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Alexander Neitz
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2020 – today
- 2023
- [c10]Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Predicting Ordinary Differential Equations with Transformers. ICML 2023: 1978-2002 - [i9]Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Predicting Ordinary Differential Equations with Transformers. CoRR abs/2307.12617 (2023) - [i8]Kate Baumli, Satinder Baveja, Feryal M. P. Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei Zhang:
Vision-Language Models as a Source of Rewards. CoRR abs/2312.09187 (2023) - 2022
- [c9]Hsiao-Ru Pan, Nico Gürtler, Alexander Neitz, Bernhard Schölkopf:
Direct Advantage Estimation. NeurIPS 2022 - [i7]Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Discovering ordinary differential equations that govern time-series. CoRR abs/2211.02830 (2022) - 2021
- [c8]Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer:
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. ICLR 2021 - [c7]Alexander Neitz, Giambattista Parascandolo, Bernhard Schölkopf:
A teacher-student framework to distill future trajectories. ICLR 2021 - [c6]Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf:
Learning explanations that are hard to vary. ICLR 2021 - [c5]Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo:
Neural Symbolic Regression that scales. ICML 2021: 936-945 - [i6]Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo:
Neural Symbolic Regression that Scales. CoRR abs/2106.06427 (2021) - [i5]Hsiao-Ru Pan, Nico Gürtler, Alexander Neitz, Bernhard Schölkopf:
Direct Advantage Estimation. CoRR abs/2109.06093 (2021) - 2020
- [i4]Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber:
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning. CoRR abs/2004.11410 (2020) - [i3]Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf:
Learning explanations that are hard to vary. CoRR abs/2009.00329 (2020) - [i2]Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wüthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer:
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. CoRR abs/2010.04296 (2020)
2010 – 2019
- 2018
- [c4]Kyrill Schmid, Lenz Belzner, Marie Kiermeier, Alexander Neitz, Thomy Phan, Thomas Gabor, Claudia Linnhoff:
Risk-Sensitivity in Simulation Based Online Planning. KI 2018: 229-240 - [c3]Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf:
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. NeurIPS 2018: 9838-9848 - [i1]Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf:
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. CoRR abs/1808.04768 (2018) - 2016
- [c2]Thomas Gabor, Lenz Belzner, Marie Kiermeier, Michael Till Beck, Alexander Neitz:
A Simulation-Based Architecture for Smart Cyber-Physical Systems. ICAC 2016: 374-379 - [c1]Lenz Belzner, Alexander Neitz:
Learning relational probabilistic action models for online planning with decision forests. SAC 2016: 248-253
Coauthor Index
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