Search Results for author: Stefan Bauer

Found 92 papers, 42 papers with code

Opportunities for machine learning in scientific discovery

no code implementations7 May 2024 Ricardo Vinuesa, Jean Rabault, Hossein Azizpour, Stefan Bauer, Bingni W. Brunton, Arne Elofsson, Elias Jarlebring, Hedvig Kjellstrom, Stefano Markidis, David Marlevi, Paola Cinnella, Steven L. Brunton

Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques across various fields.

Derivative-free tree optimization for complex systems

1 code implementation5 Apr 2024 Ye Wei, Bo Peng, Ruiwen Xie, Yangtao Chen, Yu Qin, Peng Wen, Stefan Bauer, Po-Yen Tung

Our method demonstrates wide applicability to a wide range of real-world complex systems spanning materials, physics, and biology, considerably outperforming state-of-the-art algorithms.

The Essential Role of Causality in Foundation World Models for Embodied AI

no code implementations6 Feb 2024 Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Marc Rigter, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang

The study of causality lends itself to the construction of veridical world models, which are crucial for accurately predicting the outcomes of possible interactions.


DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design

1 code implementation7 Dec 2023 Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab

The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms.

Experimental Design

Causal machine learning for single-cell genomics

no code implementations23 Oct 2023 Alejandro Tejada-Lapuerta, Paul Bertin, Stefan Bauer, Hananeh Aliee, Yoshua Bengio, Fabian J. Theis

Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells.

Experimental Design

Benchmarking Offline Reinforcement Learning on Real-Robot Hardware

2 code implementations28 Jul 2023 Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius

To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging.

Benchmarking reinforcement-learning

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery

1 code implementation NeurIPS 2023 Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks.

Causal Discovery Variational Inference

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

no code implementations11 Jul 2023 Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer, Yoshua Bengio

Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference.

Benchmarking Causal Discovery +2

DRCFS: Doubly Robust Causal Feature Selection

no code implementations12 Jun 2023 Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer

Knowing the features of a complex system that are highly relevant to a particular target variable is of fundamental interest in many areas of science.

feature selection

Understanding Causality with Large Language Models: Feasibility and Opportunities

no code implementations11 Apr 2023 Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan

We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question.

Decision Making

Differentiable Multi-Target Causal Bayesian Experimental Design

1 code implementation21 Feb 2023 Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer

We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky.

Causal Discovery Experimental Design

Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery

no code implementations NeurIPS 2023 Mateusz Olko, Michał Zając, Aleksandra Nowak, Nino Scherrer, Yashas Annadani, Stefan Bauer, Łukasz Kuciński, Piotr Miłoś

In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function.

Causal Discovery Experimental Design

Federated Causal Discovery From Interventions

3 code implementations7 Nov 2022 Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou

We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples.

Causal Discovery Federated Learning +1

From Points to Functions: Infinite-dimensional Representations in Diffusion Models

1 code implementation25 Oct 2022 Sarthak Mittal, Guillaume Lajoie, Stefan Bauer, Arash Mehrjou

Consequently, it is reasonable to ask if there is an intermediate time step at which the preserved information is optimal for a given downstream task.


Learning Latent Structural Causal Models

no code implementations24 Oct 2022 Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou

For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions.

Bayesian Inference Image Generation +1

Invariant Causal Mechanisms through Distribution Matching

1 code implementation23 Jun 2022 Mathieu Chevalley, Charlotte Bunne, Andreas Krause, Stefan Bauer

Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks.

Domain Generalization

On the Generalization and Adaption Performance of Causal Models

no code implementations9 Jun 2022 Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, Nan Rosemary Ke

Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes and offer robust generalization.

Causal Discovery Out-of-Distribution Generalization

Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks

1 code implementation19 May 2022 Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel O'Connor, Manuel Wüthrich, Felix Widmaier, Stefan Bauer, Stephen J. Redmond

Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory.

Transfer Learning

Interventions, Where and How? Experimental Design for Causal Models at Scale

1 code implementation3 Mar 2022 Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer

Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target.

Causal Discovery Experimental Design

Bayesian Structure Learning with Generative Flow Networks

1 code implementation28 Feb 2022 Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio

In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data.

Variational Inference

Compositional Multi-Object Reinforcement Learning with Linear Relation Networks

no code implementations31 Jan 2022 Davide Mambelli, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, Francesco Locatello

Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge.

Object reinforcement-learning +2

Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations

1 code implementation20 Jan 2022 Simon Bing, Andrea Dittadi, Stefan Bauer, Patrick Schwab

We demonstrate experimentally that HealthGen generates synthetic cohorts that are significantly more faithful to real patient EHRs than the current state-of-the-art, and that augmenting real data sets with conditionally generated cohorts of underrepresented subpopulations of patients can significantly enhance the generalisability of models derived from these data sets to different patient populations.

Time Series Time Series Analysis

GeneDisco: A Benchmark for Experimental Design in Drug Discovery

2 code implementations ICLR 2022 Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab

GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.

Active Learning Drug Discovery +1

On the interventional consistency of autoencoders

no code implementations29 Sep 2021 Giulia Lanzillotta, Felix Leeb, Stefan Bauer, Bernhard Schölkopf

Autoencoders have played a crucial role in the field of representation learning since its inception, proving to be a flexible learning scheme able to accommodate various notions of optimality of the representation.


Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger

1 code implementation22 Aug 2021 Arthur Allshire, Mayank Mittal, Varun Lodaya, Viktor Makoviychuk, Denys Makoviichuk, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Ankur Handa, Animesh Garg

We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator.


The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents

no code implementations ICLR 2022 Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

By training 240 representations and over 10, 000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of pretrained VAE-based representations affect the OOD generalization of downstream agents.

Reinforcement Learning (RL) Representation Learning

Exploring the Latent Space of Autoencoders with Interventional Assays

1 code implementation30 Jun 2021 Felix Leeb, Stefan Bauer, Michel Besserve, Bernhard Schölkopf

Autoencoders exhibit impressive abilities to embed the data manifold into a low-dimensional latent space, making them a staple of representation learning methods.


Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

1 code implementation14 Jun 2021 Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.

Bayesian Inference Causal Inference +2

Representation Learning in Continuous-Time Score-Based Generative Models

no code implementations ICML Workshop INNF 2021 Korbinian Abstreiter, Stefan Bauer, Arash Mehrjou

Score-based methods represented as stochastic differential equations on a continuous time domain have recently proven successful as a non-adversarial generative model.

Denoising Representation Learning

Diffusion-Based Representation Learning

no code implementations29 May 2021 Korbinian Abstreiter, Sarthak Mittal, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou

In contrast, the introduced diffusion-based representation learning relies on a new formulation of the denoising score matching objective and thus encodes the information needed for denoising.

Denoising Representation Learning +1

Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases

1 code implementation24 Mar 2021 Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Samir Bhatt, Bernhard Schölkopf, Stefan Bauer

Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak.

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments

no code implementations20 Mar 2021 Sonali Parbhoo, Stefan Bauer, Patrick Schwab

Estimating an individual's potential response to interventions from observational data is of high practical relevance for many domains, such as healthcare, public policy or economics.

counterfactual Counterfactual Inference +1

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling

1 code implementation ICLR 2021 Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann

We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class.

Decoder Density Estimation

Dependency Structure Discovery from Interventions

no code implementations1 Jan 2021 Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Bernhard Schölkopf, Michael Curtis Mozer, Hugo Larochelle, Christopher Pal, Yoshua Bengio

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.

Spatially Structured Recurrent Modules

no code implementations ICLR 2021 Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution.

Starcraft II Video Prediction

Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation

1 code implementation29 Nov 2020 August DuMont Schütte, Jürgen Hetzel, Sergios Gatidis, Tobias Hepp, Benedikt Dietz, Stefan Bauer, Patrick Schwab

Our study offers valuable guidelines and outlines practical conditions under which insights derived from synthetic medical images are similar to those that would have been derived from real imaging data.

Computed Tomography (CT) Image Generation +3

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

no code implementations27 Oct 2020 Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.


On the Transfer of Disentangled Representations in Realistic Settings

no code implementations ICLR 2021 Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf

Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning.


Function Contrastive Learning of Transferable Meta-Representations

no code implementations14 Oct 2020 Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wüthrich, Bernhard Schölkopf

This \emph{meta-representation}, which is computed from a few observed examples of the underlying function, is learned jointly with the predictive model.

Contrastive Learning Decoder +1

Function Contrastive Learning of Transferable Representations

no code implementations28 Sep 2020 Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wuthrich, Bernhard Schölkopf

Few-shot-learning seeks to find models that are capable of fast-adaptation to novel tasks which are not encountered during training.

Contrastive Learning Few-Shot Learning

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records

no code implementations31 Aug 2020 Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients.


A Commentary on the Unsupervised Learning of Disentangled Representations

no code implementations28 Jul 2020 Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

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.

S2RMs: Spatially Structured Recurrent Modules

no code implementations13 Jul 2020 Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution.

Starcraft II Video Prediction

Causal Feature Selection via Orthogonal Search

no code implementations6 Jul 2020 Ashkan Soleymani, Anant Raj, Stefan Bauer, Bernhard Schölkopf, Michel Besserve

The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines.

Causal Discovery feature selection

Structure by Architecture: Structured Representations without Regularization

no code implementations14 Jun 2020 Felix Leeb, Guilia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf

We study the problem of self-supervised structured representation learning using autoencoders for downstream tasks such as generative modeling.


A machine learning route between band mapping and band structure

1 code implementation20 May 2020 Rui Patrick Xian, Vincent Stimper, Marios Zacharias, Shuo Dong, Maciej Dendzik, Samuel Beaulieu, Bernhard Schölkopf, Martin Wolf, Laurenz Rettig, Christian Carbogno, Stefan Bauer, Ralph Ernstorfer

Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials.

Data Analysis, Statistics and Probability Materials Science Computational Physics

Clinical Predictive Models for COVID-19: Systematic Study

no code implementations17 May 2020 Patrick Schwab, August DuMont Schütte, Benedikt Dietz, Stefan Bauer

Here, we study clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care.


Disentangling Factors of Variations Using Few Labels

no code implementations ICLR Workshop LLD 2019 Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem

Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations.

Disentanglement Model Selection

SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives

1 code implementation5 Mar 2020 Emmanouil Angelis, Philippe Wenk, Bernhard Schölkopf, Stefan Bauer, Andreas Krause

Gaussian processes are an important regression tool with excellent analytic properties which allow for direct integration of derivative observations.

Gaussian Processes regression

Causal models for dynamical systems

no code implementations17 Jan 2020 Jonas Peters, Stefan Bauer, Niklas Pfister

In this chapter, we provide a natural and straight-forward extension of this concept to dynamical systems, focusing on continuous time models.

Methodology Dynamical Systems

Learning Neural Causal Models from Unknown Interventions

2 code implementations2 Oct 2019 Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael C. Mozer, Chris Pal, Yoshua Bengio

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.


Multidimensional Contrast Limited Adaptive Histogram Equalization

1 code implementation26 Jun 2019 Vincent Stimper, Stefan Bauer, Ralph Ernstorfer, Bernhard Schölkopf, R. Patrick Xian

Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision.

Disentangled State Space Representations

no code implementations7 Jun 2019 Đorđe Miladinović, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer

Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist.

regression Transfer Learning

On the Fairness of Disentangled Representations

no code implementations NeurIPS 2019 Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem

Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks.

Disentanglement Fairness

Disentangling Factors of Variation Using Few Labels

no code implementations3 May 2019 Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem

Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations.

Disentanglement Model Selection

Orthogonal Structure Search for Efficient Causal Discovery from Observational Data

no code implementations6 Mar 2019 Anant Raj, Luigi Gresele, Michel Besserve, Bernhard Schölkopf, Stefan Bauer

The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines.

Causal Discovery regression

ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

2 code implementations17 Feb 2019 Philippe Wenk, Gabriele Abbati, Michael A. Osborne, Bernhard Schölkopf, Andreas Krause, Stefan Bauer

Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting.

Gaussian Processes Model Selection +1

Bayesian Online Prediction of Change Points

1 code implementation12 Feb 2019 Diego Agudelo-España, Sebastian Gomez-Gonzalez, Stefan Bauer, Bernhard Schölkopf, Jan Peters

Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences.

Bayesian Inference Change Point Detection

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

1 code implementation3 Feb 2019 Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy.

counterfactual Model Selection

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

8 code implementations ICML 2019 Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.


Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

no code implementations31 Oct 2018 Raphael Suter, Đorđe Miladinović, Bernhard Schölkopf, Stefan Bauer

The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is important for data efficient and robust use of neural networks.


Learning stable and predictive structures in kinetic systems: Benefits of a causal approach

no code implementations28 Oct 2018 Niklas Pfister, Stefan Bauer, Jonas Peters

Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective.

Causal Inference Model Selection

Scalable Variational Inference for Dynamical Systems

1 code implementation NeurIPS 2017 Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann

That is why, despite the high computational cost, numerical integration is still the gold standard in many applications.

Numerical Integration Variational Inference

Mean-Field Variational Inference for Gradient Matching with Gaussian Processes

no code implementations21 Oct 2016 Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann

The essence of gradient matching is to model the prior over state variables as a Gaussian process which implies that the joint distribution given the ODE's and GP kernels is also Gaussian distributed.

Gaussian Processes Variational Inference

Multi-Organ Cancer Classification and Survival Analysis

no code implementations2 Jun 2016 Stefan Bauer, Nicolas Carion, Peter Schüffler, Thomas Fuchs, Peter Wild, Joachim M. Buhmann

Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology.

Classification General Classification +3

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