PEPPA: a project for evolutionary predator prey algorithms. - Hendrik Blom; Christiane Küch; Katja Losemann; Chris Schwiegelshohn (GECCO (Companion), 2009)

Solving the Minimum String Cover Problem. - Stefan Canzar; Tobias Marschall; Sven Rahmann; Chris Schwiegelshohn (ALENEX, 2012)

BICO: BIRCH Meets Coresets for k-Means Clustering. - Hendrik Fichtenberger; Marc Gillé; Melanie Schmidt 001; Chris Schwiegelshohn; Christian Sohler (ESA, 2013)

Sublinear Estimation of Weighted Matchings in Dynamic Data Streams. - Marc Bury; Chris Schwiegelshohn (ESA, 2015)

The Power of Migration for Online Slack Scheduling. - Chris Schwiegelshohn; Uwe Schwiegelshohn (ESA, 2016)

Diameter and k-Center in Sliding Windows. - Vincent Cohen-Addad; Chris Schwiegelshohn; Christian Sohler (ICALP, 2016)

Efficient Similarity Search in Dynamic Data Streams. - Marc Bury; Chris Schwiegelshohn (CoRR, 2016)

On the Local Structure of Stable Clustering Instances. - Vincent Cohen-Addad; Chris Schwiegelshohn (FOCS, 2017)

Algorithmen für datenintensive Graph- und Clusteringprobleme. - Chris Schwiegelshohn (Ausgezeichnete Informatikdissertationen, 2017)

On Finding the Jaccard Center. - Marc Bury; Chris Schwiegelshohn (ICALP, 2017)

One Size Fits All : Effectiveness of Local Search on Structured Data. - Vincent Cohen-Addad; Chris Schwiegelshohn (CoRR, 2017)

On algorithms for large-scale graph and clustering problems - Chris Schwiegelshohn (2017)

Random Projection to Preserve Patient Privacy. - Aris Anagnostopoulos; Fabio Angeletti; Federico Arcangeli; Chris Schwiegelshohn; Andrea Vitaletti (CIKM Workshops, 2018)

On Coresets for Logistic Regression. - Alexander Munteanu; Chris Schwiegelshohn; Christian Sohler; David P. Woodruff (NeurIPS, 2018)

Sketch 'Em All: Fast Approximate Similarity Search for Dynamic Data Streams. - Marc Bury; Chris Schwiegelshohn; Mara Sorella (WSDM, 2018)

Fair Coresets and Streaming Algorithms for Fair k-Means Clustering. - Melanie Schmidt 001; Chris Schwiegelshohn; Christian Sohler (CoRR, 2018)

Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms. - Alexander Munteanu; Chris Schwiegelshohn (Künstliche Intell., 2018)

Fully Dynamic Consistent Facility Location. - Vincent Cohen-Addad; Niklas Hjuler; Nikos Parotsidis; David Saulpic; Chris Schwiegelshohn (NeurIPS, 2019)

(1 + ε)-Approximate Incremental Matching in Constant Deterministic Amortized Time. - Fabrizio Grandoni 001; Stefano Leonardi 001; Piotr Sankowski; Chris Schwiegelshohn; Shay Solomon (SODA, 2019)

Oblivious dimension reduction for k-means: beyond subspaces and the Johnson-Lindenstrauss lemma. - Luca Becchetti; Marc Bury; Vincent Cohen-Addad; Fabrizio Grandoni 001; Chris Schwiegelshohn (STOC, 2019)

Fair Coresets and Streaming Algorithms for Fair k-means. - Melanie Schmidt 001; Chris Schwiegelshohn; Christian Sohler (WAOA, 2019)

Algorithms for Fair Team Formation in Online Labour Marketplaces✱. - Giorgio Barnabò; Adriano Fazzone; Stefano Leonardi 001; Chris Schwiegelshohn (WWW (Companion Volume), 2019)

Structural Results on Matching Estimation with Applications to Streaming. - Marc Bury; Elena Grigorescu; Andrew McGregor 001; Morteza Monemizadeh; Chris Schwiegelshohn; Sofya Vorotnikova; Samson Zhou (Algorithmica, 2019)

Maximizing Online Utilization with Commitment. - Chris Schwiegelshohn; Uwe Schwiegelshohn (CoRR, 2019)

Principal Fairness: Removing Bias via Projections. - Aris Anagnostopoulos; Luca Becchetti; Matteo Böhm; Adriano Fazzone; Stefano Leonardi 001; Cristina Menghini; Chris Schwiegelshohn (CoRR, 2019)

Spectral Relaxations and Fair Densest Subgraphs. - Aris Anagnostopoulos; Luca Becchetti; Adriano Fazzone; Cristina Menghini; Chris Schwiegelshohn (CIKM, 2020)

Commitment and Slack for Online Load Maximization. - Samin Jamalabadi; Chris Schwiegelshohn; Uwe Schwiegelshohn (SPAA, 2020)

Fair Clustering with Multiple Colors. - Matteo Böhm; Adriano Fazzone; Stefano Leonardi 001; Chris Schwiegelshohn (CoRR, 2020)

Similarity Search for Dynamic Data Streams. - Marc Bury; Chris Schwiegelshohn; Mara Sorella (IEEE Trans. Knowl. Data Eng., 2020)

Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces. - Vincent Cohen-Addad; David Saulpic; Chris Schwiegelshohn (NeurIPS, 2021)

A new coreset framework for clustering. - Vincent Cohen-Addad; David Saulpic; Chris Schwiegelshohn (STOC, 2021)

Polynomial Time Approximation Schemes for All 1-Center Problems on Metric Rational Set Similarities. - Marc Bury; Michele Gentili; Chris Schwiegelshohn; Mara Sorella (Algorithmica, 2021)

Algorithms for fair k-clustering with multiple protected attributes. - Matteo Böhm; Adriano Fazzone; Stefano Leonardi 001; Cristina Menghini; Chris Schwiegelshohn (Oper. Res. Lett., 2021)

An Empirical Evaluation of k-Means Coresets. - Chris Schwiegelshohn; Omar Ali Sheikh-Omar (ESA, 2022)

The Power of Uniform Sampling for Coresets. - Vladimir Braverman; Vincent Cohen-Addad; Shaofeng H.-C. Jiang; Robert Krauthgamer; Chris Schwiegelshohn; Mads Bech Toftrup; Xuan Wu 002 (FOCS, 2022)

Scalable Differentially Private Clustering via Hierarchically Separated Trees. - Vincent Cohen-Addad; Alessandro Epasto; Silvio Lattanzi; Vahab Mirrokni; Andres Muñoz Medina; David Saulpic; Chris Schwiegelshohn; Sergei Vassilvitskii (KDD, 2022)

Improved Coresets for Euclidean k-Means. - Vincent Cohen-Addad; Kasper Green Larsen; David Saulpic; Chris Schwiegelshohn; Omar Ali Sheikh-Omar (NeurIPS, 2022)

Maintaining an EDCS in General Graphs: Simpler, Density-Sensitive and with Worst-Case Time Bounds. - Fabrizio Grandoni 001; Chris Schwiegelshohn; Shay Solomon; Amitai Uzrad (SOSA, 2022)

Towards optimal lower bounds for k-median and k-means coresets. - Vincent Cohen-Addad; Kasper Green Larsen; David Saulpic; Chris Schwiegelshohn (STOC, 2022)

Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. - Vincent Cohen-Addad; Fabrizio Grandoni 001; Euiwoong Lee; Chris Schwiegelshohn (CoRR, 2022)

Adaptive Out-Orientations with Applications. - Aleksander B. G. Christiansen; Jacob Holm; Ivor van der Hoog; Eva Rotenberg; Chris Schwiegelshohn (CoRR, 2022)

Optimal Sketching Bounds for Sparse Linear Regression. - Tung Mai; Alexander Munteanu; Cameron Musco; Anup Rao 002; Chris Schwiegelshohn; David P. Woodruff (AISTATS, 2023)

Fitting Data on a Grain of Rice. - Chris Schwiegelshohn (ALGOCLOUD, 2023)

Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. - Vincent Cohen-Addad; David Saulpic; Chris Schwiegelshohn (FOCS, 2023)

On Generalization Bounds for Projective Clustering. - Maria Sofia Bucarelli; Matilde Fjeldsø Larsen; Chris Schwiegelshohn; Mads Toftrup (NeurIPS, 2023)

Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. - Vincent Cohen-Addad; Fabrizio Grandoni 001; Euiwoong Lee; Chris Schwiegelshohn (SODA, 2023)

Optimally Interpolating between Ex-Ante Fairness and Welfare. - Mikael Høgsgaard; Panagiotis Karras; Wenyue Ma 001; Nidhi Rathi; Chris Schwiegelshohn (CoRR, 2023)

Low-Distortion Clustering with Ordinal and Limited Cardinal Information. - Jakob Burkhardt; Ioannis Caragiannis; Karl Fehrs; Matteo Russo 002; Chris Schwiegelshohn; Sudarshan Shyam (AAAI, 2024)

Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds. - Nikhil Bansal 001; Vincent Cohen-Addad; Milind Prabhu; David Saulpic; Chris Schwiegelshohn (FOCS, 2024)

Optimal Coresets for Low-Dimensional Geometric Median. - Peyman Afshani; Chris Schwiegelshohn (ICML, 2024)

Sparse Dimensionality Reduction Revisited. - Mikael Møller Høgsgaard; Lior Kamma; Kasper Green Larsen; Jelani Nelson; Chris Schwiegelshohn (ICML, 2024)

Adaptive Out-Orientations with Applications. - Chandra Chekuri; Aleksander Bjørn Grodt Christiansen; Jacob Holm; Ivor van der Hoog; Kent Quanrud; Eva Rotenberg; Chris Schwiegelshohn (SODA, 2024)

A Simple and Optimal Sublinear Algorithm for Mean Estimation. - Beatrice Bertolotti; Matteo Russo 002; Chris Schwiegelshohn (CoRR, 2024)

On Approximability of ℓ22 Min-Sum Clustering. - Karthik C. S. 001; Euiwoong Lee; Yuval Rabani; Chris Schwiegelshohn; Samson Zhou (CoRR, 2024)

Distributed Differentially Private Data Analytics via Secure Sketching. - Jakob Burkhardt; Hannah Keller; Claudio Orlandi; Chris Schwiegelshohn (IACR Cryptol. ePrint Arch., 2024)

Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. - Andrew Draganov; David Saulpic; Chris Schwiegelshohn (Proc. ACM Manag. Data, 2024)

On Approximability of ℓ₂² Min-Sum Clustering. - Karthik C. S. 001; Euiwoong Lee; Yuval Rabani; Chris Schwiegelshohn; Samson Zhou (SoCG, 2025)

A Tight VC-Dimension Analysis of Clustering Coresets with Applications. - Vincent Cohen-Addad; Andrew Draganov; Matteo Russo 002; David Saulpic; Chris Schwiegelshohn (SODA, 2025)

A (2+ε)-Approximation Algorithm for Metric k-Median. - Vincent Cohen-Addad; Fabrizio Grandoni 001; Euiwoong Lee; Chris Schwiegelshohn; Ola Svensson (STOC, 2025)

Almost Optimal PAC Learning for k-Means. - Vincent Cohen-Addad; Silvio Lattanzi; Chris Schwiegelshohn (STOC, 2025)

Fair Projections as a Means toward Balanced Recommendations. - Aris Anagnostopoulos; Luca Becchetti; Matteo Böhm; Adriano Fazzone; Stefano Leonardi 001; Cristina Menghini; Chris Schwiegelshohn (ACM Trans. Intell. Syst. Technol., 2025)

A (2+ ϵ)-Approximation Algorithm for Metric k-Median. - Vincent Cohen-Addad; Fabrizio Grandoni 001; Euiwoong Lee; Chris Schwiegelshohn; Ola Svensson (CoRR, 2025)

Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves. - Amer Krivosija; Alexander Munteanu; André Nusser; Chris Schwiegelshohn (CoRR, 2025)

Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures. - Jie Gao; Rajesh Jayaram; Benedikt Kolbe; Shay Sapir; Chris Schwiegelshohn; Sandeep Silwal; Erik Waingarten (CoRR, 2025)