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Theodoros Rekatsinas
Theodoros I. Rekatsinas
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2020 – today
- 2024
- [c31]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: 3782-3803 - [c30]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 - [i31]Zifan Liu, Amin Karbasi, Theodoros Rekatsinas:
TSDS: Data Selection for Task-Specific Model Finetuning. CoRR abs/2410.11303 (2024) - 2023
- [j12]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): 126-139 (2023) - [j11]Jason Mohoney
, Anil Pacaci
High-Throughput Vector Similarity Search in Knowledge Graphs. Proc. ACM Manag. Data 1(2): 197:1-197:25 (2023) - [j10]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): 2962-2975 (2023) - [c29]Roger Waleffe
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. EuroSys 2023: 144-161 - [c28]Hongyu Hè
EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy. HotCarbon 2023: 4:1-4:8 - [c27]Wenqi Jiang
Co-design Hardware and Algorithm for Vector Search. SC 2023: 87:1-87:15 - [c26]Ihab F. Ilyas
Growing and Serving Large Open-domain Knowledge Graphs. SIGMOD Conference Companion 2023: 253-259 - [i30]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 abs/2304.01926 (2023) - [i29]Ihab F. Ilyas, J. P. Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, Chiraag Sumanth:
Growing and Serving Large Open-domain Knowledge Graphs. CoRR abs/2305.09464 (2023) - [i28]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. CoRR abs/2305.18424 (2023) - [i27]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 abs/2306.11182 (2023) - [i26]Hongyu Hè, Michal Friedman, Theodoros Rekatsinas:
EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy. CoRR abs/2307.05647 (2023) - [i25]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 abs/2309.11669 (2023) - [i24]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 abs/2312.09424 (2023) - 2022
- [j9]Ihab F. Ilyas
Machine Learning and Data Cleaning: Which Serves the Other? ACM J. Data Inf. Qual. 14(3): 13:1-13:11 (2022) - [j8]Zifan Liu
Picket: guarding against corrupted data in tabular data during learning and inference. VLDB J. 31(5): 927-955 (2022) - [c25]Yunjia Zhang, Yannis Chronis, Jignesh M. Patel, Theodoros Rekatsinas:
Can Transfer Learning be used to build a Query Optimizer? CIDR 2022 - [c24]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: 2259-2272 - [i23]Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman:
Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine. CoRR abs/2202.02365 (2022) - [i22]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 abs/2204.07309 (2022) - 2021
- [j7]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): 2759-2762 (2021) - [j6]Sahaana Suri, Ihab F. Ilyas, Christopher Ré, Theodoros Rekatsinas:
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins. Proc. VLDB Endow. 15(3): 699-712 (2021) - [c23]Zifan Liu, Jongho Park, Theodoros Rekatsinas, Christos Tzamos:
On Robust Mean Estimation under Coordinate-level Corruption. ICML 2021: 6914-6924 - [c22]Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, Shivaram Venkataraman:
Marius: Learning Massive Graph Embeddings on a Single Machine. OSDI 2021: 533-549 - [i21]Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman:
Learning Massive Graph Embeddings on a Single Machine. CoRR abs/2101.08358 (2021) - [i20]Sahaana Suri, Ihab F. Ilyas, Christopher Ré, Theodoros Rekatsinas:
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins. CoRR abs/2106.01501 (2021) - 2020
- [c21]Ankur Goswami, Akshata Bhat, Hadar Ohana, Theodoros Rekatsinas:
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion. EMNLP (Findings) 2020: 1263-1276 - [c20]Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha:
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. ICML 2020: 1939-1951 - [c19]Richard Wu, Aoqian Zhang, Ihab F. Ilyas, Theodoros Rekatsinas:
Attention-based Learning for Missing Data Imputation in HoloClean. MLSys 2020 - [c18]Yunjia Zhang, Zhihan Guo, Theodoros Rekatsinas:
A Statistical Perspective on Discovering Functional Dependencies in Noisy Data. SIGMOD Conference 2020: 861-876 - [i19]Zifan Liu, Jongho Park, Nils Palumbo, Theodoros Rekatsinas, Christos Tzamos:
Robust Mean Estimation under Coordinate-level Corruption. CoRR abs/2002.04137 (2020) - [i18]Deepan Das, Haley Massa, Abhimanyu Kulkarni, Theodoros Rekatsinas:
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation. CoRR abs/2006.03810 (2020) - [i17]Zifan Liu, Zhechun Zhou, Theodoros Rekatsinas:
Picket: Self-supervised Data Diagnostics for ML Pipelines. CoRR abs/2006.04730 (2020) - [i16]Alireza Heidari, George Michalopoulos, Shrinu Kushagra, Ihab F. Ilyas, Theodoros Rekatsinas:
Record fusion: A learning approach. CoRR abs/2006.10208 (2020) - [i15]Roger Waleffe, Theodoros Rekatsinas:
Principal Component Networks: Parameter Reduction Early in Training. CoRR abs/2006.13347 (2020) - [i14]Ankur Goswami, Akshata Bhat, Hadar Ohana, Theodoros Rekatsinas:
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion. CoRR abs/2010.06804 (2020)
2010 – 2019
- 2019
- [j5]Theodoros Rekatsinas
Opportunities for Data Management Research in the Era of Horizontal AI/ML. Proc. VLDB Endow. 12(12): 2323-2324 (2019) - [c17]Theodoros Rekatsinas
CRUX: Adaptive Querying for Efficient Crowdsourced Data Extraction. CIKM 2019: 841-850 - [c16]Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas
A Formal Framework for Probabilistic Unclean Databases. ICDT 2019: 6:1-6:18 - [c15]Xin Luna Dong, Theodoros Rekatsinas
Data Integration and Machine Learning: A Natural Synergy. KDD 2019: 3193-3194 - [c14]Alireza Heidari
HoloDetect: Few-Shot Learning for Error Detection. SIGMOD Conference 2019: 829-846 - [c13]Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas
Approximate Inference in Structured Instances with Noisy Categorical Observations. UAI 2019: 412-421 - [i13]Alireza Heidari, Joshua McGrath, Ihab F. Ilyas, Theodoros Rekatsinas:
HoloDetect: Few-Shot Learning for Error Detection. CoRR abs/1904.02285 (2019) - [i12]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
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i11]Zhihan Guo, Theodoros Rekatsinas:
Learning Functional Dependencies with Sparse Regression. CoRR abs/1905.01425 (2019) - [i10]Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha:
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. CoRR abs/1905.12813 (2019) - [i9]Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas:
Approximate Inference in Structured Instances with Noisy Categorical Observations. CoRR abs/1907.00141 (2019) - [i8]Ankur Goswami, Joshua McGrath, Shanan Peters, Theodoros Rekatsinas:
Fine-Grained Object Detection over Scientific Document Images with Region Embeddings. CoRR abs/1910.12462 (2019) - 2018
- [j4]Luna Dong, Theodoros Rekatsinas
Data Integration and Machine Learning: A Natural Synergy. Proc. VLDB Endow. 11(12): 2094-2097 (2018) - [c12]Sidharth Mudgal, Han Li, Theodoros Rekatsinas
Deep Learning for Entity Matching: A Design Space Exploration. SIGMOD Conference 2018: 19-34 - [c11]Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas
Fonduer: Knowledge Base Construction from Richly Formatted Data. SIGMOD Conference 2018: 1301-1316 - [c10]Xin Luna Dong, Theodoros Rekatsinas
Data Integration and Machine Learning: A Natural Synergy. SIGMOD Conference 2018: 1645-1650 - [i7]Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas:
A Formal Framework For Probabilistic Unclean Databases. CoRR abs/1801.06750 (2018) - 2017
- [j3]Theodoros Rekatsinas
HoloClean: Holistic Data Repairs with Probabilistic Inference. Proc. VLDB Endow. 10(11): 1190-1201 (2017) - [j2]Theodoros Rekatsinas
Forecasting rare disease outbreaks from open source indicators. Stat. Anal. Data Min. 10(2): 136-150 (2017) - [c9]Theodoros Rekatsinas
SLiMFast: Guaranteed Results for Data Fusion and Source Reliability. SIGMOD Conference 2017: 1399-1414 - [i6]Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, Christopher Ré:
HoloClean: Holistic Data Repairs with Probabilistic Inference. CoRR abs/1702.00820 (2017) - [i5]Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas, Philip Alexander Levis, Christopher Ré:
Fonduer: Knowledge Base Construction from Richly Formatted Data. CoRR abs/1703.05028 (2017) - 2016
- [c8]Theodoros Rekatsinas
SourceSight: Enabling Effective Source Selection. SIGMOD Conference 2016: 2157-2160 - 2015
- [b1]Theodoros Rekatsinas
Quality-Aware Data Source Management. University of Maryland, College Park, MD, USA, 2015 - [c7]Theodoros Rekatsinas, Xin Luna Dong, Lise Getoor, Divesh Srivastava:
Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration. CIDR 2015 - [c6]Xinran He, Theodoros Rekatsinas
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. ICML 2015: 871-880 - [c5]Theodoros Rekatsinas
SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources. SDM 2015: 379-387 - [c4]Anja Gruenheid, Donald Kossmann, Theodoros Rekatsinas
StoryPivot: Comparing and Contrasting Story Evolution. SIGMOD Conference 2015: 1415-1420 - [i4]Theodoros Rekatsinas, Amol Deshpande, Aditya G. Parameswaran:
CrowdGather: Entity Extraction over Structured Domains. CoRR abs/1502.06823 (2015) - [i3]Manas Joglekar, Theodoros Rekatsinas, Hector Garcia-Molina, Aditya G. Parameswaran, Christopher Ré:
Exploiting Features for Data Source Quality Estimation. CoRR abs/1512.06474 (2015) - 2014
- [c3]Theodoros Rekatsinas
Characterizing and selecting fresh data sources. SIGMOD Conference 2014: 919-930 - 2013
- [j1]Theodoros Rekatsinas
A SPARSI: Partitioning Sensitive Data amongst Multiple Adversaries. Proc. VLDB Endow. 6(13): 1594-1605 (2013) - [i2]Theodoros Rekatsinas, Amol Deshpande, Ashwin Machanavajjhala:
On Sharing Private Data with Multiple Non-Colluding Adversaries. CoRR abs/1302.6556 (2013) - [i1]Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor:
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss. CoRR abs/1303.1733 (2013) - 2012
- [c2]Theodoros Rekatsinas
Local structure and determinism in probabilistic databases. SIGMOD Conference 2012: 373-384 - 2010
- [c1]John N. Karigiannis, Theodoros I. Rekatsinas, Costas S. Tzafestas
Fuzzy rule based neuro-dynamic programming for mobile robot skill acquisition on the basis of a nested multi-agent architecture. ROBIO 2010: 312-319
Coauthor Index
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last updated on 2024-11-25 22:48 CET by the dblp team
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