Francesco Locatello

CoRR, 2024

Adjusting Pretrained Backbones for Performativity.

[BibT_eX]

[DOI]

Celestine Mendler-Dünner

CoRR, 2024

Unifying Causal Representation Learning with the Invariance Principle.

[BibT_eX]

[DOI]

CoRR, 2024

NeCo: Improving DINOv2's spatial representations in 19 GPU hours with Patch Neighbor Consistency.

[BibT_eX]

[DOI]

CoRR, 2024

Score matching through the roof: linear, nonlinear, and latent variables causal discovery.

[BibT_eX]

[DOI]

Francesco Montagna

Philipp Michael Faller

Patrick Blöbaum

Elke Kirschbaum

CoRR, 2024

Latent Space Translation via Inverse Relative Projection.

[BibT_eX]

[DOI]

CoRR, 2024

Latent Functional Maps.

[BibT_eX]

[DOI]

CoRR, 2024

Scalable unsupervised alignment of general metric and non-metric structures.

[BibT_eX]

[DOI]

Alexander M. Bronstein

CoRR, 2024

Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention.

[BibT_eX]

[DOI]

Avinash Kori

Ainkaran Santhirasekaram

Francesca Toni

Ben Glocker

Fabio De Sousa Ribeiro

CoRR, 2024

Smoke and Mirrors in Causal Downstream Tasks.

[BibT_eX]

[DOI]

CoRR, 2024

Demystifying amortized causal discovery with transformers.

[BibT_eX]

[DOI]

CoRR, 2024

Marrying Causal Representation Learning with Dynamical Systems for Science.

[BibT_eX]

[DOI]

Dingling Yao

Caroline Muller

CoRR, 2024

Two Tricks to Improve Unsupervised Segmentation Learning.

[BibT_eX]

[DOI]

Alp Eren Sari

Paolo Favaro

CoRR, 2024

Mechanistic Neural Networks for Scientific Machine Learning.

[BibT_eX]

[DOI]

Adeel Pervez

Efstratios Gavves

CoRR, 2024

Binding Dynamics in Rotating Features.

[BibT_eX]

[DOI]

Sindy Löwe

Max Welling

CoRR, 2024

A Sparsity Principle for Partially Observable Causal Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Mechanistic Neural Networks for Scientific Machine Learning.

[BibT_eX]

[DOI]

Adeel Pervez

Stratis Gavves

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Unsupervised Concept Discovery Mitigates Spurious Correlations.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Multi-View Causal Representation Learning with Partial Observability.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Grounded Object-Centric Learning.

[BibT_eX]

[DOI]

Avinash Kori

Atalanti-Anastasia Mastakouri

Fabio De Sousa Ribeiro

Francesca Toni

Ben Glocker

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Adaptive Slot Attention: Object Discovery with Dynamic Slot Number.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Self-Compatibility: Evaluating Causal Discovery without Ground Truth.

[BibT_eX]

[DOI]

Philipp Michael Faller

Leena C. Vankadara

Dominik Janzing

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Image retrieval outperforms diffusion models on data augmentation.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Shortcuts for causal discovery of nonlinear models by score matching.

[BibT_eX]

[DOI]

CoRR, 2023

Unsupervised Conditional Slot Attention for Object Centric Learning.

[BibT_eX]

[DOI]

CoRR, 2023

A data augmentation perspective on diffusion models and retrieval.

[BibT_eX]

[DOI]

CoRR, 2023

Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots.

[BibT_eX]

[DOI]

CoRR, 2023

TeST: Test-time Self-Training under Distribution Shift.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Preface of UniReps: the First Workshop on Unifying Representations in Neural Models.

[BibT_eX]

[DOI]

Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models, 2023

Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling.

[BibT_eX]

[DOI]

Zhenyu Zhu

Atalanti-Anastasia Mastakouri

Volkan Cevher

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Assumption violations in causal discovery and the robustness of score matching.

[BibT_eX]

[DOI]

Francesco Montagna

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Latent Space Translation via Semantic Alignment.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Rotating Features for Object Discovery.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Leveraging sparse and shared feature activations for disentangled representation learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Benign Overfitting in Deep Neural Networks under Lazy Training.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations.

[BibT_eX]

[DOI]

Andrii Zadaianchuk

Yi Zhu

Thomas Brox

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Bridging the Gap to Real-World Object-Centric Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Relative representations enable zero-shot latent space communication.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Object-Centric Multiple Object Tracking.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Unsupervised Open-Vocabulary Object Localization in Videos.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Unsupervised Object Learning via Common Fate.

[BibT_eX]

[DOI]

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Scalable Causal Discovery with Score Matching.

[BibT_eX]

[DOI]

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.

[BibT_eX]

[DOI]

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

A General Purpose Neural Architecture for Geospatial Systems.

[BibT_eX]

[DOI]

CoRR, 2022

Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.

[BibT_eX]

[DOI]

CoRR, 2022

Self-supervised Amodal Video Object Segmentation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Assaying Out-Of-Distribution Generalization in Transfer Learning.

[BibT_eX]

[DOI]

Florian Wenzel

Andrea Dittadi

Peter V. Gehler

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Neural Attentive Circuits.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.

[BibT_eX]

[DOI]

Michael Lohaus

Krishnaram Kenthapadi

Chris Russell

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models.

[BibT_eX]

[DOI]

Paul Rolland

Volkan Cevher

Proceedings of the International Conference on Machine Learning, 2022

Generalization and Robustness Implications in Object-Centric Learning.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

The Role of Pretrained Representations for the OOD Generalization of RL Agents.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.

[BibT_eX]

[DOI]

Dominik Zietlow

Michael Lohaus

Guha Balakrishnan

Bernhard Schölkopf

Chris Russell

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.

[BibT_eX]

[DOI]

Gideon Dresdner

Maria-Luiza Vladarean

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Toward Causal Representation Learning.

[BibT_eX]

[DOI]

Proc. IEEE, 2021

Enforcing and Discovering Structure in Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2021

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2021

Towards Causal Representation Learning.

[BibT_eX]

[DOI]

CoRR, 2021

Backward-Compatible Prediction Updates: A Probabilistic Approach.

[BibT_eX]

[DOI]

Frederik Träuble

Julius von Kügelgen

Bernhard Schölkopf

Peter V. Gehler

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Dynamic Inference with Neural Interpreters.

[BibT_eX]

[DOI]

Nasim Rahaman

Muhammad Waleed Gondal

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Boosting Variational Inference With Locally Adaptive Step-Sizes.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Neighborhood Contrastive Learning Applied to Online Patient Monitoring.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

On Disentangled Representations Learned from Correlated Data.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

On the Transfer of Disentangled Representations in Realistic Settings.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Enforcing and Discovering Structure in Machine Learning.

[BibT_eX]

[DOI]

PhD thesis, 2020

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

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data.

[BibT_eX]

[DOI]

CoRR, 2020

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization.

[BibT_eX]

[DOI]

CoRR, 2020

Object-Centric Learning with Slot Attention.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Weakly-Supervised Disentanglement Without Compromises.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Disentangling Factors of Variations Using Few Labels.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

A Commentary on the Unsupervised Learning of Disentangled Representations.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Disentangling Factors of Variation Using Few Labels.

[BibT_eX]

[DOI]

CoRR, 2019

Stochastic Conditional Gradient Method for Composite Convex Minimization.

[BibT_eX]

[DOI]

CoRR, 2019

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

[BibT_eX]

[DOI]

Sjoerd van Steenkiste

Jürgen Schmidhuber

Olivier Bachem

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stochastic Frank-Wolfe for Composite Convex Minimization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Fairness of Disentangled Representations.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset.

[BibT_eX]

[DOI]

Muhammad Waleed Gondal

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

SOM-VAE: Interpretable Discrete Representation Learning on Time Series.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.

[BibT_eX]

[DOI]

CoRR, 2018

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series.

[BibT_eX]

[DOI]

CoRR, 2018

Revisiting First-Order Convex Optimization Over Linear Spaces.

[BibT_eX]

[DOI]

Anant Raj

Sai Praneeth Karimireddy

CoRR, 2018

Boosting Black Box Variational Inference.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

On Matching Pursuit and Coordinate Descent.

[BibT_eX]

[DOI]