Chris Schwiegelshohn

93 papers A* 29A 8B 2Journal 46Unranked 6
YearRankTypeTitle / Venue / Authors
2026 J jnl
Almost-Optimal Upper and Lower Bounds for Clustering in Low Dimensional Euclidean Spaces.
CoRR
Vincent Cohen-Addad, Karthik C. S., David Saulpic, Chris Schwiegelshohn
2026 J jnl
Maintaining an EDCS in General Graphs: Simpler, Density-Sensitive and with Worst-Case Time Bounds.
ACM Trans. Algorithms
Fabrizio Grandoni, Chris Schwiegelshohn, Shay Solomon, Amitai Uzrad
2026 J jnl
Near-Optimal Bounds for Parameterized Euclidean k-means.
CoRR
Vincent Cohen-Addad, Karthik C. S., David Saulpic, Chris Schwiegelshohn
2025 J jnl
A (2+ ϵ)-Approximation Algorithm for Metric
CoRR
Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn, Ola Svensson
2025 A* conf
A (2+ε)-Approximation Algorithm for Metric k-Median.
STOC
Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn, Ola Svensson
2025 A* conf
A Tight VC-Dimension Analysis of Clustering Coresets with Applications.
SODA
Vincent Cohen-Addad, Andrew Draganov, Matteo Russo, David Saulpic, Chris Schwiegelshohn
2025 J jnl
A Tight VC-Dimension Analysis of Clustering Coresets with Applications.
CoRR
Vincent Cohen-Addad, Andrew Draganov, Matteo Russo, David Saulpic, Chris Schwiegelshohn
2025 A* conf
Almost Optimal PAC Learning for k-Means.
STOC
Vincent Cohen-Addad, Silvio Lattanzi, Chris Schwiegelshohn
2025 A* conf
Distributed Differentially Private Data Analytics via Secure Sketching.
ICML
Jakob Burkhardt, Hannah Keller, Claudio Orlandi, Chris Schwiegelshohn
2025 J jnl
Fair Projections as a Means toward Balanced Recommendations.
ACM Trans. Intell. Syst. Technol.
Aris Anagnostopoulos, Luca Becchetti, Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Cristina Menghini, Chris Schwiegelshohn
2025 A* conf
Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves.
ICML
Amer Krivosija, Alexander Munteanu, André Nusser, Chris Schwiegelshohn
2025 J jnl
Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves.
CoRR
Amer Krivosija, Alexander Munteanu, André Nusser, Chris Schwiegelshohn
2025 A* conf
Nearly Tight Regret Bounds for Profit Maximization in Bilateral Trade.
FOCS
Simone Di Gregorio, Paul Dütting, Federico Fusco, Chris Schwiegelshohn
2025 J jnl
Nearly Tight Regret Bounds for Profit Maximization in Bilateral Trade.
CoRR
Simone Di Gregorio, Paul Dütting, Federico Fusco, Chris Schwiegelshohn
2025 A* conf
On Approximability of ℓ₂² Min-Sum Clustering.
SoCG
Karthik C. S., Euiwoong Lee, Yuval Rabani, Chris Schwiegelshohn, Samson Zhou
2025 A* conf
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures.
ICML
Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten
2025 J jnl
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures.
CoRR
Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten
2024 J jnl
A Simple and Optimal Sublinear Algorithm for Mean Estimation.
CoRR
Beatrice Bertolotti, Matteo Russo, Chris Schwiegelshohn
2024 A* conf
Adaptive Out-Orientations with Applications.
SODA
Chandra Chekuri, Aleksander Bjørn Grodt Christiansen, Jacob Holm, Ivor van der Hoog, Kent Quanrud, Eva Rotenberg, Chris Schwiegelshohn
2024 J jnl
Distributed Differentially Private Data Analytics via Secure Sketching.
CoRR
Jakob Burkhardt, Hannah Keller, Claudio Orlandi, Chris Schwiegelshohn
2024 J jnl
Distributed Differentially Private Data Analytics via Secure Sketching.
IACR Cryptol. ePrint Arch.
Jakob Burkhardt, Hannah Keller, Claudio Orlandi, Chris Schwiegelshohn
2024 A* conf
Low-Distortion Clustering with Ordinal and Limited Cardinal Information.
AAAI
Jakob Burkhardt, Ioannis Caragiannis, Karl Fehrs, Matteo Russo, Chris Schwiegelshohn, Sudarshan Shyam
2024 J jnl
Low-Distortion Clustering with Ordinal and Limited Cardinal Information.
CoRR
Jakob Burkhardt, Ioannis Caragiannis, Karl Fehrs, Matteo Russo, Chris Schwiegelshohn, Sudarshan Shyam
2024 J jnl
On Approximability of ℓ
CoRR
Karthik C. S., Euiwoong Lee, Yuval Rabani, Chris Schwiegelshohn, Samson Zhou
2024 A* conf
Optimal Coresets for Low-Dimensional Geometric Median.
ICML
Peyman Afshani, Chris Schwiegelshohn
2024 A* conf
Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds.
FOCS
Nikhil Bansal, Vincent Cohen-Addad, Milind Prabhu, David Saulpic, Chris Schwiegelshohn
2024 J jnl
Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds.
CoRR
Nikhil Bansal, Vincent Cohen-Addad, Milind Prabhu, David Saulpic, Chris Schwiegelshohn
2024 J jnl
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data.
CoRR
Andrew Draganov, David Saulpic, Chris Schwiegelshohn
2024 J jnl
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data.
Proc. ACM Manag. Data
Andrew Draganov, David Saulpic, Chris Schwiegelshohn
2024 A* conf
Sparse Dimensionality Reduction Revisited.
ICML
Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen, Jelani Nelson, Chris Schwiegelshohn
2023 J jnl
Adaptive Out-Orientations with Applications.
CoRR
Chandra Chekuri, Aleksander Bjørn Grodt Christiansen, Jacob Holm, Ivor van der Hoog, Kent Quanrud, Eva Rotenberg, Chris Schwiegelshohn
2023 A* conf
Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to
SODA
Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn
2023 A* conf
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation.
FOCS
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
2023 J jnl
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation.
CoRR
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
2023 — conf
Fitting Data on a Grain of Rice.
ALGOCLOUD
Chris Schwiegelshohn
2023 A* conf
On Generalization Bounds for Projective Clustering.
NeurIPS
Maria Sofia Bucarelli, Matilde Fjeldsø Larsen, Chris Schwiegelshohn, Mads Toftrup
2023 J jnl
On Generalization Bounds for Projective Clustering.
CoRR
Maria Sofia Bucarelli, Matilde Fjeldsø Larsen, Chris Schwiegelshohn, Mads Bech Toftrup
2023 A conf
Optimal Sketching Bounds for Sparse Linear Regression.
AISTATS
Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff
2023 J jnl
Optimal Sketching Bounds for Sparse Linear Regression.
CoRR
Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao, Chris Schwiegelshohn, David P. Woodruff
2023 J jnl
Optimally Interpolating between Ex-Ante Fairness and Welfare.
CoRR
Mikael Høgsgaard, Panagiotis Karras, Wenyue Ma, Nidhi Rathi, Chris Schwiegelshohn
2023 J jnl
Sparse Dimensionality Reduction Revisited.
CoRR
Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen, Jelani Nelson, Chris Schwiegelshohn
2022 J jnl
Adaptive Out-Orientations with Applications.
CoRR
Aleksander B. G. Christiansen, Jacob Holm, Ivor van der Hoog, Eva Rotenberg, Chris Schwiegelshohn
2022 A conf
An Empirical Evaluation of k-Means Coresets.
ESA
Chris Schwiegelshohn, Omar Ali Sheikh-Omar
2022 J jnl
An Empirical Evaluation of k-Means Coresets.
CoRR
Chris Schwiegelshohn, Omar Ali Sheikh-Omar
2022 J jnl
Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median.
CoRR
Vincent Cohen-Addad, Fabrizio Grandoni, Euiwoong Lee, Chris Schwiegelshohn
2022 A* conf
Improved Coresets for Euclidean k-Means.
NeurIPS
Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar
2022 J jnl
Improved Coresets for Euclidean k-Means.
CoRR
Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar
2022 — conf
Maintaining an EDCS in General Graphs: Simpler, Density-Sensitive and with Worst-Case Time Bounds.
SOSA
Fabrizio Grandoni, Chris Schwiegelshohn, Shay Solomon, Amitai Uzrad
2022 A* conf
Scalable Differentially Private Clustering via Hierarchically Separated Trees.
KDD
Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii
2022 J jnl
Scalable Differentially Private Clustering via Hierarchically Separated Trees.
CoRR
Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab S. Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii
2022 A* conf
The Power of Uniform Sampling for Coresets.
FOCS
Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu
2022 J jnl
The Power of Uniform Sampling for Coresets.
CoRR
Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu
2022 J jnl
Towards Optimal Lower Bounds for k-median and k-means Coresets.
CoRR
Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn
2022 A* conf
Towards optimal lower bounds for k-median and k-means coresets.
STOC
Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn
2021 J jnl
A New Coreset Framework for Clustering.
CoRR
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
2021 A* conf
A new coreset framework for clustering.
STOC
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
2021 J jnl
Algorithms for fair
Oper. Res. Lett.
Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Cristina Menghini, Chris Schwiegelshohn
2021 A* conf
Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces.
NeurIPS
Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
2021 J jnl
Maintaining an EDCS in General Graphs: Simpler, Density-Sensitive and with Worst-Case Time Bounds.
CoRR
Fabrizio Grandoni, Chris Schwiegelshohn, Shay Solomon, Amitai Uzrad
2021 J jnl
Polynomial Time Approximation Schemes for All 1-Center Problems on Metric Rational Set Similarities.
Algorithmica
Marc Bury, Michele Gentili, Chris Schwiegelshohn, Mara Sorella
2020 J jnl
Algorithms for Fair Team Formation in Online Labour Marketplaces.
CoRR
Giorgio Barnabò, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn
2020 B conf
Commitment and Slack for Online Load Maximization.
SPAA
Samin Jamalabadi, Chris Schwiegelshohn, Uwe Schwiegelshohn
2020 J jnl
Fair Clustering with Multiple Colors.
CoRR
Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn
2020 J jnl
Similarity Search for Dynamic Data Streams.
IEEE Trans. Knowl. Data Eng.
Marc Bury, Chris Schwiegelshohn, Mara Sorella
2020 A conf
Spectral Relaxations and Fair Densest Subgraphs.
CIKM
Aris Anagnostopoulos, Luca Becchetti, Adriano Fazzone, Cristina Menghini, Chris Schwiegelshohn
2019 A* conf
(1 + ε)-Approximate Incremental Matching in Constant Deterministic Amortized Time.
SODA
Fabrizio Grandoni, Stefano Leonardi, Piotr Sankowski, Chris Schwiegelshohn, Shay Solomon
2019 — conf
Algorithms for Fair Team Formation in Online Labour Marketplaces✱.
WWW (Companion Volume)
Giorgio Barnabò, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn
2019 B conf
Fair Coresets and Streaming Algorithms for Fair k-means.
WAOA
Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler
2019 A* conf
Fully Dynamic Consistent Facility Location.
NeurIPS
Vincent Cohen-Addad, Niklas Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn
2019 J jnl
Maximizing Online Utilization with Commitment.
CoRR
Chris Schwiegelshohn, Uwe Schwiegelshohn
2019 A* conf
Oblivious dimension reduction for
STOC
Luca Becchetti, Marc Bury, Vincent Cohen-Addad, Fabrizio Grandoni, Chris Schwiegelshohn
2019 — conf
On Coresets for Logistic Regression.
GI-Jahrestagung
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff
2019 J jnl
Principal Fairness: \\ Removing Bias via Projections.
CoRR
Aris Anagnostopoulos, Luca Becchetti, Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Cristina Menghini, Chris Schwiegelshohn
2019 J jnl
Structural Results on Matching Estimation with Applications to Streaming.
Algorithmica
Marc Bury, Elena Grigorescu, Andrew McGregor, Morteza Monemizadeh, Chris Schwiegelshohn, Sofya Vorotnikova, Samson Zhou
2018 J jnl
Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms.
Künstliche Intell.
Alexander Munteanu, Chris Schwiegelshohn
2018 J jnl
Fair Coresets and Streaming Algorithms for Fair k-Means Clustering.
CoRR
Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler
2018 A* conf
On Coresets for Logistic Regression.
NeurIPS
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff
2018 J jnl
On Coresets for Logistic Regression.
CoRR
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff
2018 — conf
Random Projection to Preserve Patient Privacy.
CIKM Workshops
Aris Anagnostopoulos, Fabio Angeletti, Federico Arcangeli, Chris Schwiegelshohn, Andrea Vitaletti
2018 A conf
Sketch 'Em All: Fast Approximate Similarity Search for Dynamic Data Streams.
WSDM
Marc Bury, Chris Schwiegelshohn, Mara Sorella
2017 — ch.
Algorithmen für datenintensive Graph- und Clusteringprobleme.
Ausgezeichnete Informatikdissertationen
Chris Schwiegelshohn
2017 A* conf
On Finding the Jaccard Center.
ICALP
Marc Bury, Chris Schwiegelshohn
2017 — —
On algorithms for large-scale graph and clustering problems
Chris Schwiegelshohn
2017 A* conf
On the Local Structure of Stable Clustering Instances.
FOCS
Vincent Cohen-Addad, Chris Schwiegelshohn
2017 J jnl
One Size Fits All : Effectiveness of Local Search on Structured Data.
CoRR
Vincent Cohen-Addad, Chris Schwiegelshohn
2016 A* conf
Diameter and k-Center in Sliding Windows.
ICALP
Vincent Cohen-Addad, Chris Schwiegelshohn, Christian Sohler
2016 J jnl
Efficient Similarity Search in Dynamic Data Streams.
CoRR
Marc Bury, Chris Schwiegelshohn
2016 A conf
The Power of Migration for Online Slack Scheduling.
ESA
Chris Schwiegelshohn, Uwe Schwiegelshohn
2015 A conf
Sublinear Estimation of Weighted Matchings in Dynamic Data Streams.
ESA
Marc Bury, Chris Schwiegelshohn
2015 J jnl
Sublinear Estimation of Weighted Matchings in Dynamic Data Streams.
CoRR
Marc Bury, Chris Schwiegelshohn
2013 A conf
BICO: BIRCH Meets Coresets for k-Means Clustering.
ESA
Hendrik Fichtenberger, Marc Gillé, Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler
2012 A conf
Solving the Minimum String Cover Problem.
ALENEX
Stefan Canzar, Tobias Marschall, Sven Rahmann, Chris Schwiegelshohn
2009 — conf
PEPPA: a project for evolutionary predator prey algorithms.
GECCO (Companion)
Hendrik Blom, Christiane Küch, Katja Losemann, Chris Schwiegelshohn