Author pages are created from data sourced from our academic publisher partnerships and public sources.

Publications Influence

Share This Author

Fisher discriminant analysis with kernels

- S. Mika, Gunnar Rätsch, J. Weston, B. Scholkopf, K.R. Mullers
- Mathematics, Computer Science
- Neural Networks for Signal Processing IX…
- 23 August 1999

TLDR

Kernel methods in machine learning

- Thomas Hofmann, B. Scholkopf, Alex Smola
- Mathematics
- 31 January 2007

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 on… Expand

Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]

- O. Chapelle, B. Scholkopf, A. Zien, Eds.
- Computer Science
- IEEE Transactions on Neural Networks
- 24 February 2009

TLDR

Quantifying causal influences

- D. Janzing, D. Balduzzi, M. Grosse-Wentrup, B. Scholkopf
- Mathematics, Engineering
- 29 March 2012

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 contains… Expand

On integral probability metrics, φ-divergences and binary classification

- Bharath K. Sriperumbudur, K. Fukumizu, A. Gretton, B. Scholkopf, G. Lanckriet
- Mathematics, Computer Science
- 18 January 2009

TLDR

A Systematic Search for Transiting Planets in the K2 Data

- D. Foreman-Mackey, B. Montet, D. Hogg, T. Morton, Dun Wang, B. Scholkopf
- Computer Science, Physics
- 16 February 2015

TLDR

Kernel-based Tests for Joint Independence

- Niklas Pfister, Peter Buhlmann, B. Scholkopf, J. Peters
- Mathematics
- 1 March 2016

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 variable… Expand

Toward Causal Representation Learning

- B. Scholkopf, Francesco Locatello, +4 authors Yoshua Bengio
- Computer Science
- Proceedings of the IEEE
- 22 February 2021

TLDR

Results of the GREAT08 Challenge?: an image analysis competition for cosmological lensing: Results o

- S. Bridle, S. Balan, +31 authors D. Wittman
- Physics
- 7 August 2009

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. The… Expand

Influence Maximization in Continuous Time Diffusion Networks

- M. G. Rodriguez, B. Scholkopf
- Computer Science, Physics
- ICML
- 8 May 2012

TLDR

...

1

2

3

4

5

...