Nikolaj Thams
Nikolaj Thams
PhD student
Bekræftet mail på math.ku.dk - Startside
Citeret af
Citeret af
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
Regularizing towards causal invariance: Linear models with proxies
M Oberst, N Thams, J Peters, D Sontag
International Conference on Machine Learning, 8260-8270, 2021
Statistical testing under distributional shifts
N Thams, S Saengkyongam, N Pfister, J Peters
arXiv preprint arXiv:2105.10821, 2021
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
N Thams, M Oberst, D Sontag
arXiv preprint arXiv:2205.15947, 2022
Invariant Policy Learning: A Causal Perspective
S Saengkyongam, N Thams, J Peters, N Pfister
arXiv preprint arXiv:2106.00808, 2021
Causal Structure Learning in Multivariate Point Processes
NT Thams
Master’s thesis, University of Copenhagen, 2019
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
Invariant Ancestry Search
PB Mogensen, N Thams, J Peters
arXiv preprint arXiv:2202.00913, 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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Artikler 1–10