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Fisher discriminant analysis with kernels
TLDR
A non-linear classification technique based on Fisher's discriminant which allows the efficient computation of Fisher discriminant in feature space and large scale simulations demonstrate the competitiveness of this approach. Expand
Kernel methods in machine learning
We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined onExpand
Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]
TLDR
This book addresses some theoretical aspects of semisupervised learning (SSL) and classify these methods into four classes that correspond to the first four main parts of the book (this would include generative models; low-density separation methods; graph-based methods; and algorithms). Expand
Quantifying causal influences
Many methods for causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between $n$ variables. Given the joint distribution on all these variables, the DAG containsExpand
On integral probability metrics, φ-divergences and binary classification
TLDR
A novel interpretation is provided for IPMs by relating them to binary classification, where it is shown that the IPM between class-conditional distributions is the negative of the optimal risk associated with a binary classifier. Expand
A Systematic Search for Transiting Planets in the K2 Data
TLDR
This work applies a method for searching K2 light curves for evidence of exoplanets by simultaneously fitting for these systematics and the transit signals of interest, and presents posterior distributions on the properties of each system based strictly on the transit observables. Expand
Kernel-based Tests for Joint Independence
We investigate the problem of testing whether $d$ random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two variableExpand
Toward Causal Representation Learning
TLDR
Fundamental concepts of causal inference are reviewed and related to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. Expand
Results of the GREAT08 Challenge?: an image analysis competition for cosmological lensing: Results o
We present the results of the Gravitational LEnsing Accuracy Testing 2008 (GREAT08) Challenge, a blind analysis challenge to infer weak gravitational lensing shear distortions from images. TheExpand
Influence Maximization in Continuous Time Diffusion Networks
TLDR
It is shown that selecting the set of most influential source nodes in the continuous time influence maximization problem is NP-hard and an efficient approximation algorithm with provable near-optimal performance is developed. Expand
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