Workload management for cloud databases via machine learning. - Ryan Marcus; Olga Papaemmanouil (ICDE Workshops, 2016)

WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases. - Ryan Marcus; Olga Papaemmanouil (Proc. VLDB Endow., 2016)

Releasing Cloud Databases for the Chains of Performance Prediction Models. - Ryan Marcus; Olga Papaemmanouil (CIDR, 2017)

A Learning-Based Service for Cost and Performance Management of Cloud Databases. - Ryan Marcus; Sofiya Semenova; Olga Papaemmanouil (ICDE, 2017)

Deep Reinforcement Learning for Join Order Enumeration. - Ryan Marcus; Olga Papaemmanouil (aiDM@SIGMOD, 2018)

NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning. - Ryan Marcus; Olga Papaemmanouil; Sofiya Semenova; Solomon Garber (SIGMOD Conference, 2018)

Towards a Hands-Free Query Optimizer through Deep Learning. - Ryan Marcus; Olga Papaemmanouil (CIDR, 2019)

Compact Representations of Dynamic Video Background Using Motion Sprites. - Solomon Garber; Aaditya Prakash; Ryan Marcus; Antonella DiLillo; James A. Storer (DCC, 2019)

Park: An Open Platform for Learning-Augmented Computer Systems. - Hongzi Mao; Parimarjan Negi; Akshay Narayan 001; Hanrui Wang 002; Jiacheng Yang; Haonan Wang; Ryan Marcus; Ravichandra Addanki; Mehrdad Khani Shirkoohi; Songtao He; Vikram Nathan; Frank Cangialosi; et al. (NeurIPS, 2019)

AI Meets AI: Leveraging Query Executions to Improve Index Recommendations. - Bailu Ding; Sudipto Das; Ryan Marcus; Wentao Wu 001; Surajit Chaudhuri; Vivek R. Narasayya (SIGMOD Conference, 2019)

Flexible Operator Embeddings via Deep Learning. - Ryan Marcus; Olga Papaemmanouil (CoRR, 2019)

SOSD: A Benchmark for Learned Indexes. - Andreas Kipf; Ryan Marcus; Alexander van Renen; Mihail Stoian; Alfons Kemper; Tim Kraska; Thomas Neumann 001 (CoRR, 2019)

Neo: A Learned Query Optimizer. - Ryan Marcus; Parimarjan Negi; Hongzi Mao; Chi Zhang 068; Mohammad Alizadeh; Tim Kraska; Olga Papaemmanouil; Nesime Tatbul (Proc. VLDB Endow., 2019)

Plan-Structured Deep Neural Network Models for Query Performance Prediction. - Ryan Marcus; Olga Papaemmanouil (Proc. VLDB Endow., 2019)

NashDB: Fragmentation, Replication, and Provisioning using Economic Methods. - Ryan Marcus; Chi Zhang 068; Shuai Yu; Geoffrey Kao; Olga Papaemmanouil (Proc. VLDB Endow., 2019)

Low Rate Compression of Video with Dynamic Backgrounds. - Solomon Garber; Ryan Marcus; Antonella DiLillo; James A. Storer (DCC, 2020)

Cost-Guided Cardinality Estimation: Focus Where it Matters. - Parimarjan Negi; Ryan Marcus; Hongzi Mao; Nesime Tatbul; Tim Kraska; Mohammad Alizadeh (ICDE Workshops, 2020)

Learned garbage collection. - Lujing Cen; Ryan Marcus; Hongzi Mao; Justin Gottschlich; Mohammad Alizadeh; Tim Kraska (MAPL@PLDI, 2020)

RadixSpline: a single-pass learned index. - Andreas Kipf; Ryan Marcus; Alexander van Renen; Mihail Stoian; Alfons Kemper; Tim Kraska; Thomas Neumann 001 (aiDM@SIGMOD, 2020)

CDFShop: Exploring and Optimizing Learned Index Structures. - Ryan Marcus; Emily Zhang; Tim Kraska (SIGMOD Conference, 2020)

Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning. - Chi Zhang 068; Ryan Marcus; Anat Kleiman; Olga Papaemmanouil (AIDB@VLDB, 2020)

Context-Aware Parse Trees. - Fangke Ye; Shengtian Zhou; Anand Venkat; Ryan Marcus; Paul Petersen; Jesmin Jahan Tithi; Tim Mattson; Tim Kraska; Pradeep Dubey; Vivek Sarkar; Justin Gottschlich (CoRR, 2020)

Bao: Learning to Steer Query Optimizers. - Ryan Marcus; Parimarjan Negi; Hongzi Mao; Nesime Tatbul; Mohammad Alizadeh; Tim Kraska (CoRR, 2020)

MISIM: An End-to-End Neural Code Similarity System. - 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 (CoRR, 2020)

Class-Weighted Evaluation Metrics for Imbalanced Data Classification. - Akhilesh Gupta; Nesime Tatbul; Ryan Marcus; Shengtian Zhou; Insup Lee 001; Justin Gottschlich (CoRR, 2020)

ARDA: Automatic Relational Data Augmentation for Machine Learning. - Nadiia Chepurko; Ryan Marcus; Emanuel Zgraggen; Raul Castro Fernandez; Tim Kraska; David R. Karger (Proc. VLDB Endow., 2020)

Benchmarking Learned Indexes. - Ryan Marcus; Andreas Kipf; Alexander van Renen; Mihail Stoian; Sanchit Misra; Alfons Kemper; Thomas Neumann 001; Tim Kraska (Proc. VLDB Endow., 2020)

Towards a Benchmark for Learned Systems. - Laurent Bindschaedler; Andreas Kipf; Tim Kraska; Ryan Marcus; Umar Farooq Minhas (ICDE Workshops, 2021)

LEA: A Learned Encoding Advisor for Column Stores. - Lujing Cen; Andreas Kipf; Ryan Marcus; Tim Kraska (aiDM@SIGMOD, 2021)

Steering Query Optimizers: A Practical Take on Big Data Workloads. - Parimarjan Negi; Matteo Interlandi; Ryan Marcus; Mohammad Alizadeh; Tim Kraska; Marc T. Friedman; Alekh Jindal (SIGMOD Conference, 2021)

PLEX: Towards Practical Learned Indexing. - Mihail Stoian; Andreas Kipf; Ryan Marcus; Tim Kraska (CoRR, 2021)

Flow-Loss: Learning Cardinality Estimates That Matter. - Parimarjan Negi; Ryan Marcus; Andreas Kipf; Hongzi Mao; Nesime Tatbul; Tim Kraska; Mohammad Alizadeh (Proc. VLDB Endow., 2021)

aiDM'22: Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. - Rajesh Bordawekar; Yael Amsterdamer; Donatella Firmani; Ryan Marcus; Oded Shmueli (SIGMOD Conference, 2022)

LSI: a learned secondary index structure. - Andreas Kipf; Dominik Horn; Pascal Pfeil; Ryan Marcus; Tim Kraska (aiDM@SIGMOD, 2022)

SageDB: An Instance-Optimized Data Analytics System. - Jialin Ding; Ryan Marcus; Andreas Kipf; Vikram Nathan; Aniruddha Nrusimha; Kapil Vaidya; Alexander van Renen; Tim Kraska (Proc. VLDB Endow., 2022)

Bao: Making Learned Query Optimization Practical. - Ryan Marcus; Parimarjan Negi; Hongzi Mao; Nesime Tatbul; Mohammad Alizadeh; Tim Kraska (SIGMOD Rec., 2022)

Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift. - Gaurav Saxena; Mohammad Rahman; Naresh Chainani; Chunbin Lin; George Caragea; Fahim Chowdhury; Ryan Marcus; Tim Kraska; Ippokratis Pandis; Balakrishnan (Murali) Narayanaswamy (SIGMOD Conference Companion, 2023)

Learned Query Superoptimization. - Ryan Marcus (VLDB Workshops, 2023)

Towards Adaptive Fault-Tolerant Sharded Databases. - Bhavana Mehta; Neelesh C. A; Prashanth S. Iyer; Mohammad Javad Amiri; Boon Thau Loo; Ryan Marcus (VLDB Workshops, 2023)

Kepler: Robust Learning for Faster Parametric Query Optimization. - Lyric Doshi; Vincent Zhuang; Gaurav Jain; Ryan Marcus; Haoyu Huang; Deniz Altinbüken; Eugene Brevdo; Campbell Fraser (CoRR, 2023)

Adding Domain Knowledge to Query-Driven Learned Databases. - Peizhi Wu; Ryan Marcus; Zachary G. Ives (CoRR, 2023)

Kepler: Robust Learning for Parametric Query Optimization. - Lyric Doshi; Vincent Zhuang; Gaurav Jain; Ryan Marcus; Haoyu Huang; Deniz Altinbüken; Eugene Brevdo; Campbell Fraser (Proc. ACM Manag. Data, 2023)

QO-Insight: Inspecting Steered Query Optimizers. - Christoph Anneser; Mario Petruccelli; Nesime Tatbul; David E. Cohen; Zhenggang Xu; Prithviraj Pandian; Nikolay Laptev; Ryan Marcus; Alfons Kemper (Proc. VLDB Endow., 2023)

AutoSteer: Learned Query Optimization for Any SQL Database. - Christoph Anneser; Nesime Tatbul; David E. Cohen; Zhenggang Xu; Prithviraj Pandian; Nikolay Laptev; Ryan Marcus (Proc. VLDB Endow., 2023)

Robust Query Driven Cardinality Estimation under Changing Workloads. - Parimarjan Negi; Ziniu Wu; Andreas Kipf; Nesime Tatbul; Ryan Marcus; Sam Madden; Tim Kraska; Mohammad Alizadeh (Proc. VLDB Endow., 2023)

AdaChain: A Learned Adaptive Blockchain. - Chenyuan Wu; Bhavana Mehta; Mohammad Javad Amiri; Ryan Marcus; Boon Thau Loo (Proc. VLDB Endow., 2023)

Low Rank Approximation for Learned Query Optimization. - Zixuan Yi; Yao Tian; Zachary G. Ives; Ryan Marcus (aiDM@SIGMOD, 2024)

Stage: Query Execution Time Prediction in Amazon Redshift. - Ziniu Wu; Ryan Marcus; Zhengchun Liu; Parimarjan Negi; Vikram Nathan; Pascal Pfeil; Gaurav Saxena; Mohammad Rahman; Balakrishnan Narayanaswamy; Tim Kraska (SIGMOD Conference Companion, 2024)

BFTBrain: Adaptive BFT Consensus with Reinforcement Learning. - Chenyuan Wu; Haoyun Qin; Mohammad Javad Amiri; Boon Thau Loo; Dahlia Malkhi; Ryan Marcus (CoRR, 2024)

A Practical Theory of Generalization in Selectivity Learning. - Peizhi Wu; Haoshu Xu; Ryan Marcus; Zachary G. Ives (CoRR, 2024)

Towards Full Stack Adaptivity in Permissioned Blockchains. - Chenyuan Wu; Mohammad Javad Amiri; Haoyun Qin; Bhavana Mehta; Ryan Marcus; Boon Thau Loo (Proc. VLDB Endow., 2024)

Towards Truly Adaptive Byzantine Fault-Tolerant Consensus. - Chenyuan Wu; Haoyun Qin; Mohammad Javad Amiri; Boon Thau Loo; Dahlia Malkhi; Ryan Marcus (ACM SIGOPS Oper. Syst. Rev., 2024)

BFTGym: An Interactive Playground for BFT Protocols. - Haoyun Qin; Chenyuan Wu; Mohammad Javad Amiri; Ryan Marcus; Boon Thau Loo (Proc. VLDB Endow., 2024)