- Generic approach to learn continuum number of tasks jointly via vector-valued RKHSs.
- cost-sensitive classification,
- quantile regression,
- density level set estimation.
Adaptive linear-time nonparametric hypothesis tests:
- Adaptivity: features & kernel parameters are chosen to optimize power. The power of the tests matches quadratic-time tests. The returned features indicate why the two distributions differ.
- NLP (distinguishing articles from two categories),
- computer vision (differentiating positive and negative emotions).
Applications: dependency testing of media annotations:
- song - year of release,
- video - caption.
- Best Paper Award @ NIPS-2017! (=top 0.09%)
- Application: analysis of criminal data.
Information Theoretical Estimators (ITE) Toolbox [in Python, Matlab]:
- Large number of estimators for entropy, mutual information, divergence, association measures, kernels on probability distributions.
- independent subspace analysis,
- outlier-robust image registration,
- distribution regression (aerosol prediction from multispectral satellite images).
KMC (Kernel Hamiltonian Monte Carlo):
- A gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities where classical HMC is not an option due to intractable gradients, KMC adaptively learns the target's gradient structure from the sample path, by fitting an exponential family model in a Reproducing Kernel Hilbert Space.
- sampling from the marginal posterior of hyper-parameters in Gaussian process classification,
- approximate Bayesian computation.
LL-LVM (Locally Linear Latent Variable Model):
- A probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships; 'Bayesian LLE'.
- Application: climate data analysis.
Kernel-EP (Kernel based just-in-time Expectation Propagation):
- A fast, online algorithm for nonparametric learning of EP message updates.
- It extends Infer.NET.
OSDL (Online Group-Structured Dictionary Learning)
- Structured-sparse dictionary learning method which (i) is online, (ii) allows overlapping group structures with (iii) non-convex group-structure inducing regularization, and (iv) handles incomplete observations.
- collaborative filtering,
- image inpainting.