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Ryan Marcus
Person information
- affiliation: University of Pennsylvania, Department of Computer and Information Science, Philadelphia, PA, USA
- affiliation: Massachusetts Institute of Technology (MIT), Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
- affiliation: Brandeis University, Waltham, MA, USA
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2020 – today
- 2024
- [j17]Chenyuan Wu, Mohammad Javad Amiri, Haoyun Qin, Bhavana Mehta, Ryan Marcus, Boon Thau Loo
:
Towards Full Stack Adaptivity in Permissioned Blockchains. Proc. VLDB Endow. 17(5): 1073-1080 (2024) - [j16]Haoyun Qin, Chenyuan Wu, Mohammad Javad Amiri, Ryan Marcus, Boon Thau Loo:
BFTGym: An Interactive Playground for BFT Protocols. Proc. VLDB Endow. 17(12): 4261-4264 (2024) - [j15]Chenyuan Wu, Haoyun Qin, Mohammad Javad Amiri, Boon Thau Loo, Dahlia Malkhi, Ryan Marcus:
Towards Truly Adaptive Byzantine Fault-Tolerant Consensus. ACM SIGOPS Oper. Syst. Rev. 58(1): 15-22 (2024) - [c26]Zixuan Yi
Low Rank Approximation for Learned Query Optimization. aiDM@SIGMOD 2024: 4:1-4:5 - [c25]Ziniu Wu
Stage: Query Execution Time Prediction in Amazon Redshift. SIGMOD Conference Companion 2024: 280-294 - [i27]Ziniu Wu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi, Vikram Nathan, Pascal Pfeil, Gaurav Saxena, Mohammad Rahman, Balakrishnan Narayanaswamy, Tim Kraska:
Stage: Query Execution Time Prediction in Amazon Redshift. CoRR abs/2403.02286 (2024) - [i26]Chenyuan Wu, Haoyun Qin, Mohammad Javad Amiri, Boon Thau Loo, Dahlia Malkhi, Ryan Marcus:
BFTBrain: Adaptive BFT Consensus with Reinforcement Learning. CoRR abs/2408.06432 (2024) - [i25]Peizhi Wu, Haoshu Xu, Ryan Marcus, Zachary G. Ives:
A Practical Theory of Generalization in Selectivity Learning. CoRR abs/2409.07014 (2024) - 2023
- [j14]Lyric Doshi
Kepler: Robust Learning for Parametric Query Optimization. Proc. ACM Manag. Data 1(1): 109:1-109:25 (2023) - [j13]Parimarjan Negi, Ziniu Wu, Andreas Kipf, Nesime Tatbul, Ryan Marcus, Sam Madden, Tim Kraska, Mohammad Alizadeh:
Robust Query Driven Cardinality Estimation under Changing Workloads. Proc. VLDB Endow. 16(6): 1520-1533 (2023) - [j12]Chenyuan Wu, Bhavana Mehta, Mohammad Javad Amiri
AdaChain: A Learned Adaptive Blockchain. Proc. VLDB Endow. 16(8): 2033-2046 (2023) - [j11]Christoph Anneser, Nesime Tatbul, David E. Cohen, Zhenggang Xu, Prithviraj Pandian, Nikolay Laptev, Ryan Marcus
AutoSteer: Learned Query Optimization for Any SQL Database. Proc. VLDB Endow. 16(12): 3515-3527 (2023) - [j10]Christoph Anneser, Mario Petruccelli, Nesime Tatbul, David E. Cohen, Zhenggang Xu, Prithviraj Pandian, Nikolay Laptev, Ryan Marcus, Alfons Kemper:
QO-Insight: Inspecting Steered Query Optimizers. Proc. VLDB Endow. 16(12): 3922-3925 (2023) - [c24]Gaurav Saxena
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift. SIGMOD Conference Companion 2023: 225-237 - [c23]Ryan Marcus:
Learned Query Superoptimization. VLDB Workshops 2023 - [c22]Bhavana Mehta, Neelesh C. A, Prashanth S. Iyer, Mohammad Javad Amiri, Boon Thau Loo
Towards Adaptive Fault-Tolerant Sharded Databases. VLDB Workshops 2023 - [i24]Ryan Marcus:
Learned Query Superoptimization. CoRR abs/2303.15308 (2023) - [i23]Lyric Doshi, Vincent Zhuang, Gaurav Jain, Ryan Marcus, Haoyu Huang, Deniz Altinbüken, Eugene Brevdo, Campbell Fraser:
Kepler: Robust Learning for Faster Parametric Query Optimization. CoRR abs/2306.06798 (2023) - [i22]Peizhi Wu, Ryan Marcus, Zachary G. Ives:
Adding Domain Knowledge to Query-Driven Learned Databases. CoRR abs/2312.01025 (2023) - 2022
- [j9]Jialin Ding, Ryan Marcus, Andreas Kipf, Vikram Nathan, Aniruddha Nrusimha, Kapil Vaidya, Alexander van Renen, Tim Kraska:
SageDB: An Instance-Optimized Data Analytics System. Proc. VLDB Endow. 15(13): 4062-4078 (2022) - [j8]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska:
Bao: Making Learned Query Optimization Practical. SIGMOD Rec. 51(1): 6-13 (2022) - [c21]Andreas Kipf, Dominik Horn, Pascal Pfeil, Ryan Marcus, Tim Kraska:
LSI: a learned secondary index structure. aiDM@SIGMOD 2022: 4:1-4:5 - [c20]Rajesh Bordawekar, Yael Amsterdamer
aiDM'22: Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. SIGMOD Conference 2022: 2550-2551 - [e1]Rajesh Bordawekar, Oded Shmueli, Yael Amsterdamer, Donatella Firmani, Ryan Marcus:
aiDM '22: Proceedings of the Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, Philadelphia, Pennsylvania, USA, 17 June 2022. ACM 2022, ISBN 978-1-4503-9377-5 [contents] - [i21]Andreas Kipf, Dominik Horn, Pascal Pfeil, Ryan Marcus, Tim Kraska:
LSI: A Learned Secondary Index Structure. CoRR abs/2205.05769 (2022) - [i20]Chenyuan Wu, Bhavana Mehta, Mohammad Javad Amiri, Ryan Marcus, Boon Thau Loo
AdaChain: A Learned Adaptive Blockchain. CoRR abs/2211.01580 (2022) - 2021
- [j7]Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh:
Flow-Loss: Learning Cardinality Estimates That Matter. Proc. VLDB Endow. 14(11): 2019-2032 (2021) - [c19]Laurent Bindschaedler, Andreas Kipf, Tim Kraska, Ryan Marcus, Umar Farooq Minhas:
Towards a Benchmark for Learned Systems. ICDE Workshops 2021: 127-133 - [c18]Lujing Cen, Andreas Kipf, Ryan Marcus, Tim Kraska:
LEA: A Learned Encoding Advisor for Column Stores. aiDM@SIGMOD 2021: 32-35 - [c17]Ryan Marcus
Bao: Making Learned Query Optimization Practical. SIGMOD Conference 2021: 1275-1288 - [c16]Parimarjan Negi, Matteo Interlandi, Ryan Marcus
Steering Query Optimizers: A Practical Take on Big Data Workloads. SIGMOD Conference 2021: 2557-2569 - [i19]Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh:
Flow-Loss: Learning Cardinality Estimates That Matter. CoRR abs/2101.04964 (2021) - [i18]Lujing Cen, Andreas Kipf, Ryan Marcus, Tim Kraska:
LEA: A Learned Encoding Advisor for Column Stores. CoRR abs/2105.08830 (2021) - [i17]Mihail Stoian, Andreas Kipf, Ryan Marcus, Tim Kraska:
PLEX: Towards Practical Learned Indexing. CoRR abs/2108.05117 (2021) - 2020
- [j6]Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen, Raul Castro Fernandez, Tim Kraska, David R. Karger
ARDA: Automatic Relational Data Augmentation for Machine Learning. Proc. VLDB Endow. 13(9): 1373-1387 (2020) - [j5]Ryan Marcus, Andreas Kipf, Alexander van Renen, Mihail Stoian, Sanchit Misra, Alfons Kemper, Thomas Neumann, Tim Kraska:
Benchmarking Learned Indexes. Proc. VLDB Endow. 14(1): 1-13 (2020) - [c15]Solomon Garber, Ryan Marcus, Antonella DiLillo, James A. Storer:
Low Rate Compression of Video with Dynamic Backgrounds. DCC 2020: 371 - [c14]Parimarjan Negi, Ryan Marcus, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh:
Cost-Guided Cardinality Estimation: Focus Where it Matters. ICDE Workshops 2020: 154-157 - [c13]Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska:
Learned garbage collection. MAPL@PLDI 2020: 38-44 - [c12]Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, Thomas Neumann:
RadixSpline: a single-pass learned index. aiDM@SIGMOD 2020: 5:1-5:5 - [c11]Ryan Marcus, Emily Zhang, Tim Kraska:
CDFShop: Exploring and Optimizing Learned Index Structures. SIGMOD Conference 2020: 2789-2792 - [c10]Chi Zhang, Ryan Marcus, Anat Kleiman, Olga Papaemmanouil:
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning. AIDB@VLDB 2020 - [i16]Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen, Raul Castro Fernandez, Tim Kraska, David R. Karger:
ARDA: Automatic Relational Data Augmentation for Machine Learning. CoRR abs/2003.09758 (2020) - [i15]Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Paul Petersen, Jesmin Jahan Tithi, Tim Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich:
Context-Aware Parse Trees. CoRR abs/2003.11118 (2020) - [i14]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska:
Bao: Learning to Steer Query Optimizers. CoRR abs/2004.03814 (2020) - [i13]Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska:
Learned Garbage Collection. CoRR abs/2004.13301 (2020) - [i12]Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, Thomas Neumann:
RadixSpline: A Single-Pass Learned Index. CoRR abs/2004.14541 (2020) - [i11]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:
MISIM: An End-to-End Neural Code Similarity System. CoRR abs/2006.05265 (2020) - [i10]Ryan Marcus, Andreas Kipf, Alexander van Renen, Mihail Stoian, Sanchit Misra, Alfons Kemper, Thomas Neumann, Tim Kraska:
Benchmarking Learned Indexes. CoRR abs/2006.12804 (2020) - [i9]Chi Zhang, Ryan Marcus, Anat Kleiman, Olga Papaemmanouil:
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning. CoRR abs/2007.10568 (2020) - [i8]Akhilesh Gupta, Nesime Tatbul, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich:
Class-Weighted Evaluation Metrics for Imbalanced Data Classification. CoRR abs/2010.05995 (2020)
2010 – 2019
- 2019
- [j4]Ryan Marcus
Neo: A Learned Query Optimizer. Proc. VLDB Endow. 12(11): 1705-1718 (2019) - [j3]Ryan Marcus
Plan-Structured Deep Neural Network Models for Query Performance Prediction. Proc. VLDB Endow. 12(11): 1733-1746 (2019) - [j2]Ryan Marcus, Chi Zhang
NashDB: Fragmentation, Replication, and Provisioning using Economic Methods. Proc. VLDB Endow. 12(12): 1830-1833 (2019) - [c9]Ryan Marcus, Olga Papaemmanouil:
Towards a Hands-Free Query Optimizer through Deep Learning. CIDR 2019 - [c8]Solomon Garber, Aaditya Prakash, Ryan Marcus, Antonella DiLillo, James A. Storer:
Compact Representations of Dynamic Video Background Using Motion Sprites. DCC 2019: 438-447 - [c7]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:
Park: An Open Platform for Learning-Augmented Computer Systems. NeurIPS 2019: 2490-2502 - [c6]Bailu Ding, Sudipto Das
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations. SIGMOD Conference 2019: 1241-1258 - [i7]Ryan Marcus, Olga Papaemmanouil:
Flexible Operator Embeddings via Deep Learning. CoRR abs/1901.09090 (2019) - [i6]Ryan Marcus, Olga Papaemmanouil:
Plan-Structured Deep Neural Network Models for Query Performance Prediction. CoRR abs/1902.00132 (2019) - [i5]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, Nesime Tatbul:
Neo: A Learned Query Optimizer. CoRR abs/1904.03711 (2019) - [i4]Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, Thomas Neumann:
SOSD: A Benchmark for Learned Indexes. CoRR abs/1911.13014 (2019) - 2018
- [c5]Ryan Marcus, Olga Papaemmanouil
Deep Reinforcement Learning for Join Order Enumeration. aiDM@SIGMOD 2018: 3:1-3:4 - [c4]Ryan Marcus, Olga Papaemmanouil, Sofiya Semenova, Solomon Garber:
NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning. SIGMOD Conference 2018: 1253-1267 - [i3]Ryan Marcus, Olga Papaemmanouil:
Deep Reinforcement Learning for Join Order Enumeration. CoRR abs/1803.00055 (2018) - [i2]Ryan Marcus, Olga Papaemmanouil:
Towards a Hands-Free Query Optimizer through Deep Learning. CoRR abs/1809.10212 (2018) - 2017
- [c3]Ryan Marcus, Olga Papaemmanouil:
Releasing Cloud Databases for the Chains of Performance Prediction Models. CIDR 2017 - [c2]Ryan Marcus
A Learning-Based Service for Cost and Performance Management of Cloud Databases. ICDE 2017: 1361-1362 - 2016
- [j1]Ryan Marcus
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases. Proc. VLDB Endow. 9(10): 780-791 (2016) - [c1]Ryan Marcus
Workload management for cloud databases via machine learning. ICDE Workshops 2016: 27-30 - [i1]Ryan Marcus, Olga Papaemmanouil:
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases. CoRR abs/1601.08221 (2016)
Coauthor Index
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last updated on 2024-12-22 19:04 CET by the dblp team
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