Articles with public access mandates - Nikolaj ThamsLearn more
Available somewhere: 7
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
S Weichwald, ME Jakobsen, PB Mogensen, L Petersen, N Thams, ...
NeurIPS 2019 Competition and Demonstration Track, 27-36, 2020
Mandates: Villum Foundation, Carlsberg Foundation DK
Regularizing towards causal invariance: Linear models with proxies
M Oberst, N Thams, J Peters, D Sontag
International Conference on Machine Learning, 8260-8270, 2021
Mandates: US Department of Defense, Villum Foundation, Carlsberg Foundation DK
Invariant policy learning: A causal perspective
S Saengkyongam, N Thams, J Peters, N Pfister
IEEE transactions on pattern analysis and machine intelligence 45 (7), 8606-8620, 2023
Mandates: Villum Foundation, Carlsberg Foundation DK
Statistical testing under distributional shifts
N Thams, S Saengkyongam, N Pfister, J Peters
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023
Mandates: Villum Foundation, Carlsberg Foundation DK
Evaluating robustness to dataset shift via parametric robustness sets
N Thams, M Oberst, D Sontag
Advances in Neural Information Processing Systems 35, 16877-16889, 2022
Mandates: US Department of Defense, Villum Foundation
Invariant ancestry search
PB Mogensen, N Thams, J Peters
International Conference on Machine Learning, 15832-15857, 2022
Mandates: Villum Foundation
Local Independence Testing for Point Processes
N Thams, NR Hansen
IEEE Transactions on Neural Networks and Learning Systems, 2023
Mandates: Villum Foundation
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