16 papers at NeurIPS 2019 accepted
The Cluster of Excellence “Machine Learning" has 11 members featured across 16 of the accepted papers at this year's NeurIPS conference.
The 33rd Conference on Neural Information Processing Systems (NeurIPS) is held from Dec 8 - 14, 2019, in Vancouver, Canada. NeurIPS is the biggest conference on machine learning and computational neuroscience. The purpose of the annual meetings is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their field.
In 2019, NeurIPS received a record-breaking 6.743 paper submissions, of which 1.428 were accepted. The Cluster of Excellence “Machine Learning" has 11 members featured across 16 of the accepted papers at this year's NeurIPS conference.
List of titles of the papers by our members (check out the rest of the accepted papers here):
- Modeling Conceptual Understanding in Image Reference Games.
Rodolfo Corona Rodriguez, Stephan Alaniz, Zeynep Akata
- Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse.
Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens
- Learning from brains how to regularize machines.
Zhe Li, Wieland Brendel, Edgar Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian Sinz, Zachary Pitkow, Andreas Tolias
- Accurate, reliable and fast robustness evaluation.
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge
- Disentangled behavioural representations.
Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong
- Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs.
Pedro Mercado, Francesco Tudisco, Matthias Hein
- Provably robust boosted decision stumps and trees against adversarial attacks.
Maksym Andriushchenko, Matthias Hein
- Limitations of the empirical Fisher approximation for natural gradient descent.
Frederik Kunstner, Philipp Hennig, Lukas Balles
- Convergence Guarantees for Adaptive Bayesian Quadrature Methods.
Motonobu Kanagawa, Philipp Hennig
- Foundations of Comparison-Based Hierarchical Clustering.
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
- Control What You Can: Intrinsically Motivated Task-Planning Agent.
Sebastian Blaes, Marin Vlastelica Pogančić, Jiajie Zhu, Georg Martius
- Selecting causal brain features with a single conditional independence test per feature.
Atalanti Mastakouri, Bernhard Schölkopf, Dominik Janzing
- On the Fairness of Disentangled Representations.
Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
- On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset.
Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer
- Kernel Stein Tests for Multiple Model Comparison.
Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum
- Perceiving the arrow of time in autoregressive motion.
Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann