538 citations
- University College LondonGB9 papers
- Gatsby Computational Neuroscience UnitGB6 papers
- Imperial College LondonGB2 papers
- University of CambridgeGB2 papers
- University of OxfordGB2 papers
- Atlanta Technical CollegeUS1 paper
- Carnegie Mellon UniversityUS1 paper
- Georgia Institute of TechnologyUS1 paper
- Helsinki Institute for Information TechnologyFI1 paper
- Laser Scan Engineering (United Kingdom)GB1 paper
- London School of Economics and Political ScienceGB1 paper
- Microsoft Research (United Kingdom)GB1 paper
11 papers
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Aapo Hyvarinen, Hiroaki Sasaki, Richard E. Turner
Nonlinear ICA is a fundamental problem for unsupervised representation learning, emphasizing the capacity to recover the underlying latent variables generating the data (i.e., iden…
A determinant-free method to simulate the parameters of large Gaussian fields
Louis Ellam, Heiko Strathmann, Mark Girolami +1
We propose a determinant-free approach for simulation-based Bayesian inference in high-dimensional Gaussian models. We introduce auxiliary variables with covariance equal to the in…
Adaptive regularization for Lasso models in the context of non-stationary data streams
Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana
Large scale, streaming datasets are ubiquitous in modern machine learning. Streaming algorithms must be scalable, amenable to incremental training and robust to the presence of non…
Interpretable Distribution Features with Maximum Testing Power
Wittawat Jitkrittum, Zoltan Szabo, Kacper Chwialkowski +1
Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations (i.e, f…
A Kernel Test of Goodness of Fit
Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton
We propose a nonparametric statistical test for goodness-of-fit: given a set of samples, the test determines how likely it is that these were generated from a target density functi…
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic
Complicated generative models often result in a situation where computing the likelihood of observed data is intractable, while simulating from the conditional density given a para…