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Nikolaj Thams
Nikolaj Thams
PhD student
Verificeret mail på math.ku.dk - Startside
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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
312020
Regularizing towards causal invariance: Linear models with proxies
M Oberst, N Thams, J Peters, D Sontag
International Conference on Machine Learning, 8260-8270, 2021
242021
Invariant policy learning: A causal perspective
S Saengkyongam, N Thams, J Peters, N Pfister
IEEE transactions on pattern analysis and machine intelligence, 2023
192023
Statistical testing under distributional shifts
N Thams, S Saengkyongam, N Pfister, J Peters
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023
92023
Identifying causal effects using instrumental time series: Nuisance iv and correcting for the past
N Thams, R Søndergaard, S Weichwald, J Peters
arXiv preprint arXiv:2203.06056, 2022
72022
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
52022
Invariant ancestry search
PB Mogensen, N Thams, J Peters
International Conference on Machine Learning, 15832-15857, 2022
32022
Causal structure learning in multivariate point processes
N Thams
Master’s 434, 2019
22019
Local Independence Testing for Point Processes
N Thams, NR Hansen
IEEE Transactions on Neural Networks and Learning Systems, 2023
12023
Causality and distribution shift
NTB Thams
Department of Mathematical Sciences, University of Copenhagen, 2022
12022
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
M Oberst, N Thams, D Sontag
ICML 2022: Workshop on Spurious Correlations, Invariance and Stability, 2022
2022
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Artikler 1–11