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- Chris Schwiegelshohn
Aarhus University, Denmark
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Publication search results
found 80 matches
- 2024
- Andrew Draganov
, David Saulpic
, Chris Schwiegelshohn
:
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. Proc. ACM Manag. Data 2(3): 173 (2024) - 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 - 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 - 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) - Andrew Draganov, David Saulpic, Chris Schwiegelshohn:
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. CoRR abs/2404.01936 (2024) - 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) - Beatrice Bertolotti, Matteo Russo, Chris Schwiegelshohn:
A Simple and Optimal Sublinear Algorithm for Mean Estimation. CoRR abs/2406.05254 (2024) - 2023
- Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. AISTATS 2023: 11288-11316 - Chris Schwiegelshohn
:
Fitting Data on a Grain of Rice. ALGOCLOUD 2023: 1-8 - Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. FOCS 2023: 1105-1130 - Maria Sofia Bucarelli, Matilde Fjeldsø Larsen, Chris Schwiegelshohn, Mads Toftrup:
On Generalization Bounds for Projective Clustering. NeurIPS 2023 - 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 - Mikael Høgsgaard, Panagiotis Karras, Wenyue Ma, Nidhi Rathi, Chris Schwiegelshohn:
Optimally Interpolating between Ex-Ante Fairness and Welfare. CoRR abs/2302.03071 (2023) - Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen, Jelani Nelson, Chris Schwiegelshohn:
Sparse Dimensionality Reduction Revisited. CoRR abs/2302.06165 (2023) - 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) - Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn:
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. CoRR abs/2310.04076 (2023) - Maria Sofia Bucarelli, Matilde Fjeldsø Larsen, Chris Schwiegelshohn, Mads Bech Toftrup:
On Generalization Bounds for Projective Clustering. CoRR abs/2310.09127 (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 abs/2310.18146 (2023) - 2022
- Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
An Empirical Evaluation of k-Means Coresets. ESA 2022: 84:1-84:17 - 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 - 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 - Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
Improved Coresets for Euclidean k-Means. NeurIPS 2022 - 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 - 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 - 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) - 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) - Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
An Empirical Evaluation of k-Means Coresets. CoRR abs/2207.00966 (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 abs/2207.05150 (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 abs/2209.01901 (2022) - Aleksander B. G. Christiansen, Jacob Holm, Ivor van der Hoog, Eva Rotenberg, Chris Schwiegelshohn:
Adaptive Out-Orientations with Applications. CoRR abs/2209.14087 (2022)
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