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Gunnar Rätsch

7 accepted papers

2022

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever
AISTATS 2022poster

We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully increasing the batch size over the course of t…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2021

Boosting Variational Inference With Locally Adaptive Step-Sizes

Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch
IJCAI 2021poster

Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Variational Inference allows practitioners to obtain increasingly good posterior approximations by spending more compute. Th…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2021

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
ICML 2021spotlight

Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated p…

Cited by 50Source ↗PDF ↗code ↗Scholar ↗
2021

Scalable Gaussian Process Variational Autoencoders

Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
AISTATS 2021poster

Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GP-VAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP infer…

Cited by 34Source ↗PDF ↗code ↗Scholar ↗
2021

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Khan Mohammad Emtiyaz
ICML 2021spotlight

Marginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data, which may not be readily available. In this work, we present a scalable marginal-likelihood estimation method to select…

Cited by 136Source ↗PDF ↗code ↗Scholar ↗
2020

Disentangling Factors of Variations Using Few Labels

Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
ICLR 2020poster

Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised met…

Cited by 210Source ↗Scholar ↗
2019

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
ICLR 2019poster

High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult t…

Cited by 205Source ↗PDF ↗code ↗Scholar ↗