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6 papers · 2 filters
Kernel Mean Estimation via Spectral Filtering
Krikamol Muandet, Bharath Sriperumbudur, Bernhard Schölkopf
The problem of estimating the kernel mean in a reproducing kernel Hilbert space (RKHS) is central to kernel methods in that it is used by classical approaches (e.g., when centering…
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
Philipp Geiger, Kun Zhang, Mingming Gong +2
A widely applied approach to causal inference from a non-experimental time series , often referred to as "(linear) Granger causal analysis", is to regress present on past and in…
Inferring latent structures via information inequalities
R. Chaves, L. Luft, T. O. Maciel +3
One of the goals of probabilistic inference is to decide whether an empirically observed distribution is compatible with a candidate Bayesian network. However, Bayesian networks wi…
Kernel Mean Shrinkage Estimators
Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu +2
A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principa…
Justifying Information-Geometric Causal Inference
Dominik Janzing, Bastian Steudel, Naji Shajarisales +1
Information Geometric Causal Inference (IGCI) is a new approach to distinguish between cause and effect for two variables. It is based on an independence assumption between input d…
Randomized Nonlinear Component Analysis
David Lopez-Paz, Suvrit Sra, Alex Smola +2
Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to revea…