06.11.2023
35 Paper bei NeurIPS 2023 akzeptiert
Bei der diesjährigen NeurIPS-Konferenz wurden 35 Beiträge von Forschenden unseres Exzellenzclusters akzeptiert.
Die 37. Konferenz zu Neural Information Processing Systems (NeurIPS) findet in Präsenz am New Orleans Ernest N. Morial Convention Center in den USA vom 10. - 16. Dezember 2023 statt. NeurIPS ist die größte Konferenz für Maschinelles Lernen und Computational Neuroscience. Ziel der jährlichen Treffen ist es, den Forschungsaustausch zu neuronalen Informationsverarbeitungssysteme in ihren biologischen, technologischen, mathematischen und theoretischen Aspekten zu fördern. Der Schwerpunkt liegt auf peer-reviewed, neuartigen Forschungsarbeiten, die in einer allgemeinen Session vorgestellt und diskutiert werden, sowie auf eingeladenen Vorträgen von ausgewiesenen Experten.
In diesem Jahr ist unser Cluster mit 35 Papern auf der NeurIPS vertreten.
Liste der akzeptierten Beiträge unserer Mitglieder (alle Beiträge sind hier zu finden):
- Leonard Salewski, Isabel Rio-Torto, Stephan Alaniz, Eric Schulz, Zeynep Akata
In-Context Impersonation Reveals Large Language Models' Strengths and Biases - Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matt Botvinick, Jane Wang, Eric Schulz
Meta-in-context learning in large language models - Abhra Chaudhuri, Massimiliano Mancini, Zeynep Akata, Anjan Dutta
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships - Ilze Amanda Auzina, Çağatay Yıldız, Sara Magliacane, Matthias Bethge, Efstratios Gavves
Modulated Neural ODEs - Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, Wieland Brendel
Compositional Generalization from First Principles - Ori Press, Steffen Schneider, Matthias Kümmerer, Matthias Bethge
RDumb: A simple approach that questions our progress in continual test-time adaptation - Roland S. Zimmermann, Thomas Klein, Wieland Brendel
Scale Alone Does not Improve Mechanistic Interpretability in Vision Models - Tankred Saanum, Noemi Elteto, Peter Dayan, Marcel Binz, Eric Schulz
Reinforcement Learning with Simple Sequence Priors - Maximilian Mueller, Tiffany Vlaar, David Rolnick, Matthias Hein
Normalization Layers Are All That Sharpness-Aware Minimization Needs - Naman Deep Singh, Francesco Croce, Matthias Hein
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models - Jonathan Schmidt, Philipp Hennig, Jörg Nick, Filip Tronarp
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions - Nathanael Bosch, Philipp Hennig, Filip Tronarp
Probabilistic Exponential Integrators - Runa Eschenhagen, Alexander Immer, Richard Turner, Frank Schneider, Philipp Hennig
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures - Agustinus Kristiadi, Felix Dangel, Philipp Hennig
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization - Michael Kirchhof, Bálint Mucsányi, Seong Joon Oh, Dr. Enkelejda Kasneci
URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates - Moritz Haas, David Holzmüller, Ulrike Luxburg, Ingo Steinwart
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension - Basile Confavreux, Poornima Ramesh, Pedro Goncalves, Jakob H Macke, Tim Vogels
Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference - Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen Green, Jakob H Macke, Bernhard Schölkopf
Flow Matching for Scalable Simulation-Based Inference - Richard Gao, Michael Deistler, Jakob H Macke
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation - Marco Bagatella, Georg Martius
Goal-conditioned Offline Planning from Curious Exploration - Cansu Sancaktar, Justus Piater, Georg Martius
Regularity as Intrinsic Reward for Free Play - Pavel Kolev, Georg Martius, Michael Muehlebach
Online Learning under Adversarial Nonlinear Constraints - Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities - Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing - Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf
Controlling Text-to-Image Diffusion by Orthogonal Finetuning - Laurence Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
SE(3) Equivariant Augmented Coupling Flows - Siyuan Guo, Viktor Toth, Bernhard Schölkopf, Ferenc Huszar
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data - Liang Wendong, Armin Kekić, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf
Causal Component Analysis - Zhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele, Ojasv Kamal, Zhiheng LYU, Kevin Blin, Fernando Gonzalez Adauto, Max Kleiman-Weiner, Mrinmaya Sachan, Bernhard Schölkopf
CLadder: Assessing Causal Reasoning in Language Models - Julius von Kügelgen, Michel Besserve, Liang Wendong, Luigi Gresele, Armin Kekić, Elias Bareinboim, David Blei, Bernhard Schölkopf
Nonparametric Identifiability of Causal Representations from Unknown Interventions - Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello
Leveraging sparse and shared feature activations for disentangled representation learning - Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet
A Measure-Theoretic Axiomatisation of Causality - Cian Eastwood, Shashank Singh, Andrei L Nicolicioiu, Marin Vlastelica Pogančić, Julius von Kügelgen, Bernhard Schölkopf
Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features - Adrián Javaloy, Pablo Sanchez-Martin, Isabel Valera
Causal normalizing flows: from theory to practice - Alexandre Marthe, Aurélien Garivier, Claire Vernade
Beyond Average Reward in Markov Decision Processes