Nikolaj Thams
Nikolaj Thams
PhD student
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Regularizing towards causal invariance: Linear models with proxies
M Oberst, N Thams, J Peters, D Sontag
International Conference on Machine Learning, 8260-8270, 2021
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
Statistical testing under distributional shifts
N Thams, S Saengkyongam, N Pfister, J Peters
arXiv preprint arXiv:2105.10821, 2021
Invariant policy learning: A causal perspective
S Saengkyongam, N Thams, J Peters, N Pfister
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Evaluating robustness to dataset shift via parametric robustness sets
N Thams, M Oberst, D Sontag
arXiv preprint arXiv:2205.15947, 2022
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
Causal structure learning in multivariate point processes
N Thams
Master’s 434, 2019
Invariant Ancestry Search
PB Mogensen, N Thams, J Peters
International Conference on Machine Learning, 15832-15857, 2022
Local Independence Testing for Point Processes
N Thams, NR Hansen
arXiv preprint arXiv:2110.12709, 2021
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
M Oberst, N Thams, D Sontag
ICML 2022: Workshop on Spurious Correlations, Invariance and Stability, 0
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Articles 1–10