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. SchemaPile: A Large Collection of Relational Database Schemas. ACM SIGMOD, 2024.

. Red Onions, Soft Cheese and Data: From Food Safety to Data Traceability for Responsible AI. IEEE Data Engineering Bulletin (Special Issue on Data-Centric Responsible AI), 2024.

. Canonpipe: Data Debugging with Shapley Importance over Machine Learning Pipelines. International Conference on Learning Representations (ICLR), 2024.

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. Automated Data Cleaning Can Hurt Fairness in Machine Learning-based Decision Making. IEEE Transactions on Knowledge and Data Engineering (TKDE), Special Issue for Best and Innovation Papers from ICDE’23, 2024.

. Assisted Design of Data Science Pipelines. The VLDB Journal — The International Journal on Very Large Data Bases, 2024.

. Etude - Evaluating the Inference Latency of Session-Based Recommendation Models at Scale. International Conference on Data Engineering (ICDE), 2024.

. Domain Generalization in Time Series Forecasting. ACM Transactions on Knowledge Discovery from Data (TKDD), 2024.

. Improving Retrieval-Augmented Large Language Models via Data Importance Learning. [arxiv preprint], 2023.

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. Towards Declarative Systems for Data-Centric Machine Learning. Data-Centric Machine Learning Research (DMLR) Workshop at ICML, 2023.

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. mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over?. VLDB (demo), 2023.

. Forget Me Now - Fast and Exact Unlearning in Neighborhood-Based Recommendation. ACM SIGIR, 2023.

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. On the Impact of Outlier Bias on User Clicks. ACM SIGIR, 2023.

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. Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines. ACM SIGMOD, 2023.

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. Proactively Screening Machine Learning Pipelines with ArgusEyes. ACM SIGMOD (demo), 2023.

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. Automated Data Cleaning Can Hurt Fairness in Machine Learning-based Decision Making. International Conference on Data Engineering (ICDE), 2023.

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. How to Make an Outlier? Studying the Effect of Presentational Features on the Outlierness of Items in Product Search Results. ACM Conference on Human Information Interaction and Retrieval (CHIIR), 2022.

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. Reconstructing and Querying ML Pipeline Intermediates. Conference on Innovative Data Systems Research (CIDR, abstract), 2022.

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. Towards Parameter-Efficient Automation of Data Wrangling Tasks with Prefix-Tuning. Table Representation Learning workshop at NeurIPS, 2022.

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. A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping. ACM International Conference on Web Search and Data Mining (WSDM), 2022.

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. DORIAN in action: Assisted Design of Data Science Pipelines. VLDB (demo), 2022.

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. Responsible Data Management. Communications of the ACM, 2022.

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. Letter from the Special Issue Editor. Special issue on “Directions Towards GDPR-Compliant Data Systems and Applications” of the IEEE Data Engineering Bulletin (Vol 45, Issue 1), 2022.

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. Towards Data-Centric What-If Analysis for Native Machine Learning Pipelines. Data Management for End-to-End Machine Learning workshop at ACM SIGMOD, 2022.

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. ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping. ACM SIGIR, 2022.

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. GitSchemas: A Schema Dataset for Automating Relational Data Preparation Tasks. Databases for Machine Learning workshop at ICDE, 2022.

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. Serving Low-Latency Session-Based Recommendations at bol.com. ECIR (industry talk), 2022.

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. Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale. ACM SIGMOD, 2021.

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. Data Distribution Debugging in Machine Learning Pipelines. The VLDB Journal — The International Journal on Very Large Data Bases (Special Issue on Data Science for Responsible Data Management), 2021.

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. Parameter Efficient Deep Probabilistic Forecasting. International Journal of Forecasting, 2021.

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. Screening Native Machine Learning Pipelines with ArgusEyes. Conference on Innovative Data Systems Research (CIDR, abstract), 2021.

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. Understanding and Mitigating the Effect of Outliers in Fair Ranking. ACM International Conference on Web Search and Data Mining (WSDM), 2021.

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. Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items. Workshop on Online Recommender Systems and User Modeling at ACM RecSys, 2021.

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. Understanding Multi-channel Customer Behavior in Retail. ACM Conference on Information and Knowledge Management (CIKM), 2021.

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. Towards Efficient Machine Unlearning via Incremental View Maintenance. Workshop on Challenges in Deploying and Monitoring ML Systems at the International Conference on Machine Learning (ICML), 2021.

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. DuckDQ: Data Quality Assertions for Machine Learning Pipelines. Workshop on Challenges in Deploying and Monitoring ML Systems at the International Conference on Machine Learning (ICML), 2021.

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. Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression. ACM SIGKDD, 2021.

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. Letter from the Special Issue Editor. Special issue on “Data validation for machine learning models and applications” of the IEEE Data Engineering Bulletin (Vol 44, Issue 1), 2021.

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. HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. ACM SIGMOD, 2021.

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. Learnings from a Retail Recommendation System on Billions of Interactions at bol.com. International Conference on Data Engineering (ICDE), 2021.

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. mlinspect: a Data Distribution Debugger for Machine Learning Pipelines. ACM SIGMOD (demo), 2021.

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. Automating Data Quality Validation for Dynamic Data Ingestion. International Conference on Extending Database Technology (EDBT), 2021.

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. Jenga - A Framework to Study the Impact of Data Errors on the Predictions of Machine Learning Models. International Conference on Extending Database Technology (EDBT), 2020.

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. Taming Technical Bias in Machine Learning Pipelines. IEEE Data Engineering Bulletin (Special Issue on Interdisciplinary Perspectives on Fairness and Artificial Intelligence Systems), 2020.

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. Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. Conference on Innovative Data Systems Research (CIDR), 2020.

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. RetaiL: Open your own grocery store to reduce waste. NeurIPS (demonstration track), 2020.

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. Technical Perspective: Query Optimization for Faster Deep CNN Explanations. ACM SIGMOD Record (Vol 49, Issue 1), 2020.

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. Demand Forecasting in the Presence of Privileged Information. Workshop on Advanced Analytics and Learning on Temporal Data at ECML/PKDD, 2020.

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. A Comparison of Supervised Learning to Match Methods for Product Search. eCommerce workshop at SIGIR, 2020.

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. Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers. eCommerce workshop at SIGIR, 2020.

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. Apache Mahout: Machine Learning on Distributed Dataflow Systems. Journal of Machine Learning Research (JMLR), open source software track, 2020.

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. AlphaJoin: Join Order Selection à la AlphaGo. PhD workshop at VLDB, 2020.

. Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning. Human-In-the-Loop Data Analytics workshop at ACM SIGMOD, 2020.

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. HDDse: Enabling High-Dimensional Disk State Embedding for Generic Failure Detection of Heterogeneous Disks in Large Data Centers. USENIX Annual Technical Conference (ATC), 2020.

. Elastic Machine Learning Algorithms in Amazon SageMaker. ACM SIGMOD, 2020.

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. Tier-Scrubbing: An Adaptive and Tiered Disk Scrubbing Scheme with Improved MTTD and Reduced Cost. Design Automation Conference (DAC), 2020.

. Towards Unsupervised Data Quality Validation on Dynamic Data. Workshop on Explainability for Trustworthy ML Pipelines at EDBT, 2020.

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. Towards Automated ML Model Monitoring: Measure, Improve and Quantify Data Quality. ML Ops workshop at the Conference on Machine Learning and Systems (MLSys), 2020.

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. Tier-Scrubbing: An Adaptive and Tiered Disk Scrubbing Scheme. USENIX Conference on File and Storage Technologies (FAST), work-in-progress track., 2020.

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. Exploring Monte Carlo Tree Search for Join Order Selection. North East Database Day, 2020.

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. Learning to Validate the Predictions of Black Box Classifiers on Unseen Data. ACM SIGMOD, 2020.

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. FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions. International Conference on Extending Database Technology (EDBT), 2020.

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. Zooming Out on an Evolving Graph. International Conference on Extending Database Technology (EDBT), 2020.

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. 'Amnesia' - A Selection of Machine Learning Models That Can Forget User Data Very Fast. Conference on Innovative Data Systems Research (CIDR), 2020.

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. DataWig - Missing Value Imputation for Tables. Journal of Machine Learning Research (JMLR), open source software track, 2019.

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. An Intermediate Representation for Optimizing Machine Learning Pipelines. International Conference on Very Large Databases (VLDB), 2019.

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. AdaBench - Towards an Industry Standard Benchmark for Advanced Analytics. TPC Technology Conference on Performance Evaluation & Benchmarking (TPCTC), 2019.

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. 'Amnesia' - Towards Machine Learning Models That Can Forget User Data Very Fast. Workshop on Applied AI for Database Systems and Applications (AIDB) at VLDB, 2019.

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. Efficient Incremental Cooccurrence Analysis for Item-Based Collaborative Filtering. International Conference on Scientific and Statistical Database Management (SSDBM), 2019.

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. Learning to Validate the Predictions of Black Box Machine Learning Models on Unseen Data. Human-In-the-Loop Data Analytics workshop at ACM SIGMOD, 2019.

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. DEEM 2019: Workshop on Data Management for End-to-End Machine Learning. ACM SIGMOD (workshop summary), 2019.

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. Differential Data Quality Verification on Partitioned Data. International Conference on Data Engineering (ICDE), 2019.

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. Unit Testing Data with Deequ. ACM SIGMOD (demo), 2019.

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. Data-Related Challenges in End-to-End Machine Learning. North East Database Day, 2019.

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. On Challenges in Machine Learning Model Management. IEEE Data Engineering Bulletin, 2018.

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. Deequ - Data Quality Validation for Machine Learning Pipelines. Machine Learning Systems workshop at the conference on Neural Information Processing Systems (NeurIPS), 2018.

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. Deep Learning for Missing Value Imputation in Tables with Non-Numerical Data. ACM Conference on Information and Knowledge Management (CIKM), 2018.

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. Benchmarking Distributed Data Processing Systems for Machine Learning Workloads. TPC Technology Conference on Performance Evaluation & Benchmarking (TPCTC), 2018.

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. BlockJoin: Efficient Matrix Partitioning Through Joins. International Conference on Very Large Databases (VLDB), 2018.

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. Automating Large-Scale Data Quality Verification. International Conference on Very Large Databases (VLDB), 2018.

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. On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl. Journal of Web Science (JWS), 2018.

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. Automatically Tracking Metadata and Provenance of Machine Learning Experiments. Machine Learning Systems workshop at the conference on Neural Information Processing Systems (NIPS), 2017.

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. Dark Germany: Hidden Patterns of Participation in Online Far-Right Protests Against Refugee Housing. International Conference on Social Informatics (SocInfo), 2017.

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. Probabilistic Demand Forecasting at Scale. International Conference on Very Large Databases (VLDB), 2017.

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. Dark Germany: Hidden Patterns of Participation in Online Far-Right Protests Against Refugee Housing. ACM Web Science Conference (WebSci), 2017.

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. Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Systems. Fachtagung für Business, Technologie und Web (BTW), 2017.

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. Structural Patterns in the Rise of Germany’s New Right on Facebook. Data Mining in Politics workshop at the International Conference on Data Mining (ICDM), 2016.

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. Samsara: Declarative Machine Learning on Distributed Dataflow Systems. Machine Learning Systems workshop at the conference on Neural Information Processing Systems (NIPS), 2016.

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. Doubly stochastic large scale kernel learning with the empirical kernel map. arxiv, 2016.

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. Predicting Political Party Affiliation from Text. International Conference on the Advances in Computational Analysis of Political Text (PolText), 2016.

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. Tracking The Trackers: A Large-Scale Analysis of Embedded Web Trackers. AAAI International Conference on Web and Social Media (ICWSM), 2016.

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. Scaling Data Mining in Massively Parallel Dataflow Systems. Technische Universität Berlin, 2015.

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. Optimistic Recovery for Iterative Dataflows in Action. ACM SIGMOD (demo), 2015.

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. Efficient Sample Generation for Scalable Meta Learning. IEEE International Conference on Data Engineering (ICDE), 2015.

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. Factorbird - a Parameter Server Approach to Distributed Matrix Factorization. Distributed Machine Learning and Matrix Computations workshop at the conference on Neural Information Processing Systems (NIPS), 2014.

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. The Stratosphere platform for big data analytics. The VLDB Journal — The International Journal on Very Large Data Bases, 2014.

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. Scaling Data Mining in Massively Parallel Dataflow Systems. PhD Symposium at ACM SIGMOD, 2014.

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. 'All Roads Lead to Rome:' Optimistic Recovery for Distributed Iterative Data Processing. ACM Conference on Information and Knowledge Management (CIKM), 2013.

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. Distributed Matrix Factorization with MapReduce using a series of Broadcast-Joins. ACM Conference on Recommender Systems (RecSys), 2013.

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. Iterative Parallel Data Processing with Stratosphere: An Inside Look. ACM SIGMOD (demo), 2013.

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. Collaborative Filtering with Apache Mahout. Recommender Systems Challenge Workshop in conjunction with ACM RecSys, 2012.

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. Scalable Similarity-Based Neighborhood Methods with MapReduce. ACM Conference on Recommender Systems (RecSys), 2012.

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