Theodoros Rekatsinas

83 papers A* 9A 8Journal 49Unranked 16

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks. CoRR Roger Waleffe, Devesh Sarda, Jason Mohoney, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Theodoros Rekatsinas, Shivaram Venkataraman
2025	J	`jnl`	Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations. CoRR Cédric Renggli, Ihab F. Ilyas, Theodoros Rekatsinas
2025	A*	`conf`	MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage. ICDE Yongjun He, Roger Waleffe, Zhichao Han, Johnu George, Binhang Yuan, Zitao Zhang, Yinan Shan, Yang Zhao, Debojyoti Dutta, Theodoros Rekatsinas, Ce Zhang
2025	J	`jnl`	MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage. CoRR Yongjun He, Roger Waleffe, Zhichao Han, Johnu George, Binhang Yuan, Zitao Zhang, Yinan Shan, Yang Zhao, Debojyoti Dutta, Theodoros Rekatsinas, Ce Zhang
2025	A*	`conf`	Quake: Adaptive Indexing for Vector Search. OSDI Jason Mohoney, Devesh Sarda, Mengze Tang, Shihabur Rahman Chowdhury, Anil Pacaci, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
2025	J	`jnl`	Quake: Adaptive Indexing for Vector Search. CoRR Jason Mohoney, Devesh Sarda, Mengze Tang, Shihabur Rahman Chowdhury, Anil Pacaci, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
2025	J	`jnl`	Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis. VLDB J. Yunjia Zhang, Yannis Chronis, Jignesh M. Patel, Theodoros Rekatsinas
2024	—	`conf`	Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation. LREC/COLING Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Joshua M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly
2024	J	`jnl`	Incremental IVF Index Maintenance for Streaming Vector Search. CoRR Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Umar Farooq Minhas, Jeffrey Pound, Cédric Renggli, Nima Reyhani, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
2024	A*	`conf`	Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning. ICLR Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis, Amin Karbasi, Dionysios S. Kalogerias, Nezihe Merve Gürel, Theodoros Rekatsinas
2024	A*	`conf`	TSDS: Data Selection for Task-Specific Model Finetuning. NeurIPS Zifan Liu, Amin Karbasi, Theodoros Rekatsinas
2024	J	`jnl`	TSDS: Data Selection for Task-Specific Model Finetuning. CoRR Zifan Liu, Amin Karbasi, Theodoros Rekatsinas
2023	A	`conf`	Co-design Hardware and Algorithm for Vector Search. SC Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso
2023	J	`jnl`	Co-design Hardware and Algorithm for Vector Search. CoRR Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso
2023	J	`jnl`	Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation. CoRR Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Josh M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly
2023	—	`conf`	EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy. HotCarbon Hongyu Hè, Michal Friedman, Theodoros Rekatsinas
2023	J	`jnl`	EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy. CoRR Hongyu Hè, Michal Friedman, Theodoros Rekatsinas
2023	J	`jnl`	Fact Ranking over Large-Scale Knowledge Graphs with Reasoning Embedding Models. IEEE Data Eng. Bull. Hongyu Ren, Ali Mousavi, Anil Pacaci, Shihabur Rahman Chowdhury, Jason Mohoney, Ihab F. Ilyas, Yunyao Li, Theodoros Rekatsinas
2023	—	`conf`	Growing and Serving Large Open-domain Knowledge Graphs. SIGMOD Conference Companion Ihab F. Ilyas, J. P. Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, Chiraag Sumanth
2023	J	`jnl`	Growing and Serving Large Open-domain Knowledge Graphs. CoRR Ihab F. Ilyas, J. P. Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, Chiraag Sumanth
2023	J	`jnl`	High-Throughput Vector Similarity Search in Knowledge Graphs. CoRR Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Ali Mousavi, Ihab F. Ilyas, Umar Farooq Minhas, Jeffrey Pound, Theodoros Rekatsinas
2023	J	`jnl`	High-Throughput Vector Similarity Search in Knowledge Graphs. Proc. ACM Manag. Data Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Ali Mousavi, Ihab F. Ilyas, Umar Farooq Minhas, Jeffrey Pound, Theodoros Rekatsinas
2023	A	`conf`	MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. EuroSys Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
2023	J	`jnl`	Open Domain Knowledge Extraction for Knowledge Graphs. CoRR Kun Qian, Anton Belyi, Fei Wu, Samira Khorshidi, Azadeh Nikfarjam, Rahul Khot, Yisi Sang, Katherine Luna, Xianqi Chu, Eric Choi, Yash Govind, Chloe Seivwright, Yiwen Sun, Ahmed Fakhry, Theodoros Rekatsinas, Ihab F. Ilyas, Xiaoguang Qi, Yunyao Li
2023	J	`jnl`	Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning. CoRR Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis, Amin Karbasi, Dionysios S. Kalogerias, Nezihe Merve Gürel, Theodoros Rekatsinas
2023	J	`jnl`	Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis. Proc. VLDB Endow. Yunjia Zhang, Yannis Chronis, Jignesh M. Patel, Theodoros Rekatsinas
2022	A	`conf`	Can Transfer Learning be used to build a Query Optimizer? CIDR Yunjia Zhang, Yannis Chronis, Jignesh M. Patel, Theodoros Rekatsinas
2022	J	`jnl`	Machine Learning and Data Cleaning: Which Serves the Other? ACM J. Data Inf. Qual. Ihab F. Ilyas, Theodoros Rekatsinas
2022	J	`jnl`	Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine. CoRR Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
2022	J	`jnl`	Picket: guarding against corrupted data in tabular data during learning and inference. VLDB J. Zifan Liu, Zhechun Zhou, Theodoros Rekatsinas
2022	J	`jnl`	Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale. CoRR Ihab F. Ilyas, Theodoros Rekatsinas, Vishnu Konda, Jeffrey Pound, Xiaoguang Qi, Mohamed A. Soliman
2022	—	`conf`	Saga: A Platform for Continuous Construction and Serving of Knowledge at Scale. SIGMOD Conference Ihab F. Ilyas, Theodoros Rekatsinas, Vishnu Konda, Jeffrey Pound, Xiaoguang Qi, Mohamed A. Soliman
2021	J	`jnl`	Demonstration of Marius: Graph Embeddings with a Single Machine. Proc. VLDB Endow. Anders Carlsson, Anze Xie, Jason Mohoney, Roger Waleffe, Shanan Peters, Theodoros Rekatsinas, Shivaram Venkataraman
2021	J	`jnl`	Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins. CoRR Sahaana Suri, Ihab F. Ilyas, Christopher Ré, Theodoros Rekatsinas
2021	J	`jnl`	Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins. Proc. VLDB Endow. Sahaana Suri, Ihab F. Ilyas, Christopher Ré, Theodoros Rekatsinas
2021	J	`jnl`	Learning Massive Graph Embeddings on a Single Machine. CoRR Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman
2021	A*	`conf`	Marius: Learning Massive Graph Embeddings on a Single Machine. OSDI Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, Shivaram Venkataraman
2021	A*	`conf`	On Robust Mean Estimation under Coordinate-level Corruption. ICML Zifan Liu, Jongho Park, Theodoros Rekatsinas, Christos Tzamos
2020	—	`conf`	A Statistical Perspective on Discovering Functional Dependencies in Noisy Data. SIGMOD Conference Yunjia Zhang, Zhihan Guo, Theodoros Rekatsinas
2020	J	`jnl`	An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation. CoRR Deepan Das, Haley Massa, Abhimanyu Kulkarni, Theodoros Rekatsinas
2020	—	`conf`	Attention-based Learning for Missing Data Imputation in HoloClean. MLSys Richard Wu, Aoqian Zhang, Ihab F. Ilyas, Theodoros Rekatsinas
2020	A*	`conf`	Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. ICML Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha
2020	J	`jnl`	Picket: Self-supervised Data Diagnostics for ML Pipelines. CoRR Zifan Liu, Zhechun Zhou, Theodoros Rekatsinas
2020	J	`jnl`	Principal Component Networks: Parameter Reduction Early in Training. CoRR Roger Waleffe, Theodoros Rekatsinas
2020	J	`jnl`	Record fusion: A learning approach. CoRR Alireza Heidari, George Michalopoulos, Shrinu Kushagra, Ihab F. Ilyas, Theodoros Rekatsinas
2020	J	`jnl`	Robust Mean Estimation under Coordinate-level Corruption. CoRR Zifan Liu, Jongho Park, Nils Palumbo, Theodoros Rekatsinas, Christos Tzamos
2020	—	`conf`	Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion. EMNLP (Findings) Ankur Goswami, Akshata Bhat, Hadar Ohana, Theodoros Rekatsinas
2020	J	`jnl`	Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion. CoRR Ankur Goswami, Akshata Bhat, Hadar Ohana, Theodoros Rekatsinas
2019	A	`conf`	A Formal Framework for Probabilistic Unclean Databases. ICDT Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas
2019	A	`conf`	Approximate Inference in Structured Instances with Noisy Categorical Observations. UAI Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas
2019	J	`jnl`	Approximate Inference in Structured Instances with Noisy Categorical Observations. CoRR Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas
2019	A	`conf`	CRUX: Adaptive Querying for Efficient Crowdsourced Data Extraction. CIKM Theodoros Rekatsinas, Amol Deshpande, Aditya G. Parameswaran
2019	A*	`conf`	Data Integration and Machine Learning: A Natural Synergy. KDD Xin Luna Dong, Theodoros Rekatsinas
2019	J	`jnl`	Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. CoRR Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha
2019	J	`jnl`	Fine-Grained Object Detection over Scientific Document Images with Region Embeddings. CoRR Ankur Goswami, Joshua McGrath, Shanan Peters, Theodoros Rekatsinas
2019	—	`conf`	HoloDetect: Few-Shot Learning for Error Detection. SIGMOD Conference Alireza Heidari, Joshua McGrath, Ihab F. Ilyas, Theodoros Rekatsinas
2019	J	`jnl`	HoloDetect: Few-Shot Learning for Error Detection. CoRR Alireza Heidari, Joshua McGrath, Ihab F. Ilyas, Theodoros Rekatsinas
2019	J	`jnl`	Learning Functional Dependencies with Sparse Regression. CoRR Zhihan Guo, Theodoros Rekatsinas
2019	J	`jnl`	Opportunities for Data Management Research in the Era of Horizontal AI/ML. Proc. VLDB Endow. Theodoros Rekatsinas, Sudeepa Roy, Manasi Vartak, Ce Zhang, Neoklis Polyzotis
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
2018	J	`jnl`	A Formal Framework For Probabilistic Unclean Databases. CoRR Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas
2018	—	`conf`	Data Integration and Machine Learning: A Natural Synergy. SIGMOD Conference Xin Luna Dong, Theodoros Rekatsinas
2018	J	`jnl`	Data Integration and Machine Learning: A Natural Synergy. Proc. VLDB Endow. Luna Dong, Theodoros Rekatsinas
2018	—	`conf`	Deep Learning for Entity Matching: A Design Space Exploration. SIGMOD Conference Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, Vijay Raghavendra
2018	—	`conf`	Fonduer: Knowledge Base Construction from Richly Formatted Data. SIGMOD Conference Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas, Philip Alexander Levis, Christopher Ré
2017	J	`jnl`	Fonduer: Knowledge Base Construction from Richly Formatted Data. CoRR Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas, Philip Alexander Levis, Christopher Ré
2017	J	`jnl`	Forecasting rare disease outbreaks from open source indicators. Stat. Anal. Data Min. Theodoros Rekatsinas, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, Naren Ramakrishnan
2017	J	`jnl`	HoloClean: Holistic Data Repairs with Probabilistic Inference. CoRR Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, Christopher Ré
2017	J	`jnl`	HoloClean: Holistic Data Repairs with Probabilistic Inference. Proc. VLDB Endow. Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, Christopher Ré
2017	—	`conf`	SLiMFast: Guaranteed Results for Data Fusion and Source Reliability. SIGMOD Conference Theodoros Rekatsinas, Manas Joglekar, Hector Garcia-Molina, Aditya G. Parameswaran, Christopher Ré
2016	—	`conf`	SourceSight: Enabling Effective Source Selection. SIGMOD Conference Theodoros Rekatsinas, Amol Deshpande, Xin Luna Dong, Lise Getoor, Divesh Srivastava
2015	J	`jnl`	CrowdGather: Entity Extraction over Structured Domains. CoRR Theodoros Rekatsinas, Amol Deshpande, Aditya G. Parameswaran
2015	J	`jnl`	Exploiting Features for Data Source Quality Estimation. CoRR Manas Joglekar, Theodoros Rekatsinas, Hector Garcia-Molina, Aditya G. Parameswaran, Christopher Ré
2015	A	`conf`	Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration. CIDR Theodoros Rekatsinas, Xin Luna Dong, Lise Getoor, Divesh Srivastava
2015	A*	`conf`	HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. ICML Xinran He, Theodoros Rekatsinas, James R. Foulds, Lise Getoor, Yan Liu
2015	—	`—`	Quality-Aware Data Source Management. Theodoros Rekatsinas
2015	A	`conf`	SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources. SDM Theodoros Rekatsinas, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, Naren Ramakrishnan
2015	—	`conf`	StoryPivot: Comparing and Contrasting Story Evolution. SIGMOD Conference Anja Gruenheid, Donald Kossmann, Theodoros Rekatsinas, Divesh Srivastava
2014	—	`conf`	Characterizing and selecting fresh data sources. SIGMOD Conference Theodoros Rekatsinas, Xin Luna Dong, Divesh Srivastava
2013	J	`jnl`	A SPARSI: Partitioning Sensitive Data amongst Multiple Adversaries. Proc. VLDB Endow. Theodoros Rekatsinas, Amol Deshpande, Ashwin Machanavajjhala
2013	J	`jnl`	Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss CoRR Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor
2013	J	`jnl`	On Sharing Private Data with Multiple Non-Colluding Adversaries CoRR Theodoros Rekatsinas, Amol Deshpande, Ashwin Machanavajjhala
2012	—	`conf`	Local structure and determinism in probabilistic databases. SIGMOD Conference Theodoros Rekatsinas, Amol Deshpande, Lise Getoor