Theodoros Rekatsinas

According to our database, Theodoros Rekatsinas authored at least 58 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

TSDS: Data Selection for Task-Specific Model Finetuning.

Zifan Liu, Amin Karbasi, Theodoros Rekatsinas

CoRR, 2024

Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning.

Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis, Amin Karbasi, Dionysios S. Kalogerias, Nezihe Merve Gürel, Theodoros Rekatsinas

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation.

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

Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024

2023

Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis.

Yunjia Zhang, Yannis Chronis, Jignesh M. Patel, Theodoros Rekatsinas

Proc. VLDB Endow., 2023

High-Throughput Vector Similarity Search in Knowledge Graphs.

Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Ali Mousavi, Ihab F. Ilyas, Umar Farooq Minhas, Jeffrey Pound, Theodoros Rekatsinas

Proc. ACM Manag. Data, 2023

Fact Ranking over Large-Scale Knowledge Graphs with Reasoning Embedding Models.

Hongyu Ren, Ali Mousavi, Anil Pacaci, Shihabur Rahman Chowdhury, Jason Mohoney, Ihab F. Ilyas, Yunyao Li, Theodoros Rekatsinas

IEEE Data Eng. Bull., 2023

Open Domain Knowledge Extraction for Knowledge Graphs.

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

CoRR, 2023

Growing and Serving Large Open-domain Knowledge Graphs.

Ihab F. Ilyas, J. P. Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, Chiraag Sumanth

Proceedings of the Companion of the 2023 International Conference on Management of Data, 2023

Co-design Hardware and Algorithm for Vector Search.

Wenqi Jiang, Shigang Li, Yu Zhu, Johannes de Fine Licht, Zhenhao He, Runbin Shi, Cédric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso

Proceedings of the International Conference for High Performance Computing, 2023

EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy.

Hongyu Hè, Michal Friedman, Theodoros Rekatsinas

Proceedings of the 2nd Workshop on Sustainable Computer Systems, 2023

MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks.

Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman

Proceedings of the Eighteenth European Conference on Computer Systems, 2023

2022

Picket: guarding against corrupted data in tabular data during learning and inference.

Zifan Liu, Zhechun Zhou, Theodoros Rekatsinas

VLDB J., 2022

Machine Learning and Data Cleaning: Which Serves the Other?

Ihab F. Ilyas, Theodoros Rekatsinas

ACM J. Data Inf. Qual., 2022

Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine.

Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman

CoRR, 2022

Saga: A Platform for Continuous Construction and Serving of Knowledge at Scale.

Ihab F. Ilyas, Theodoros Rekatsinas, Vishnu Konda, Jeffrey Pound, Xiaoguang Qi, Mohamed A. Soliman

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

Can Transfer Learning be used to build a Query Optimizer?

Yunjia Zhang, Yannis Chronis, Jignesh M. Patel, Theodoros Rekatsinas

Proceedings of the 12th Conference on Innovative Data Systems Research, 2022

2021

Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins.

Sahaana Suri, Ihab F. Ilyas, Christopher Ré, Theodoros Rekatsinas

Proc. VLDB Endow., 2021

Demonstration of Marius: Graph Embeddings with a Single Machine.

Anders Carlsson, Anze Xie, Jason Mohoney, Roger Waleffe, Shanan Peters, Theodoros Rekatsinas, Shivaram Venkataraman

Proc. VLDB Endow., 2021

Learning Massive Graph Embeddings on a Single Machine.

Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman

CoRR, 2021

Marius: Learning Massive Graph Embeddings on a Single Machine.

Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, Shivaram Venkataraman

Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation, 2021

On Robust Mean Estimation under Coordinate-level Corruption.

Zifan Liu, Jongho Park, Theodoros Rekatsinas, Christos Tzamos

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Principal Component Networks: Parameter Reduction Early in Training.

Roger Waleffe, Theodoros Rekatsinas

CoRR, 2020

Record fusion: A learning approach.

Alireza Heidari, George Michalopoulos, Shrinu Kushagra, Ihab F. Ilyas, Theodoros Rekatsinas

CoRR, 2020

Picket: Self-supervised Data Diagnostics for ML Pipelines.

Zifan Liu, Zhechun Zhou, Theodoros Rekatsinas

CoRR, 2020

An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation.

Deepan Das, Haley Massa, Abhimanyu Kulkarni, Theodoros Rekatsinas

CoRR, 2020

Robust Mean Estimation under Coordinate-level Corruption.

Zifan Liu, Jongho Park, Nils Palumbo, Theodoros Rekatsinas, Christos Tzamos

CoRR, 2020

A Statistical Perspective on Discovering Functional Dependencies in Noisy Data.

Yunjia Zhang, Zhihan Guo, Theodoros Rekatsinas

Proceedings of the 2020 International Conference on Management of Data, 2020

Attention-based Learning for Missing Data Imputation in HoloClean.

Richard Wu, Aoqian Zhang, Ihab F. Ilyas, Theodoros Rekatsinas

Proceedings of the Third Conference on Machine Learning and Systems, 2020

Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models.

Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha

Proceedings of the 37th International Conference on Machine Learning, 2020

Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion.

Ankur Goswami, Akshata Bhat, Hadar Ohana, Theodoros Rekatsinas

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

2019

Opportunities for Data Management Research in the Era of Horizontal AI/ML.

Theodoros Rekatsinas, Sudeepa Roy, Manasi Vartak, Ce Zhang, Neoklis Polyzotis

Proc. VLDB Endow., 2019

Fine-Grained Object Detection over Scientific Document Images with Region Embeddings.

Ankur Goswami, Joshua McGrath, Shanan Peters, Theodoros Rekatsinas

CoRR, 2019

Learning Functional Dependencies with Sparse Regression.

Zhihan Guo, Theodoros Rekatsinas

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

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 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 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

CoRR, 2019

Approximate Inference in Structured Instances with Noisy Categorical Observations.

Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

HoloDetect: Few-Shot Learning for Error Detection.

Alireza Heidari, Joshua McGrath, Ihab F. Ilyas, Theodoros Rekatsinas

Proceedings of the 2019 International Conference on Management of Data, 2019

A Formal Framework for Probabilistic Unclean Databases.

Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas

Proceedings of the 22nd International Conference on Database Theory, 2019

CRUX: Adaptive Querying for Efficient Crowdsourced Data Extraction.

Theodoros Rekatsinas, Amol Deshpande, Aditya G. Parameswaran

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

2018

Data Integration and Machine Learning: A Natural Synergy.

Luna Dong, Theodoros Rekatsinas

Proc. VLDB Endow., 2018

Deep Learning for Entity Matching: A Design Space Exploration.

Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, Vijay Raghavendra

Proceedings of the 2018 International Conference on Management of Data, 2018

Fonduer: Knowledge Base Construction from Richly Formatted Data.

Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas, Philip Alexander Levis, Christopher Ré

Proceedings of the 2018 International Conference on Management of Data, 2018

2017

Forecasting rare disease outbreaks from open source indicators.

Theodoros Rekatsinas, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, Naren Ramakrishnan

Stat. Anal. Data Min., 2017

HoloClean: Holistic Data Repairs with Probabilistic Inference.

Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, Christopher Ré

Proc. VLDB Endow., 2017

SLiMFast: Guaranteed Results for Data Fusion and Source Reliability.

Theodoros Rekatsinas, Manas Joglekar, Hector Garcia-Molina, Aditya G. Parameswaran, Christopher Ré

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

2016

SourceSight: Enabling Effective Source Selection.

Theodoros Rekatsinas, Amol Deshpande, Xin Luna Dong, Lise Getoor, Divesh Srivastava

Proceedings of the 2016 International Conference on Management of Data, 2016

2015

Quality-Aware Data Source Management.

Theodoros Rekatsinas

PhD thesis, 2015

CrowdGather: Entity Extraction over Structured Domains.

Theodoros Rekatsinas, Amol Deshpande, Aditya G. Parameswaran

CoRR, 2015

Exploiting Features for Data Source Quality Estimation.

Manas Joglekar, Theodoros Rekatsinas, Hector Garcia-Molina, Aditya G. Parameswaran, Christopher Ré

CoRR, 2015

StoryPivot: Comparing and Contrasting Story Evolution.

Anja Gruenheid, Donald Kossmann, Theodoros Rekatsinas, Divesh Srivastava

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31, 2015

SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources.

Theodoros Rekatsinas, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, Naren Ramakrishnan

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades.

Xinran He, Theodoros Rekatsinas, James R. Foulds, Lise Getoor, Yan Liu

Proceedings of the 32nd International Conference on Machine Learning, 2015

Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration.

Theodoros Rekatsinas, Xin Luna Dong, Lise Getoor, Divesh Srivastava

Proceedings of the Seventh Biennial Conference on Innovative Data Systems Research, 2015

2014

Characterizing and selecting fresh data sources.

Theodoros Rekatsinas, Xin Luna Dong, Divesh Srivastava

Proceedings of the International Conference on Management of Data, 2014

2013

A SPARSI: Partitioning Sensitive Data amongst Multiple Adversaries.

Theodoros Rekatsinas, Amol Deshpande, Ashwin Machanavajjhala

Proc. VLDB Endow., 2013

Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss

Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor

CoRR, 2013

On Sharing Private Data with Multiple Non-Colluding Adversaries

Theodoros Rekatsinas, Amol Deshpande, Ashwin Machanavajjhala

CoRR, 2013

2012

Local structure and determinism in probabilistic databases.

Theodoros Rekatsinas, Amol Deshpande, Lise Getoor

Proceedings of the ACM SIGMOD International Conference on Management of Data, 2012

2010

Fuzzy rule based neuro-dynamic programming for mobile robot skill acquisition on the basis of a nested multi-agent architecture.

John N. Karigiannis, Theodoros I. Rekatsinas, Costas S. Tzafestas

Proceedings of the 2010 IEEE International Conference on Robotics and Biomimetics, 2010