Search Results for author:
Found 13 papers, 7 papers with code
New methods for drug synergy prediction: a mini-review
In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens.
Scalable variable selection for two-view learning tasks with projection operators
With the projection operators the relationship, correlation, between sets of input and output variables can also be expressed by kernel functions, thus nonlinear correlation models can be exploited as well.
Vector-Valued Least-Squares Regression under Output Regularity Assumptions
We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output.
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
This paper introduces a novel and generic framework to solve the flagship task of supervised labeled graph prediction by leveraging Optimal Transport tools.
Learning primal-dual sparse kernel machines
In this paper, instead of using the traditional representer theorem, we propose to search for a solution in RKHS that has a pre-image decomposition in the original data space, where the elements don't necessarily correspond to the elements in the training set.
Learning Output Embeddings in Structured Prediction
A powerful and flexible approach to structured prediction consists in embedding the structured objects to be predicted into a feature space of possibly infinite dimension by means of output kernels, and then, solving a regression problem in this output space.
A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction
For learning the models, we present an efficient gradient-based algorithm that can be implemented in linear time in the sample size, order, rank of the tensor and the dimension of the input.
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear programming model, and most methods ignore the notable uncertainty in flux estimates.
A Tutorial on Canonical Correlation Methods
Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables.
Multi-view Kernel Completion
In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, (2) does not require any of the kernels to be complete a priori, and (3) can tackle non-linear kernels.
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be simultaneously input-dependent.
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees.
Multilabel Classification through Random Graph Ensembles
We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel and a kernel-based structured output learner as the base classifier.
