Nonlinear causal discovery with additive noise models (2009)
Hoyer, P.O., Janzing, D., Mooij, J.M., Peters, J., Schölkopf, B.
The discovery of causal relationships between a set of observed variables is a fundamental problem in science. For continuous-valued data linear acyclic causal models are often used because these...
Kernel Methods for Detecting the Direction of Time Series (2009)
Peters, J., Janzing, D., Gretton, A., Schölkopf, B.
We propose two kernel based methods for detecting the time direction in empirical time series. First we apply a Support Vector Machine on the finitedimensional distributions of the time series...
A simple PromiseBQP-complete matrix problem (2008)
Dominik Janzing, Pawel Wocjan, D. Janzing, P. Wocjan
Abstract: Let A be a real symmetric matrix of size N such that the number of non-zero entries in each row is polylogarithmic in N and the positions and the values of these entries are specified by an...
Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods (2008)
Sun, X., Janzing, D., Schölkopf, B.
We propose a method to quantify the complexity of conditional probability measures by a Hilbert space seminorm of the logarithm of its density. The concept of reproducing kernel Hilbert spaces...
Relating the Thermodynamic Arrow of Time to the Causal Arrow (2008)
Allahverdyan, A.A., Janzing, D.
Consider a Hamiltonian system that consists of a slow subsystem S and a fast subsystem F. The autonomous dynamics of S is driven by an effective Hamiltonian, but its thermodynamics is unexpected. We...
Janzing, D., Wocjan, P., Zhang, S.
In measurement-based quantum computation, quantum algorithms are implemented via sequences of measurements. We describe a translationally invariant finite-range interaction on a one-dimensional qudit...
Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods (2008)
Sun, X., Janzing, D., Schölkopf, B.
We propose a method to quantify the complexity of conditional probability measures by a Hilbert space seminorm of the logarithm of its density. The concept of reproducing kernel Hilbert spaces...
A kernel-based causal learning algorithm (2007)
Sun, X., Janzing, D., Schölkopf, B.
We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the...
Sun, X., Janzing, D., Schölkopf, B.
We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities....
A kernel-based causal learning algorithm (2007)
Sun, X., Janzing, D., Schölkopf, B., Fukumizu, K.
We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the...
Sun, X., Janzing, D., Schölkopf, B.
We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities....
Exploring the causal order of binary variables via exponential hierarchies of Markov kernels (2007)
We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n>=4) binary variables X_1,...,X_n. Our inference principle states that the...
Learning causality by identifying common effects with kernel-based dependence measures (2007)
We describe a method for causal inference that measures the strength of statistical dependence by the Hilbert-Schmidt norm of kernel-based conditional cross-covariance operators. We consider the...
Wocjan, P., Janzing, D., Beth, Th.
We use an n-spin system with permutation symmetric zz-interaction for simulating arbitrary pair-interaction Hamiltonians. The calculation of the required time overhead is mathematically equivalent to...