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Chris Schwiegelshohn

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affiliation: Aarhus University, Denmark
affiliation (former): TU Dortmund, Germany

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2020 – today

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2024
[j6]
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Andrew Draganov, David Saulpic, Chris Schwiegelshohn:
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. Proc. ACM Manag. Data 2(3): 173 (2024)
[c36]
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Jakob Burkhardt, Ioannis Caragiannis, Karl Fehrs, Matteo Russo, Chris Schwiegelshohn, Sudarshan Shyam:
Low-Distortion Clustering with Ordinal and Limited Cardinal Information. AAAI 2024: 9555-9563
[c35]
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Peyman Afshani, Chris Schwiegelshohn:
Optimal Coresets for Low-Dimensional Geometric Median. ICML 2024
[c34]
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Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen, Jelani Nelson, Chris Schwiegelshohn:
Sparse Dimensionality Reduction Revisited. ICML 2024
[c33]
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Chandra Chekuri, Aleksander Bjørn Grodt Christiansen, Jacob Holm, Ivor van der Hoog, Kent Quanrud, Eva Rotenberg, Chris Schwiegelshohn:
Adaptive Out-Orientations with Applications. SODA 2024: 3062-3088
[i28]
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Jakob Burkhardt, Ioannis Caragiannis, Karl Fehrs, Matteo Russo, Chris Schwiegelshohn, Sudarshan Shyam:
Low-Distortion Clustering with Ordinal and Limited Cardinal Information. CoRR abs/2402.04035 (2024)
[i27]
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  - journals/corr/abs-2404-01936
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Andrew Draganov, David Saulpic, Chris Schwiegelshohn:
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. CoRR abs/2404.01936 (2024)
[i26]
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  - journals/corr/abs-2405-01339
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Nikhil Bansal, Vincent Cohen-Addad, Milind Prabhu, David Saulpic, Chris Schwiegelshohn:
Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds. CoRR abs/2405.01339 (2024)
[i25]
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  - journals/corr/abs-2406-05254
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Beatrice Bertolotti, Matteo Russo, Chris Schwiegelshohn:
A Simple and Optimal Sublinear Algorithm for Mean Estimation. CoRR abs/2406.05254 (2024)
2023
[c32]
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Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. AISTATS 2023: 11288-11316
[c31]
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Chris Schwiegelshohn:
Fitting Data on a Grain of Rice. ALGOCLOUD 2023: 1-8
[c30]
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Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. FOCS 2023: 1105-1130
[c29]
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Maria Sofia Bucarelli, Matilde Fjeldsø Larsen, Chris Schwiegelshohn, Mads Toftrup:
On Generalization Bounds for Projective Clustering. NeurIPS 2023
[c28]
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Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn:
Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. SODA 2023: 940-986
[i24]
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  - journals/corr/abs-2302-03071
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Mikael Høgsgaard, Panagiotis Karras, Wenyue Ma, Nidhi Rathi, Chris Schwiegelshohn:
Optimally Interpolating between Ex-Ante Fairness and Welfare. CoRR abs/2302.03071 (2023)
[i23]
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  - journals/corr/abs-2302-06165
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Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen, Jelani Nelson, Chris Schwiegelshohn:
Sparse Dimensionality Reduction Revisited. CoRR abs/2302.06165 (2023)
[i22]
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  - journals/corr/abs-2304-02261
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Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. CoRR abs/2304.02261 (2023)
[i21]
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  - journals/corr/abs-2310-04076
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Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. CoRR abs/2310.04076 (2023)
[i20]
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  - journals/corr/abs-2310-09127
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Maria Sofia Bucarelli, Matilde Fjeldsø Larsen, Chris Schwiegelshohn, Mads Bech Toftrup:
On Generalization Bounds for Projective Clustering. CoRR abs/2310.09127 (2023)
[i19]
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  - journals/corr/abs-2310-18146
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Chandra Chekuri, Aleksander Bjørn Grodt Christiansen, Jacob Holm, Ivor van der Hoog, Kent Quanrud, Eva Rotenberg, Chris Schwiegelshohn:
Adaptive Out-Orientations with Applications. CoRR abs/2310.18146 (2023)
2022
[c27]
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Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
An Empirical Evaluation of k-Means Coresets. ESA 2022: 84:1-84:17
[c26]
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Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu:
The Power of Uniform Sampling for Coresets. FOCS 2022: 462-473
[c25]
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  - conf/kdd/Cohen-AddadELMM22
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Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. KDD 2022: 221-230
[c24]
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Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
Improved Coresets for Euclidean k-Means. NeurIPS 2022
[c23]
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Fabrizio Grandoni, Chris Schwiegelshohn, Shay Solomon, Amitai Uzrad:
Maintaining an EDCS in General Graphs: Simpler, Density-Sensitive and with Worst-Case Time Bounds. SOSA 2022: 12-23
[c22]
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Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn:
Towards optimal lower bounds for k-median and k-means coresets. STOC 2022: 1038-1051
[i18]
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  - journals/corr/abs-2202-12793
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Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn:
Towards Optimal Lower Bounds for k-median and k-means Coresets. CoRR abs/2202.12793 (2022)
[i17]
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  - journals/corr/abs-2206-08646
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Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab S. Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. CoRR abs/2206.08646 (2022)
[i16]
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  - journals/corr/abs-2207-00966
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Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
An Empirical Evaluation of k-Means Coresets. CoRR abs/2207.00966 (2022)
[i15]
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  - journals/corr/abs-2207-05150
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Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn:
Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. CoRR abs/2207.05150 (2022)
[i14]
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  - journals/corr/abs-2209-01901
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Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu:
The Power of Uniform Sampling for Coresets. CoRR abs/2209.01901 (2022)
[i13]
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  - journals/corr/abs-2209-14087
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Aleksander B. G. Christiansen, Jacob Holm, Ivor van der Hoog, Eva Rotenberg, Chris Schwiegelshohn:
Adaptive Out-Orientations with Applications. CoRR abs/2209.14087 (2022)
[i12]
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  - journals/corr/abs-2211-08184
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Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
Improved Coresets for Euclidean k-Means. CoRR abs/2211.08184 (2022)
2021
[j5]
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Marc Bury, Michele Gentili, Chris Schwiegelshohn, Mara Sorella:
Polynomial Time Approximation Schemes for All 1-Center Problems on Metric Rational Set Similarities. Algorithmica 83(5): 1371-1392 (2021)
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Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Cristina Menghini, Chris Schwiegelshohn:
Algorithms for fair k-clustering with multiple protected attributes. Oper. Res. Lett. 49(5): 787-789 (2021)
[c21]
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Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces. NeurIPS 2021: 21085-21098
[c20]
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Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
A new coreset framework for clustering. STOC 2021: 169-182
[i11]
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  - journals/corr/abs-2104-06133
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Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
A New Coreset Framework for Clustering. CoRR abs/2104.06133 (2021)
[i10]
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Fabrizio Grandoni, Chris Schwiegelshohn, Shay Solomon, Amitai Uzrad:
Maintaining an EDCS in General Graphs: Simpler, Density-Sensitive and with Worst-Case Time Bounds. CoRR abs/2108.08825 (2021)
2020
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Marc Bury, Chris Schwiegelshohn, Mara Sorella:
Similarity Search for Dynamic Data Streams. IEEE Trans. Knowl. Data Eng. 32(11): 2241-2253 (2020)
[c19]
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Aris Anagnostopoulos, Luca Becchetti, Adriano Fazzone, Cristina Menghini, Chris Schwiegelshohn:
Spectral Relaxations and Fair Densest Subgraphs. CIKM 2020: 35-44
[c18]
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Samin Jamalabadi, Chris Schwiegelshohn, Uwe Schwiegelshohn:
Commitment and Slack for Online Load Maximization. SPAA 2020: 339-348
[i9]
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  - journals/corr/abs-2002-07892
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Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn:
Fair Clustering with Multiple Colors. CoRR abs/2002.07892 (2020)
[i8]
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  - journals/corr/abs-2002-11621
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Giorgio Barnabò, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn:
Algorithms for Fair Team Formation in Online Labour Marketplaces. CoRR abs/2002.11621 (2020)

2010 – 2019

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2019
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Marc Bury, Elena Grigorescu, Andrew McGregor, Morteza Monemizadeh, Chris Schwiegelshohn, Sofya Vorotnikova, Samson Zhou:
Structural Results on Matching Estimation with Applications to Streaming. Algorithmica 81(1): 367-392 (2019)
[c17]
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Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. GI-Jahrestagung 2019: 267-268
[c16]
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Vincent Cohen-Addad, Niklas Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn:
Fully Dynamic Consistent Facility Location. NeurIPS 2019: 3250-3260
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Fabrizio Grandoni, Stefano Leonardi, Piotr Sankowski, Chris Schwiegelshohn, Shay Solomon:
(1 + ε)-Approximate Incremental Matching in Constant Deterministic Amortized Time. SODA 2019: 1886-1898
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Luca Becchetti, Marc Bury, Vincent Cohen-Addad, Fabrizio Grandoni, Chris Schwiegelshohn:
Oblivious dimension reduction for k-means: beyond subspaces and the Johnson-Lindenstrauss lemma. STOC 2019: 1039-1050
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Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler:
Fair Coresets and Streaming Algorithms for Fair k-means. WAOA 2019: 232-251
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Giorgio Barnabò, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn:
Algorithms for Fair Team Formation in Online Labour Marketplaces✱. WWW (Companion Volume) 2019: 484-490
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Chris Schwiegelshohn, Uwe Schwiegelshohn:
Maximizing Online Utilization with Commitment. CoRR abs/1904.06150 (2019)
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Aris Anagnostopoulos, Luca Becchetti, Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Cristina Menghini, Chris Schwiegelshohn:
Principal Fairness: \\ Removing Bias via Projections. CoRR abs/1905.13651 (2019)
2018
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Alexander Munteanu, Chris Schwiegelshohn:
Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms. Künstliche Intell. 32(1): 37-53 (2018)
[c11]
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Aris Anagnostopoulos, Fabio Angeletti, Federico Arcangeli, Chris Schwiegelshohn, Andrea Vitaletti:
Random Projection to Preserve Patient Privacy. CIKM Workshops 2018
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Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. NeurIPS 2018: 6562-6571
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Marc Bury, Chris Schwiegelshohn, Mara Sorella:
Sketch 'Em All: Fast Approximate Similarity Search for Dynamic Data Streams. WSDM 2018: 72-80
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Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. CoRR abs/1805.08571 (2018)
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Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler:
Fair Coresets and Streaming Algorithms for Fair k-Means Clustering. CoRR abs/1812.10854 (2018)
2017
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Chris Schwiegelshohn:
On algorithms for large-scale graph and clustering problems. Dortmund University, Germany, 2017
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Vincent Cohen-Addad, Chris Schwiegelshohn:
On the Local Structure of Stable Clustering Instances. FOCS 2017: 49-60
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Marc Bury, Chris Schwiegelshohn:
On Finding the Jaccard Center. ICALP 2017: 23:1-23:14
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Chris Schwiegelshohn:
Algorithmen für datenintensive Graph- und Clusteringprobleme. Ausgezeichnete Informatikdissertationen 2017: 231-240
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Vincent Cohen-Addad, Chris Schwiegelshohn:
One Size Fits All : Effectiveness of Local Search on Structured Data. CoRR abs/1701.08423 (2017)
2016
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Chris Schwiegelshohn, Uwe Schwiegelshohn:
The Power of Migration for Online Slack Scheduling. ESA 2016: 75:1-75:17
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Vincent Cohen-Addad, Chris Schwiegelshohn, Christian Sohler:
Diameter and k-Center in Sliding Windows. ICALP 2016: 19:1-19:12
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Marc Bury, Chris Schwiegelshohn:
Efficient Similarity Search in Dynamic Data Streams. CoRR abs/1605.03949 (2016)
2015
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Marc Bury, Chris Schwiegelshohn:
Sublinear Estimation of Weighted Matchings in Dynamic Data Streams. ESA 2015: 263-274
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Marc Bury, Chris Schwiegelshohn:
Sublinear Estimation of Weighted Matchings in Dynamic Data Streams. CoRR abs/1505.02019 (2015)
2013
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Hendrik Fichtenberger, Marc Gillé, Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler:
BICO: BIRCH Meets Coresets for k-Means Clustering. ESA 2013: 481-492
2012
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Stefan Canzar, Tobias Marschall, Sven Rahmann, Chris Schwiegelshohn:
Solving the Minimum String Cover Problem. ALENEX 2012: 75-83

2000 – 2009

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2009
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Hendrik Blom, Christiane Küch, Katja Losemann, Chris Schwiegelshohn:
PEPPA: a project for evolutionary predator prey algorithms. GECCO (Companion) 2009: 1993-1998

Coauthor Index

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