268 citations · 518 across the 22 of their papers we have counts for
23 papers
Preference Elicitation for Offline Reinforcement Learning
Alizée Pace, Bernhard Schölkopf, Gunnar Rätsch +1
Applying reinforcement learning (RL) to real-world problems is often made challenging by the inability to interact with the environment and the difficulty of designing reward funct…
Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat, Alexander Immer, Hugo Yèche +2
Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks. However, they lack inherent mechanisms…
On the Importance of Clinical Notes in Multi-modal Learning for EHR Data
Severin Husmann, Hugo Yèche, Gunnar Rätsch +1
Understanding deep learning model behavior is critical to accepting machine learning-based decision support systems in the medical community. Previous research has shown that joint…
HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
Hugo Yèche, Rita Kuznetsova, Marc Zimmermann +4
The recent success of machine learning methods applied to time series collected from Intensive Care Units (ICU) exposes the lack of standardized machine learning benchmarks for dev…
A Commentary on the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In…
Communication-Efficient Jaccard Similarity for High-Performance Distributed Genome Comparisons
Maciej Besta, Raghavendra Kanakagiri, Harun Mustafa +4
The Jaccard similarity index is an important measure of the overlap of two sets, widely used in machine learning, computational genomics, information retrieval, and many other area…
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
Laura Manduchi, Matthias Hüser, Julia Vogt +2
Generating interpretable visualizations from complex data is a common problem in many applications. Two key ingredients for tackling this issue are clustering and representation le…
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
Stefan G. Stark, Stephanie L. Hyland, Melanie F. Pradier +5
The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into…
Disentangling Factors of Variation Using Few Labels
Francesco Locatello, Michael Tschannen, Stefan Bauer +3
Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentangleme…
Machine learning for early prediction of circulatory failure in the intensive care unit
Stephanie L. Hyland, Martin Faltys, Matthias Hüser +12
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to proce…
Improving Clinical Predictions through Unsupervised Time Series Representation Learning
Xinrui Lyu, Matthias Hueser, Stephanie L. Hyland +2
In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order…
Scalable Gaussian Processes on Discrete Domains
Vincent Fortuin, Gideon Dresdner, Heiko Strathmann +1
Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel me…
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
Vincent Fortuin, Matthias Hüser, Francesco Locatello +2
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 interpretabl…
Boosting Black Box Variational Inference
Francesco Locatello, Gideon Dresdner, Rajiv Khanna +2
Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by…
Competitive Training of Mixtures of Independent Deep Generative Models
Francesco Locatello, Damien Vincent, Ilya Tolstikhin +3
A common assumption in causal modeling posits that the data is generated by a set of independent mechanisms, and algorithms should aim to recover this structure. Standard unsupervi…
On Matching Pursuit and Coordinate Descent
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy +4
Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former…
Boosting Variational Inference: an Optimization Perspective
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh +1
Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one. Recently, boosting variational inference has been p…
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch
Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN…
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Francesco Locatello, Michael Tschannen, Gunnar Rätsch +1
Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolfe (FW) algorithms regained popularity in recent years due to their simplicity, effectiveness and theoretical…
Learning Unitary Operators with Help From u(n)
Stephanie L. Hyland, Gunnar Rätsch
A major challenge in the training of recurrent neural networks is the so-called vanishing or exploding gradient problem. The use of a norm-preserving transition operator can addres…
Knowledge Transfer with Medical Language Embeddings
Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
Identifying relationships between concepts is a key aspect of scientific knowledge synthesis. Finding these links often requires a researcher to laboriously search through scien- t…
Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis
Christian Widmer, Marius Kloft, Vipin T Sreedharan +1
We present a general regularization-based framework for Multi-task learning (MTL), in which the similarity between tasks can be learned or refined using -norm Multiple Kern…
Probabilistic Clustering of Time-Evolving Distance Data
Julia E. Vogt, Marius Kloft, Stefan Stark +4
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the in…