemper, Thomas Neumann, Tim Kraska
2020 J jnl
Benchmarking Learned Indexes.
Proc. VLDB Endow.
Ryan Marcus, Andreas Kipf, Alexander van Renen, Mihail Stoian, Sanchit Misra, Alfons Kemper, Thomas Neumann, Tim Kraska
2020 — conf
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning.
AIDB@VLDB
Chi Zhang, Ryan Marcus, Anat Kleiman, Olga Papaemmanouil
2020 J jnl
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning.
CoRR
Chi Zhang, Ryan Marcus, Anat Kleiman, Olga Papaemmanouil
2020 — conf
CDFShop: Exploring and Optimizing Learned Index Structures.
SIGMOD Conference
Ryan Marcus, Emily Zhang, Tim Kraska
2020 J jnl
Class-Weighted Evaluation Metrics for Imbalanced Data Classification.
CoRR
Akhilesh Gupta, Nesime Tatbul, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich
2020 J jnl
Context-Aware Parse Trees.
CoRR
Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Paul Petersen, Jesmin Jahan Tithi, Tim Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
2020 — conf
Cost-Guided Cardinality Estimation: Focus Where it Matters.
ICDE Workshops
Parimarjan Negi, Ryan Marcus, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh
2020 J jnl
Learned Garbage Collection.
CoRR
Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska
2020 — conf
Learned garbage collection.
MAPL@PLDI
Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska
2020 B conf
Low Rate Compression of Video with Dynamic Backgrounds.
DCC
Solomon Garber, Ryan Marcus, Antonella DiLillo, James A. Storer
2020 J jnl
MISIM: An End-to-End Neural Code Similarity System.
CoRR
Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Paul Petersen, Timothy G. Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
2020 J jnl
RadixSpline: A Single-Pass Learned Index.
CoRR
Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, Thomas Neumann
2020 — conf
RadixSpline: a single-pass learned index.
aiDM@SIGMOD
Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, Thomas Neumann
2019 — conf
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations.
SIGMOD Conference
Bailu Ding, Sudipto Das, Ryan Marcus, Wentao Wu, Surajit Chaudhuri, Vivek R. Narasayya
2019 B conf
Compact Representations of Dynamic Video Background Using Motion Sprites.
DCC
Solomon Garber, Aaditya Prakash, Ryan Marcus, Antonella DiLillo, James A. Storer
2019 J jnl
Flexible Operator Embeddings via Deep Learning.
CoRR
Ryan Marcus, Olga Papaemmanouil
2019 J jnl
NashDB: Fragmentation, Replication, and Provisioning using Economic Methods.
Proc. VLDB Endow.
Ryan Marcus, Chi Zhang, Shuai Yu, Geoffrey Kao, Olga Papaemmanouil
2019 J jnl
Neo: A Learned Query Optimizer.
CoRR
Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, Nesime Tatbul
2019 J jnl
Neo: A Learned Query Optimizer.
Proc. VLDB Endow.
Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, Nesime Tatbul
2019 A* conf
Park: An Open Platform for Learning-Augmented Computer Systems.
NeurIPS
Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Bojja Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Mohammad Alizadeh
2019 J jnl
Plan-Structured Deep Neural Network Models for Query Performance Prediction.
CoRR
Ryan Marcus, Olga Papaemmanouil
2019 J jnl
Plan-Structured Deep Neural Network Models for Query Performance Prediction.
Proc. VLDB Endow.
Ryan Marcus, Olga Papaemmanouil
2019 J jnl
SOSD: A Benchmark for Learned Indexes.
CoRR
Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, Thomas Neumann
2019 A conf
Towards a Hands-Free Query Optimizer through Deep Learning.
CIDR
Ryan Marcus, Olga Papaemmanouil
2018 — conf
Deep Reinforcement Learning for Join Order Enumeration.
aiDM@SIGMOD
Ryan Marcus, Olga Papaemmanouil
2018 J jnl
Deep Reinforcement Learning for Join Order Enumeration.
CoRR
Ryan Marcus, Olga Papaemmanouil
2018 — conf
NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning.
SIGMOD Conference
Ryan Marcus, Olga Papaemmanouil, Sofiya Semenova, Solomon Garber
2018 J jnl
Towards a Hands-Free Query Optimizer through Deep Learning.
CoRR
Ryan Marcus, Olga Papaemmanouil
2017 A* conf
A Learning-Based Service for Cost and Performance Management of Cloud Databases.
ICDE
Ryan Marcus, Sofiya Semenova, Olga Papaemmanouil
2017 A conf
Releasing Cloud Databases for the Chains of Performance Prediction Models.
CIDR
Ryan Marcus, Olga Papaemmanouil
2016 J jnl
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases.
CoRR
Ryan Marcus, Olga Papaemmanouil
2016 J jnl
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases.
Proc. VLDB Endow.
Ryan Marcus, Olga Papaemmanouil
2016 — conf
Workload management for cloud databases via machine learning.
ICDE Workshops
Ryan Marcus, Olga Papaemmanouil