Theodoros Rekatsinas

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 61

Top Venues

University of Maryland, College Park, MD, USA (PHD)

Education History

University of Maryland, College Park, MD, USA

PHD

Publications

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
Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation. LREC/COLING (2024)
Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis, Amin Karbasi, Dionysios S. Kalogerias, Nezihe Merve Gürel, Theodoros Rekatsinas
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning. ICLR (2024)
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
Open Domain Knowledge Extraction for Knowledge Graphs. CoRR (2023)
Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. EuroSys (2023)
Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis, Amin Karbasi, Dionysis S. Kalogerias, Nezihe Merve Gürel, Theodoros Rekatsinas
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning. CoRR (2023)
Ihab F. Ilyas, JP Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, Chiraag Sumanth
Growing and Serving Large Open-domain Knowledge Graphs. SIGMOD Conference Companion (2023)
Hongyu Ren, Ali Mousavi, Anil Pacaci, Shihabur Rahman Chowdhury, Jason Mohoney, Ihab F. Ilyas, Yunyao Li, Theodoros Rekatsinas
Fact Ranking over Large-Scale Knowledge Graphs with Reasoning Embedding Models. IEEE Data Eng. Bull. 46 (2) (2023)
Yunjia Zhang, Yannis Chronis, Jignesh M. Patel, Theodoros Rekatsinas
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis. Proc. VLDB Endow. 16 (11) (2023)
Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Ali Mousavi, Ihab F. Ilyas, Umar Farooq Minhas, Jeffrey Pound, Theodoros Rekatsinas
High-Throughput Vector Similarity Search in Knowledge Graphs. CoRR (2023)
Hongyu Hè, Michal Friedman, Theodoros Rekatsinas
EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy. CoRR (2023)
Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso
Co-design Hardware and Algorithm for Vector Search. SC (2023)
Hongyu Hè, Michal Friedman, Theodoros Rekatsinas
EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy. HotCarbon (2023)
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
Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation. CoRR (2023)
Ihab F. Ilyas, JP Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, Chiraag Sumanth
Growing and Serving Large Open-domain Knowledge Graphs. CoRR (2023)
Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso
Co-design Hardware and Algorithm for Vector Search. CoRR (2023)
Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Ali Mousavi, Ihab F. Ilyas, Umar Farooq Minhas, Jeffrey Pound, Theodoros Rekatsinas
High-Throughput Vector Similarity Search in Knowledge Graphs. Proc. ACM Manag. Data 1 (2) (2023)
Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine. CoRR (2022)
Ihab F. Ilyas, Theodoros Rekatsinas, Vishnu Konda, Jeffrey Pound, Xiaoguang Qi, Mohamed A. Soliman
Saga: A Platform for Continuous Construction and Serving of Knowledge at Scale. SIGMOD Conference (2022)
Ihab F. Ilyas, Theodoros Rekatsinas
Machine Learning and Data Cleaning: Which Serves the Other? ACM J. Data Inf. Qual. 14 (3) (2022)
Ihab F. Ilyas, Theodoros Rekatsinas, Vishnu Konda, Jeffrey Pound, Xiaoguang Qi, Mohamed A. Soliman
Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale. CoRR (2022)
Yunjia Zhang, Yannis Chronis, Jignesh M. Patel, Theodoros Rekatsinas
Can Transfer Learning be used to build a Query Optimizer? CIDR (2022)
Zifan Liu, Zhechun Zhou, Theodoros Rekatsinas
Picket: guarding against corrupted data in tabular data during learning and inference. VLDB J. 31 (5) (2022)
Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman
Learning Massive Graph Embeddings on a Single Machine. CoRR (2021)
Anders Carlsson, Anze Xie, Jason Mohoney, Roger Waleffe, Shanan Peters, Theodoros Rekatsinas, Shivaram Venkataraman
Demonstration of Marius: Graph Embeddings with a Single Machine. Proc. VLDB Endow. 14 (12) (2021)
Zifan Liu, Jongho Park, Theodoros Rekatsinas, Christos Tzamos
On Robust Mean Estimation under Coordinate-level Corruption. ICML (2021)
Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, Shivaram Venkataraman
Marius: Learning Massive Graph Embeddings on a Single Machine. OSDI (2021)
Sahaana Suri, Ihab F. Ilyas, Christopher Ré, Theodoros Rekatsinas
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins. CoRR (2021)
Sahaana Suri, Ihab F. Ilyas, Christopher Ré, Theodoros Rekatsinas
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins. Proc. VLDB Endow. 15 (3) (2021)
Richard Wu, Aoqian Zhang, Ihab F. Ilyas, Theodoros Rekatsinas
Attention-based Learning for Missing Data Imputation in HoloClean. MLSys (2020)
Zifan Liu, Zhechun Zhou, Theodoros Rekatsinas
Picket: Self-supervised Data Diagnostics for ML Pipelines. CoRR (2020)
Deepan Das, Haley Massa, Abhimanyu Kulkarni, Theodoros Rekatsinas
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation. CoRR (2020)
Ankur Goswami, Akshata Bhat, Hadar Ohana, Theodoros Rekatsinas
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion. CoRR (2020)
Alireza Heidari, George Michalopoulos, Shrinu Kushagra, Ihab F. Ilyas, Theodoros Rekatsinas
Record fusion: A learning approach. CoRR (2020)
Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. ICML (2020)
Roger Waleffe, Theodoros Rekatsinas
Principal Component Networks: Parameter Reduction Early in Training. CoRR (2020)
Zifan Liu, Jongho Park, Nils Palumbo, Theodoros Rekatsinas, Christos Tzamos
Robust Mean Estimation under Coordinate-level Corruption. CoRR (2020)
Yunjia Zhang, Zhihan Guo, Theodoros Rekatsinas
A Statistical Perspective on Discovering Functional Dependencies in Noisy Data. SIGMOD Conference (2020)
Ankur Goswami, Akshata Bhat, Hadar Ohana, Theodoros Rekatsinas
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion. EMNLP (Findings) (2020)
Theodoros Rekatsinas, Sudeepa Roy, Manasi Vartak, Ce Zhang, Neoklis Polyzotis
Opportunities for Data Management Research in the Era of Horizontal AI/ML. Proc. VLDB Endow. 12 (12) (2019)
Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas
Approximate Inference in Structured Instances with Noisy Categorical Observations. CoRR (2019)
Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas
Approximate Inference in Structured Instances with Noisy Categorical Observations. UAI (2019)
Alireza Heidari, Joshua McGrath, Ihab F. Ilyas, Theodoros Rekatsinas
HoloDetect: Few-Shot Learning for Error Detection. CoRR (2019)
Zhihan Guo, Theodoros Rekatsinas
Learning Functional Dependencies with Sparse Regression. CoRR (2019)
Alireza Heidari, Joshua McGrath, Ihab F. Ilyas, Theodoros Rekatsinas
HoloDetect: Few-Shot Learning for Error Detection. SIGMOD Conference (2019)
Ankur Goswami, Joshua McGrath, Shanan Peters, Theodoros Rekatsinas
Fine-Grained Object Detection over Scientific Document Images with Region Embeddings. CoRR (2019)
Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas
A Formal Framework for Probabilistic Unclean Databases. ICDT (2019)
Xin Luna Dong, Theodoros Rekatsinas
Data Integration and Machine Learning: A Natural Synergy. KDD (2019)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric 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 G. 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 R. 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
SysML: The New Frontier of Machine Learning Systems. CoRR (2019)
Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. CoRR (2019)
Theodoros Rekatsinas, Amol Deshpande, Aditya G. Parameswaran
CRUX: Adaptive Querying for Efficient Crowdsourced Data Extraction. CIKM (2019)
Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas, Philip Alexander Levis, Christopher Ré
Fonduer: Knowledge Base Construction from Richly Formatted Data. SIGMOD Conference (2018)
Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, Vijay Raghavendra
Deep Learning for Entity Matching: A Design Space Exploration. SIGMOD Conference (2018)
Luna Dong, Theodoros Rekatsinas
Data Integration and Machine Learning: A Natural Synergy. Proc. VLDB Endow. 11 (12) (2018)
Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas
A Formal Framework For Probabilistic Unclean Databases. CoRR (2018)
Xin Luna Dong, Theodoros Rekatsinas
Data Integration and Machine Learning: A Natural Synergy. SIGMOD Conference (2018)
Theodoros Rekatsinas, Manas Joglekar, Hector Garcia-Molina, Aditya G. Parameswaran, Christopher Ré
SLiMFast: Guaranteed Results for Data Fusion and Source Reliability. SIGMOD Conference (2017)
Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, Christopher Ré
HoloClean: Holistic Data Repairs with Probabilistic Inference. Proc. VLDB Endow. 10 (11) (2017)
Theodoros Rekatsinas, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, Naren Ramakrishnan
Forecasting rare disease outbreaks from open source indicators. Stat. Anal. Data Min. 10 (2) (2017)
Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas, Philip Alexander Levis, Christopher Ré
Fonduer: Knowledge Base Construction from Richly Formatted Data. CoRR (2017)
Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, Christopher Ré
HoloClean: Holistic Data Repairs with Probabilistic Inference. CoRR (2017)
Theodoros Rekatsinas, Amol Deshpande, Xin Luna Dong, Lise Getoor, Divesh Srivastava
SourceSight: Enabling Effective Source Selection. SIGMOD Conference (2016)
Manas Joglekar, Theodoros Rekatsinas, Hector Garcia-Molina, Aditya G. Parameswaran, Christopher Ré
Exploiting Features for Data Source Quality Estimation. CoRR (2015)
Anja Gruenheid, Donald Kossmann, Theodoros Rekatsinas, Divesh Srivastava
StoryPivot: Comparing and Contrasting Story Evolution. SIGMOD Conference (2015)
Theodoros Rekatsinas, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, Naren Ramakrishnan
SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources. SDM (2015)
Xinran He, Theodoros Rekatsinas, James R. Foulds, Lise Getoor, Yan Liu
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. ICML (2015)
Theodoros Rekatsinas, Amol Deshpande, Aditya G. Parameswaran
CrowdGather: Entity Extraction over Structured Domains. CoRR (2015)
Theodoros Rekatsinas, Xin Luna Dong, Lise Getoor, Divesh Srivastava
Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration. CIDR (2015)
Theodoros Rekatsinas, Xin Luna Dong, Divesh Srivastava
Characterizing and selecting fresh data sources. SIGMOD Conference (2014)
Theodoros Rekatsinas, Amol Deshpande, Ashwin Machanavajjhala
A SPARSI: Partitioning Sensitive Data amongst Multiple Adversaries. Proc. VLDB Endow. 6 (13) (2013)
Theodoros Rekatsinas, Amol Deshpande, Ashwin Machanavajjhala
On Sharing Private Data with Multiple Non-Colluding Adversaries CoRR (2013)
Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss CoRR (2013)
Theodoros Rekatsinas, Amol Deshpande, Lise Getoor
Local structure and determinism in probabilistic databases. SIGMOD Conference (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Theodoros Rekatsinas