Chris Schwiegelshohn

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 64

Top Topics

Logistic Regression

Top Venues

Publications

Nikhil Bansal, Vincent Cohen-Addad, Milind Prabhu, David Saulpic, Chris Schwiegelshohn
Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds. CoRR (2024)
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)
Andrew Draganov, David Saulpic, Chris Schwiegelshohn
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. CoRR (2024)
Beatrice Bertolotti, Matteo Russo, Chris Schwiegelshohn
A Simple and Optimal Sublinear Algorithm for Mean Estimation. CoRR (2024)
Andrew Draganov, David Saulpic, Chris Schwiegelshohn
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. Proc. ACM Manag. Data 2 (3) (2024)
Jakob Burkhardt, Ioannis Caragiannis, Karl Fehrs, Matteo Russo, Chris Schwiegelshohn, Sudarshan Shyam
Low-Distortion Clustering with Ordinal and Limited Cardinal Information. CoRR (2024)
Jakob Burkhardt, Ioannis Caragiannis, Karl Fehrs, Matteo Russo, Chris Schwiegelshohn, Sudarshan Shyam
Low-Distortion Clustering with Ordinal and Limited Cardinal Information. AAAI (2024)
Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn
-Median. SODA (2023)
Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao, Chris Schwiegelshohn, David P. Woodruff
Optimal Sketching Bounds for Sparse Linear Regression. CoRR (2023)
Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff
Optimal Sketching Bounds for Sparse Linear Regression. AISTATS (2023)
Mikael Høgsgaard, Panagiotis Karras, Wenyue Ma, Nidhi Rathi, Chris Schwiegelshohn
Optimally Interpolating between Ex-Ante Fairness and Welfare. CoRR (2023)
Maria Sofia Bucarelli, Matilde Fjeldsø Larsen, Chris Schwiegelshohn, Mads Bech Toftrup
On Generalization Bounds for Projective Clustering. CoRR (2023)
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. CoRR (2023)
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. FOCS (2023)
Maria Sofia Bucarelli, Matilde Fjeldsø Larsen, Chris Schwiegelshohn, Mads Toftrup
On Generalization Bounds for Projective Clustering. NeurIPS (2023)
Chandra Chekuri, Aleksander Bjørn Grodt Christiansen, Jacob Holm, Ivor van der Hoog, Kent Quanrud, Eva Rotenberg, Chris Schwiegelshohn
Adaptive Out-Orientations with Applications. CoRR (2023)
Chris Schwiegelshohn
Fitting Data on a Grain of Rice. ALGOCLOUD (2023)
Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen, Jelani Nelson, Chris Schwiegelshohn
Sparse Dimensionality Reduction Revisited. CoRR (2023)
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)
Chris Schwiegelshohn, Omar Ali Sheikh-Omar
An Empirical Evaluation of k-Means Coresets. CoRR (2022)
Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn
Towards optimal lower bounds for k-median and k-means coresets. STOC (2022)
Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar
Improved Coresets for Euclidean k-Means. NeurIPS (2022)
Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab S. Mirrokni, Andres Munoz, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii
Scalable Differentially Private Clustering via Hierarchically Separated Trees. CoRR (2022)
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)
Aleksander B. G. Christiansen, Jacob Holm, Ivor van der Hoog, Eva Rotenberg, Chris Schwiegelshohn
Adaptive Out-Orientations with Applications. CoRR (2022)
Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn
Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. CoRR (2022)
Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn
Towards Optimal Lower Bounds for k-median and k-means Coresets. CoRR (2022)
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)
Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar
Improved Coresets for Euclidean k-Means. CoRR (2022)
Chris Schwiegelshohn, Omar Ali Sheikh-Omar
An Empirical Evaluation of k-Means Coresets. ESA (2022)
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 (2022)
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
A new coreset framework for clustering. STOC (2021)
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
A New Coreset Framework for Clustering. CoRR (2021)
Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Cristina Menghini, Chris Schwiegelshohn
-clustering with multiple protected attributes. Oper. Res. Lett. 49 (5) (2021)
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces. NeurIPS (2021)
Fabrizio Grandoni, Chris Schwiegelshohn, Shay Solomon, Amitai Uzrad
Maintaining an EDCS in General Graphs: Simpler, Density-Sensitive and with Worst-Case Time Bounds. CoRR (2021)
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) (2021)
Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn
Fair Clustering with Multiple Colors. CoRR (2020)
Marc Bury, Chris Schwiegelshohn, Mara Sorella
Similarity Search for Dynamic Data Streams. IEEE Trans. Knowl. Data Eng. 32 (11) (2020)
Giorgio Barnabò, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn
Algorithms for Fair Team Formation in Online Labour Marketplaces. CoRR (2020)
Aris Anagnostopoulos, Luca Becchetti, Adriano Fazzone, Cristina Menghini, Chris Schwiegelshohn
Spectral Relaxations and Fair Densest Subgraphs. CIKM (2020)
Samin Jamalabadi, Chris Schwiegelshohn, Uwe Schwiegelshohn
Commitment and Slack for Online Load Maximization. SPAA (2020)
Giorgio Barnabò, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn
Algorithms for Fair Team Formation in Online Labour Marketplaces✱. WWW (Companion Volume) (2019)
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) (2019)
Chris Schwiegelshohn, Uwe Schwiegelshohn
Maximizing Online Utilization with Commitment. CoRR (2019)
Aris Anagnostopoulos, Luca Becchetti, Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Cristina Menghini, Chris Schwiegelshohn
Principal Fairness: \\ Removing Bias via Projections. CoRR (2019)
Fabrizio Grandoni, Stefano Leonardi, Piotr Sankowski, Chris Schwiegelshohn, Shay Solomon
(1 + ε)-Approximate Incremental Matching in Constant Deterministic Amortized Time. SODA (2019)
Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler
Fair Coresets and Streaming Algorithms for Fair k-means. WAOA (2019)
Vincent Cohen-Addad, Niklas Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn
Fully Dynamic Consistent Facility Location. NeurIPS (2019)
Luca Becchetti, Marc Bury, Vincent Cohen-Addad, Fabrizio Grandoni, Chris Schwiegelshohn
-means: beyond subspaces and the Johnson-Lindenstrauss lemma. STOC (2019)
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff
On Coresets for Logistic Regression. GI-Jahrestagung (2019)
Marc Bury, Chris Schwiegelshohn, Mara Sorella
Sketch 'Em All: Fast Approximate Similarity Search for Dynamic Data Streams. WSDM (2018)
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff
On Coresets for Logistic Regression. NeurIPS (2018)
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff
On Coresets for Logistic Regression. CoRR (2018)
Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler
Fair Coresets and Streaming Algorithms for Fair k-Means Clustering. CoRR (2018)
Aris Anagnostopoulos, Fabio Angeletti, Federico Arcangeli, Chris Schwiegelshohn, Andrea Vitaletti
Random Projection to Preserve Patient Privacy. CIKM Workshops (2018)
Alexander Munteanu, Chris Schwiegelshohn
Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms. Künstliche Intell. 32 (1) (2018)
Vincent Cohen-Addad, Chris Schwiegelshohn
One Size Fits All : Effectiveness of Local Search on Structured Data. CoRR (2017)
Marc Bury, Chris Schwiegelshohn
On Finding the Jaccard Center. ICALP (2017)
Vincent Cohen-Addad, Chris Schwiegelshohn
On the Local Structure of Stable Clustering Instances. FOCS (2017)
Chris Schwiegelshohn
Algorithmen für datenintensive Graph- und Clusteringprobleme. Ausgezeichnete Informatikdissertationen (2017)
Vincent Cohen-Addad, Chris Schwiegelshohn, Christian Sohler
Diameter and k-Center in Sliding Windows. ICALP (2016)
Chris Schwiegelshohn, Uwe Schwiegelshohn
The Power of Migration for Online Slack Scheduling. ESA (2016)
Marc Bury, Chris Schwiegelshohn
Efficient Similarity Search in Dynamic Data Streams. CoRR (2016)
Marc Bury, Chris Schwiegelshohn
Sublinear Estimation of Weighted Matchings in Dynamic Data Streams. CoRR (2015)
Marc Bury, Chris Schwiegelshohn
Sublinear Estimation of Weighted Matchings in Dynamic Data Streams. ESA (2015)
Hendrik Fichtenberger, Marc Gillé, Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler
BICO: BIRCH Meets Coresets for k-Means Clustering. ESA (2013)
Stefan Canzar, Tobias Marschall, Sven Rahmann, Chris Schwiegelshohn
Solving the Minimum String Cover Problem. ALENEX (2012)
Hendrik Blom, Christiane Küch, Katja Losemann, Chris Schwiegelshohn
PEPPA: a project for evolutionary predator prey algorithms. GECCO (Companion) (2009)