6 papers
Invariance-Based Dynamic Regret Minimization
Margherita Lazzaretto, Jonas Peters, Niklas Pfister
We consider stochastic non-stationary linear bandits where the linear parameter connecting contexts to the reward changes over time. Existing algorithms in this setting localize th…
Invariant Subspace Decomposition
Margherita Lazzaretto, Jonas Peters, Niklas Pfister
We consider the task of predicting a response Y from a set of covariates X in settings where the conditional distribution of Y given X changes over time. For this to be feasible, a…
Causal Change Point Detection and Localization
Shimeng Huang, Jonas Peters, Niklas Pfister
Detecting and localizing change points in sequential data is of interest in many areas of application. Various notions of change points have been proposed, such as changes in mean,…
Boosted Control Functions: Distribution generalization and invariance in confounded models
Nicola Gnecco, Jonas Peters, Sebastian Engelke +1
Modern machine learning methods and the availability of large-scale data have significantly advanced our ability to predict target quantities from large sets of covariates. However…
Identifying Representations for Intervention Extrapolation
Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar +2
The premise of identifiable and causal representation learning is to improve the current representation learning paradigm in terms of generalizability or robustness. Despite recent…
Model-based causal feature selection for general response types
Lucas Kook, Sorawit Saengkyongam, Anton Rask Lundborg +2
Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal featur…