278 citations · 832 across the 38 of their papers we have counts for
9 papers · 1 filter
Disentangled State Space Representations
Đorđe Miladinović, Muhammad Waleed Gondal, Bernhard Schölkopf +2
Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist. To specifically learn cross-domain sequence representations,…
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Philippe Wenk, Alkis Gotovos, Stefan Bauer +3
Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data ha…
Scalable Variational Inference for Dynamical Systems
Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
Gradient matching is a promising tool for learning parameters and state dynamics of ordinary differential equations. It is a grid free inference approach, which, for fully observab…
Scalable Adaptive Stochastic Optimization Using Random Projections
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher +2
Adaptive stochastic gradient methods such as AdaGrad have gained popularity in particular for training deep neural networks. The most commonly used and studied variant maintains a…
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes
Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
Gradient matching with Gaussian processes is a promising tool for learning parameters of ordinary differential equations (ODE's). The essence of gradient matching is to model the p…
Model Selection for Gaussian Process Regression by Approximation Set Coding
Benjamin Fischer, Nico Gorbach, Stefan Bauer +2
Gaussian processes are powerful, yet analytically tractable models for supervised learning. A Gaussian process is characterized by a mean function and a covariance function (kernel…
Fast and Robust Least Squares Estimation in Corrupted Linear Models
Brian McWilliams, Gabriel Krummenacher, Mario Lucic +1
Subsampling methods have been recently proposed to speed up least squares estimation in large scale settings. However, these algorithms are typically not robust to outliers or corr…
Correlated random features for fast semi-supervised learning
Brian McWilliams, David Balduzzi, Joachim M. Buhmann
This paper presents Correlated Nystrom Views (XNV), a fast semi-supervised algorithm for regression and classification. The algorithm draws on two main ideas. First, it generates t…
Learning Dictionaries with Bounded Self-Coherence
Christian D. Sigg, Tomas Dikk, Joachim M. Buhmann
Sparse coding in learned dictionaries has been established as a successful approach for signal denoising, source separation and solving inverse problems in general. A dictionary le…