Nikolaj Thams

12 papers A* 3Journal 8Unranked 1
YearRankTypeTitle / Venue / Authors
2024 J jnl
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past.
J. Mach. Learn. Res.
Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters
2024 J jnl
Local Independence Testing for Point Processes.
IEEE Trans. Neural Networks Learn. Syst.
Nikolaj Thams, Niels Richard Hansen
2023 J jnl
Invariant Policy Learning: A Causal Perspective.
IEEE Trans. Pattern Anal. Mach. Intell.
Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister
2022 A* conf
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets.
NeurIPS
Nikolaj Thams, Michael Oberst, David A. Sontag
2022 J jnl
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets.
CoRR
Nikolaj Thams, Michael Oberst, David A. Sontag
2022 A* conf
Invariant Ancestry Search.
ICML
Phillip B. Mogensen, Nikolaj Thams, Jonas Peters
2022 J jnl
Invariant Ancestry Search.
CoRR
Phillip B. Mogensen, Nikolaj Thams, Jonas Peters
2021 J jnl
Invariant Policy Learning: A Causal Perspective.
CoRR
Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister
2021 A* conf
Regularizing towards Causal Invariance: Linear Models with Proxies.
ICML
Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
2021 J jnl
Regularizing towards Causal Invariance: Linear Models with Proxies.
CoRR
Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
2020 J jnl
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values.
CoRR
Sebastian Weichwald, Martin Emil Jakobsen, Phillip B. Mogensen, Lasse Petersen, Nikolaj Thams, Gherardo Varando
2019 — conf
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values.
NeurIPS (Competition and Demos)
Sebastian Weichwald, Martin Emil Jakobsen, Phillip B. Mogensen, Lasse Petersen, Nikolaj Thams, Gherardo Varando