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 (ROBIO, 2010)

Local structure and determinism in probabilistic databases. - Theodoros Rekatsinas; Amol Deshpande; Lise Getoor (SIGMOD Conference, 2012)

On Sharing Private Data with Multiple Non-Colluding Adversaries - Theodoros Rekatsinas; Amol Deshpande; Ashwin Machanavajjhala (CoRR, 2013)

Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss - Ben London 001; Theodoros Rekatsinas; Bert Huang; Lise Getoor (CoRR, 2013)

A SPARSI: Partitioning Sensitive Data amongst Multiple Adversaries. - Theodoros Rekatsinas; Amol Deshpande; Ashwin Machanavajjhala (Proc. VLDB Endow., 2013)

Characterizing and selecting fresh data sources. - Theodoros Rekatsinas; Xin Luna Dong; Divesh Srivastava (SIGMOD Conference, 2014)

Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration. - Theodoros Rekatsinas; Xin Luna Dong; Lise Getoor; Divesh Srivastava (CIDR, 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 002 (ICML, 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 (SDM, 2015)

StoryPivot: Comparing and Contrasting Story Evolution. - Anja Gruenheid; Donald Kossmann; Theodoros Rekatsinas; Divesh Srivastava (SIGMOD Conference, 2015)

Exploiting Features for Data Source Quality Estimation. - Manas Joglekar; Theodoros Rekatsinas; Hector Garcia-Molina; Aditya G. Parameswaran; Christopher Ré (CoRR, 2015)

CrowdGather: Entity Extraction over Structured Domains. - Theodoros Rekatsinas; Amol Deshpande; Aditya G. Parameswaran (CoRR, 2015)

Quality-Aware Data Source Management. - Theodoros Rekatsinas (2015)

SourceSight: Enabling Effective Source Selection. - Theodoros Rekatsinas; Amol Deshpande; Xin Luna Dong; Lise Getoor; Divesh Srivastava (SIGMOD Conference, 2016)

SLiMFast: Guaranteed Results for Data Fusion and Source Reliability. - Theodoros Rekatsinas; Manas Joglekar; Hector Garcia-Molina; Aditya G. Parameswaran; Christopher Ré (SIGMOD Conference, 2017)

HoloClean: Holistic Data Repairs with Probabilistic Inference. - Theodoros Rekatsinas; Xu Chu; Ihab F. Ilyas; Christopher Ré (Proc. VLDB Endow., 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)

Fonduer: Knowledge Base Construction from Richly Formatted Data. - Sen Wu 002; Luke Hsiao; Xiao Cheng; Braden Hancock; Theodoros Rekatsinas; Philip Alexander Levis; Christopher Ré (SIGMOD Conference, 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 (SIGMOD Conference, 2018)

Data Integration and Machine Learning: A Natural Synergy. - Luna Dong; Theodoros Rekatsinas (Proc. VLDB Endow., 2018)

CRUX: Adaptive Querying for Efficient Crowdsourced Data Extraction. - Theodoros Rekatsinas; Amol Deshpande; Aditya G. Parameswaran (CIKM, 2019)

A Formal Framework for Probabilistic Unclean Databases. - Christopher De Sa; Ihab F. Ilyas; Benny Kimelfeld; Christopher Ré; Theodoros Rekatsinas (ICDT, 2019)

HoloDetect: Few-Shot Learning for Error Detection. - Alireza Heidari; Joshua McGrath; Ihab F. Ilyas; Theodoros Rekatsinas (SIGMOD Conference, 2019)

Approximate Inference in Structured Instances with Noisy Categorical Observations. - Alireza Heidari; Ihab F. Ilyas; Theodoros Rekatsinas (UAI, 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; et al. (CoRR, 2019)

Learning Functional Dependencies with Sparse Regression. - Zhihan Guo; Theodoros Rekatsinas (CoRR, 2019)

Fine-Grained Object Detection over Scientific Document Images with Region Embeddings. - Ankur Goswami; Joshua McGrath; Shanan Peters; Theodoros Rekatsinas (CoRR, 2019)

Opportunities for Data Management Research in the Era of Horizontal AI/ML. - Theodoros Rekatsinas; Sudeepa Roy 001; Manasi Vartak; Ce Zhang 001; Neoklis Polyzotis (Proc. VLDB Endow., 2019)

Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion. - Ankur Goswami; Akshata Bhat; Hadar Ohana; Theodoros Rekatsinas (EMNLP (Findings), 2020)

Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. - Amrita Roy Chowdhury 001; Theodoros Rekatsinas; Somesh Jha (ICML, 2020)

Attention-based Learning for Missing Data Imputation in HoloClean. - Richard Wu; Aoqian Zhang; Ihab F. Ilyas; Theodoros Rekatsinas (MLSys, 2020)

A Statistical Perspective on Discovering Functional Dependencies in Noisy Data. - Yunjia Zhang; Zhihan Guo; Theodoros Rekatsinas (SIGMOD Conference, 2020)

Robust Mean Estimation under Coordinate-level Corruption. - Zifan Liu; Jongho Park 004; Nils Palumbo; Theodoros Rekatsinas; Christos Tzamos (CoRR, 2020)

An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation. - Deepan Das; Haley Massa; Abhimanyu Kulkarni; Theodoros Rekatsinas (CoRR, 2020)

Picket: Self-supervised Data Diagnostics for ML Pipelines. - Zifan Liu; Zhechun Zhou; Theodoros Rekatsinas (CoRR, 2020)

Record fusion: A learning approach. - Alireza Heidari; George Michalopoulos; Shrinu Kushagra; Ihab F. Ilyas; Theodoros Rekatsinas (CoRR, 2020)

Principal Component Networks: Parameter Reduction Early in Training. - Roger Waleffe; Theodoros Rekatsinas (CoRR, 2020)

On Robust Mean Estimation under Coordinate-level Corruption. - Zifan Liu; Jongho Park 004; Theodoros Rekatsinas; Christos Tzamos (ICML, 2021)

Marius: Learning Massive Graph Embeddings on a Single Machine. - Jason Mohoney; Roger Waleffe; Henry Xu; Theodoros Rekatsinas; Shivaram Venkataraman (OSDI, 2021)

Learning Massive Graph Embeddings on a Single Machine. - Jason Mohoney; Roger Waleffe; Yiheng Xu; Theodoros Rekatsinas; Shivaram Venkataraman (CoRR, 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)

Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins. - Sahaana Suri; Ihab F. Ilyas; Christopher Ré; Theodoros Rekatsinas (Proc. VLDB Endow., 2021)

Can Transfer Learning be used to build a Query Optimizer? - Yunjia Zhang; Yannis Chronis; Jignesh M. Patel; Theodoros Rekatsinas (CIDR, 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 (SIGMOD Conference, 2022)

Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine. - Roger Waleffe; Jason Mohoney; Theodoros Rekatsinas; Shivaram Venkataraman (CoRR, 2022)

Machine Learning and Data Cleaning: Which Serves the Other? - Ihab F. Ilyas; Theodoros Rekatsinas (ACM J. Data Inf. Qual., 2022)

Picket: guarding against corrupted data in tabular data during learning and inference. - Zifan Liu; Zhechun Zhou; Theodoros Rekatsinas (VLDB J., 2022)

MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. - Roger Waleffe; Jason Mohoney; Theodoros Rekatsinas; Shivaram Venkataraman (EuroSys, 2023)

EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy. - Hongyu Hè; Michal Friedman 001; Theodoros Rekatsinas (HotCarbon, 2023)

Co-design Hardware and Algorithm for Vector Search. - Wenqi Jiang 001; Shigang Li 002; Yu Zhu; Johannes de Fine Licht; Zhenhao He; Runbin Shi; Cédric Renggli; Shuai Zhang 007; Theodoros Rekatsinas; Torsten Hoefler; Gustavo Alonso (SC, 2023)

Growing and Serving Large Open-domain Knowledge Graphs. - Ihab F. Ilyas; J. P. Lacerda; Yunyao Li 001; Umar Farooq Minhas; Ali Mousavi 003; Jeffrey Pound; Theodoros Rekatsinas; Chiraag Sumanth (SIGMOD Conference Companion, 2023)

Open Domain Knowledge Extraction for Knowledge Graphs. - Kun Qian 002; Anton Belyi; Fei Wu; Samira Khorshidi; Azadeh Nikfarjam; Rahul Khot; Yisi Sang; Katherine Luna; Xianqi Chu; Eric Choi; Yash Govind; Chloe Seivwright; et al. (CoRR, 2023)

Fact Ranking over Large-Scale Knowledge Graphs with Reasoning Embedding Models. - Hongyu Ren; Ali Mousavi 003; Anil Pacaci; Shihabur Rahman Chowdhury; Jason Mohoney; Ihab F. Ilyas; Yunyao Li 001; Theodoros Rekatsinas (IEEE Data Eng. Bull., 2023)

High-Throughput Vector Similarity Search in Knowledge Graphs. - Jason Mohoney; Anil Pacaci; Shihabur Rahman Chowdhury; Ali Mousavi 003; Ihab F. Ilyas; Umar Farooq Minhas; Jeffrey Pound; Theodoros Rekatsinas (Proc. ACM Manag. Data, 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)

Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation. - Ali Mousavi 003; Xin Zhan; He Bai 002; Peng Shi 010; Theodoros Rekatsinas; Benjamin Han; Yunyao Li 001; Jeffrey Pound; Joshua M. Susskind; Natalie Schluter; Ihab F. Ilyas; Navdeep Jaitly (LREC/COLING, 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 (ICLR, 2024)

TSDS: Data Selection for Task-Specific Model Finetuning. - Zifan Liu; Amin Karbasi; Theodoros Rekatsinas (NeurIPS, 2024)

Incremental IVF Index Maintenance for Streaming Vector Search. - Jason Mohoney; Anil Pacaci; Shihabur Rahman Chowdhury; Umar Farooq Minhas; Jeffrey Pound; Cédric Renggli; Nima Reyhani; Ihab F. Ilyas; Theodoros Rekatsinas; Shivaram Venkataraman (CoRR, 2024)

MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage. - Yongjun He 004; Roger Waleffe; Zhichao Han 001; Johnu George; Binhang Yuan; Zitao Zhang; Yinan Shan; Yang Zhao; Debojyoti Dutta; Theodoros Rekatsinas; Ce Zhang 001 (ICDE, 2025)

Quake: Adaptive Indexing for Vector Search. - Jason Mohoney; Devesh Sarda; Mengze Tang; Shihabur Rahman Chowdhury; Anil Pacaci; Ihab F. Ilyas; Theodoros Rekatsinas; Shivaram Venkataraman (OSDI, 2025)

Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations. - Cédric Renggli; Ihab F. Ilyas; Theodoros Rekatsinas (CoRR, 2025)

Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks. - Roger Waleffe; Devesh Sarda; Jason Mohoney; Emmanouil-Vasileios Vlatakis-Gkaragkounis; Theodoros Rekatsinas; Shivaram Venkataraman (CoRR, 2025)