89 citations · 153 across the 4 of their papers we have counts for
4 papers
Towards a Learning Theory of Cause-Effect Inference
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf +1
We pose causal inference as the problem of learning to classify probability distributions. In particular, we assume access to a collection , where each …
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…
The Randomized Causation Coefficient
David Lopez-Paz, Krikamol Muandet, Benjamin Recht
We are interested in learning causal relationships between pairs of random variables, purely from observational data. To effectively address this task, the state-of-the-art relies…
Learning from Distributions via Support Measure Machines
Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo +1
This paper presents a kernel-based discriminative learning framework on probability measures. Rather than relying on large collections of vectorial training examples, our framework…