Schwiegelshohn, Chris

13 publications

ICML 2025 Distributed Differentially Private Data Analytics via Secure Sketching Jakob Burkhardt, Hannah Keller, Claudio Orlandi, Chris Schwiegelshohn

ICML 2025 Improved Learning via K-DTW: A Novel Dissimilarity Measure for Curves Amer Krivošija, Alexander Munteanu, André Nusser, Chris Schwiegelshohn

ICML 2025 Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten

NeurIPS 2025 Simple and Optimal Sublinear Algorithms for Mean Estimation Beatrice Bertolotti, Matteo Russo, Chris Schwiegelshohn, Sudarshan Shyam

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

ICML 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

NeurIPS 2023 On Generalization Bounds for Projective Clustering Maria Sofia Bucarelli, Matilde Larsen, Chris Schwiegelshohn, Mads Toftrup

AISTATS 2023 Optimal Sketching Bounds for Sparse Linear Regression Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David Woodruff

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

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

NeurIPS 2019 Fully Dynamic Consistent Facility Location Vincent Cohen-Addad, Niklas Oskar D Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn

NeurIPS 2018 On Coresets for Logistic Regression Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David Woodruff