Cyber Valley ends third year on a high note at leading global Machine Learning conference

Stuttgart/Tübingen – With 27 accepted papers, researchers from the Cyber Valley ecosystem are set to make a strong showing at the upcoming 33rd Conference on Neural Information Processing Systems (NeurIPS). The world’s leading conference for machine learning is set to take place in Vancouver, Canada from December 8-14.

Submissions to NeurIPS have doubled since 2017, reflecting rapid growth in the field of machine learning. This year, a record-breaking 6743 papers were submitted to the conference, of which 1428 were accepted.

“Our solid presence at one of the world’s most competitive machine learning conferences clearly shows that scientists from Cyber Valley are among the very best,” said Professor Bernhard Schölkopf, Managing Director at the Max Planck Institute for Intelligent Systems in Tübingen and Director of the institute’s Empirical Inference department. “As Cyber Valley approaches its third anniversary, our success at NeurIPS unequivocally demonstrates the momentum the Stuttgart-Tübingen region has gained as a global hotspot for AI research.”

Founded in December 2016 in southwestern Germany, the Cyber Valley initiative is Europe’s leading research consortium in the field of artificial intelligence. According to a ranking based on the number of publications at the top global conferences in machine learning NeurIPS and ICML from 2009 to 2019, Cyber Valley’s academic partners currently place eigth worldwide, and take first place in Europe and Germany.

Leading research institutions worldwide, based on the number of publications at the two major machine learning conferences (NeurIPS/NIPS & ICML)1 (2009-2019)

Contributions from German research locations to the most important conferences for machine learning (NeurIPS/NIPS & ICML)2, in percent (2009 – 2019)

1. Quelle: dlpb, 2. Quelle: interne Analyse

“Research excellence is the main reason why the Cyber Valley initiative has grown in leaps and bounds since it was founded three years ago. An AI ecosystem has emerged in the heart of Baden-Württemberg that is unparalleled in Europe,” says Schölkopf. This ecosystem has seen several major milestones in the past month alone, among them the accession of Fraunhofer Gesellschaft; the launch of the new Cyber Valley Start-Up Network; additional investments by industrial partners Robert Bosch GmbH and Amazon in Tübingen; and the successful close of a third round of applications at the International Max Planck Research School for Intelligent Systems, the PhD program which was founded as part of the Cyber Valley initiative. 

NeurIPS 2019: Accepted Submissions from the Cyber Valley Ecosystem (alphabetical)

1. Provably robust boosted decision stumps and trees against adversarial attacks
   Maksym Andriushchenko (EPFL) Matthias Hein (University of Tübingen)
    
2. Control What You Can: Intrinsically Motivated Task-Planning Agent
   Sebastian Blaes (Max Planck Institute for Intelligent Systems) Marin Vlastelica Pogan (Max-Planck Institute for Intelligent Systems, Tübingen) Jia-Jie Zhu (Max Planck Institute for Intelligent Systems) Georg Martius (MPI for Intelligent Systems)
    
3. Accurate, reliable and fast robustness evaluation
   Wieland Brendel (AG Bethge, University of Tübingen) Jonas Rauber (University of Tübingen) Matthias Kämmerer (University of Tübingen) Ivan Ustyuzhaninov (University of Tübingen) Matthias Bethge (University of Tübingen)
    
4. Disentangled behavioural representations
   Amir Dezfouli (Data61, CSIRO) Hassan Ashtiani (McMaster University) Omar Ghattas (CSIRO) Richard Nock (Data61, the Australian National University and the University of Sydney) Peter Dayan (Max Planck Institute for Biological Cybernetics) Cheng Soon Ong (Data61 and ANU)
    
5. Foundations of Comparison-Based Hierarchical Clustering
   Debarghya Ghoshdastidar (University of Tübingen) MichaÎl Perrot (Max Planck Institute for Intelligent Systems) Ulrike von Luxburg (University of Tübingen)
    
6. On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset
   Muhammad Waleed Gondal (Max Planck Institute for Intelligent Systems) Manuel Wuthrich (Max Planck Institute for Intelligent Systems) Djordje Miladinovic (ETH Zurich) Francesco Locatello (ETH Zürich - MPI Tübingen) Martin Breidt (MPI for Biological Cybernetics)  Valentin Volchkov (Max Planck Institute for Intelligent Systems) Joel Akpo (Max Planck Institute for Intelligent Systems) Olivier Bachem (Google Brain) Bernhard Schölkopf (MPI for Intelligent Systems) Stefan Bauer (MPI for Intelligent Systems)
7. Grid Saliency for Context Explanations of Semantic Segmentation
   Lukas Hoyer (Bosch Center for Artificial Intelligence) · Mauricio Munoz (Bosch Center for Artificial Intelligence) · Prateek Katiyar (Bosch Center for Artificial Intelligence) · Anna Khoreva (Bosch Center for Artificial Intelligence) · Volker Fischer (Robert Bosch GmbH, Bosch Center for Artificial Intelligence)
    
8. Causal regularization
   Dominik Janzing (Amazon)
    
9. Convergence Guarantees for Adaptive Bayesian Quadrature Methods
   Motonobu Kanagawa (EURECOM) Philipp Hennig (University of Tübingen and MPI for Intelligent Systems Tübingen)
    
10. On Tractable Computation of Expected Predictions
    Pasha Khosravi (UCLA) YooJung Choi (UCLA) Yitao Liang (UCLA) Antonio Vergari (Max Planck Institute for Intelligent Systems) Guy Van den Broeck (UCLA)
    
     
11. Limitations of the empirical Fisher approximation
    Frederik Kunstner (EPFL) Philipp Hennig (University of Tübingen and MPI for Intelligent Systems Tübingen) Lukas Balles (University of Tuebingen)
     
12. Learning from brains how to regularize machines
    Zhe Li (Baylor College of Medicine) Wieland Brendel (AG Bethge, University of Tübingen) Edgar Walker (Baylor College of Medicine) Erick Cobos (Baylor College of Medicine) Taliah Muhammad (Baylor College of Medicine) Jacob Reimer (Baylor College of Medicine) Matthias Bethge (University of Tübingen) Fabian Sinz (University Tübingen) Zachary Pitkow (BCM/Rice) Andreas Tolias (Baylor College of Medicine)
     
13. On the Fairness of Disentangled Representations
    Francesco Locatello (ETH Zürich - MPI T¸bingen) Gabriele Abbati (University of Oxford) Tom Rainforth (University of Oxford) Stefan Bauer (MPI for Intelligent Systems) Bernhard Schölkopf (MPI for Intelligent Systems) Olivier Bachem (Google Brain)
     
14. Stochastic Frank-Wolfe for Composite Convex Minimization
    Francesco Locatello (ETH Zürich - MPI Tübingen) Alp Yurtsever (EPFL) Olivier Fercoq (Telecom ParisTech) Volkan Cevher (EPFL)
     
15. The Functional Neural Process
    Christos Louizos (University of Amsterdam) Xiahan Shi (Bosch Center for Artificial Intelligence) Klamer Schutte (TNO) Max Welling (University of Amsterdam / Qualcomm AI Research)
     
16. Fisher Efficient Inference of Intractable Models
    Song Liu (University of Bristol) Takafumi Kanamori (Tokyo Institute of Technology/RIKEN) Wittawat Jitkrittum (Max Planck Institute for Intelligent Systems) Yu Chen (University of Bristol)
     
17. Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
    Pedro Mercado (University of Tübingen) Francesco Tudisco (University of Strathclyde) Matthias Hein (University of Tübingen)
     
18. Selecting causal brain features with a single conditional independence test per feature
    Atalanti Mastakouri (Max Planck Institute for Intelligent Systems) Bernhard Schölkopf (MPI for Intelligent Systems) Dominik Janzing (Amazon)
     
19. Beating SGD Saturation with Tail-Averaging and Minibatching
    Nicole Muecke (University of Stuttgart) Gergely Neu (Universitat Pompeu Fabra) Lorenzo Rosasco (University of Genova – MIT – IIT)
     
20. Perceiving the arrow of time in autoregressive motion
    Kristof Meding (Max Planck Institute for Intelligent Systems) Dominik Janzing (Amazon) Bernhard Schölkopf (MPI for Intelligent Systems) Felix A. Wichmann (University of Tübingen)
     
21. DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision
    Tam Nguyen (Freiburg Computer Vision Lab) Maximilian Dax (Bosch GmbH) Chaithanya Kumar Mummadi (Bosch Center for Artificial Intelligence) Nhung Ngo (Bosch Center for Artificial Intelligence) · Thi Hoai Phuong Nguyen (Karlsruhe Institute of Technology (KIT))  Zhongyu Lou (Robert Bosch Gmbh) Thomas Brox (University of Freiburg)
     
22. Practical and Consistent Estimation of f-Divergences
    Paul Rubenstein (Max Planck Institute for Intelligent Systems) Olivier Bousquet (Google Brain (Zurich)) Josip Djolonga (Google Research, Brain Team) Carlos Riquelme (Google Brain) Ilya Tolstikhin (MPI for Intelligent Systems)
     
23. Kernel Stein Tests for Multiple Model Comparison
    Jen Ning Lim (Max Planck Institute for Intelligent Systems) Makoto Yamada (Kyoto University / RIKEN AIP) Bernhard Schölkopf (MPI for Intelligent Systems) Wittawat Jitkrittum (Max Planck Institute for Intelligent Systems)
     
24. Invert to Learn to Invert
    Patrick Putzky (University of Amsterdam, Max Planck Institute for Intelligent Systems), Max Welling (University of Amsterdam)
     
25. Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse
    Cornelius Schröder (University of Tübingen) Ben James (University of Sussex) Leon Lagnado (University of Sussex) Philipp Berens (University of Tübingen)
     
26. Are Disentangled Representations Helpful for Abstract Visual Reasoning?
    Sjoerd van Steenkiste (The Swiss AI Lab - IDSIA) Francesco Locatello (ETH Zürich - MPI Tübingen) Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI) - NNAISENSE) Olivier Bachem (Google Brain)
     
27. Progressive Augmentation of GANs
    Dan Zhang (Bosch Center for Artificial Intelligence) · Anna Khoreva (Bosch Center for Artificial Intelligence)