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Francesco Locatello

71 papers · 2017–2025 · 11 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (8) πŸ—ΊοΈ Taxonomy Completionist (14) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (8) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (11) 🏠 Conference Loyalist (25) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (21) 🀝 Dynamic Duo (25) πŸ† Keyword Champion (2) πŸ“ˆ Trend Setter ❓ The Questioner (2) ⚑ Prolific Year (12) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (51) πŸ’Ž Century Club (71) πŸ”₯ Unstoppable (9)

Conferences

NIPS (25) ICLR (15) ICML (14) CLEAR (5) AISTATS (4) CVPR (2) ICCV (2) IJCAI (1) JMLR (1) UAI (1) WACV (1)

Top co-authors

Bernhard SchΓΆlkopf (25) Olivier Bachem (8) Stefan Bauer (8) Thomas Brox (7) Julius von KΓΌgelgen (7) Gunnar RΓ€tsch (6) Volkan Cevher (6) Marco Fumero (6) Chris Russell (6) Max Horn (6)

Research topics

Science (1)

Keywords

representation learning (12) unsupervised learning (10) disentangled representation (9) causal discovery (7) slot attention (6) score matching (5) causal inference (5) object-centric representation (4) self-supervised learning (4) convex optimization (4) distribution shift (3) causal representation learning (3) attention mechanism (3) matching pursuit (3) inductive bia (3) domain adaptation (3) conditional gradient (3) transfer learning (3) bayesian inference (3) object-centric learning (3)

Papers

Mechanistic PDE Networks for Discovery of Governing Equations ICML 2025
Scalable Mechanistic Neural Networks ICLR 2025
How to Probe: Simple Yet Effective Techniques for Improving Post-hoc Explanations ICLR 2025
Near, far: Patch-ordering enhances vision foundation models' scene understanding ICLR 2025
Score matching through the roof: linear, nonlinear, and latent variables causal discovery CLEAR 2025
Unifying Causal Representation Learning with the Invariance Principle ICLR 2025
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention NIPS 2024
Multi-View Causal Representation Learning with Partial Observability ICLR 2024
Grounded Object-Centric Learning ICLR 2024
Unsupervised Concept Discovery Mitigates Spurious Correlations ICML 2024
Self-Compatibility: Evaluating Causal Discovery without Ground Truth AISTATS 2024
Mechanistic Neural Networks for Scientific Machine Learning ICML 2024
Adaptive Slot Attention: Object Discovery with Dynamic Slot Number CVPR 2024
A Sparsity Principle for Partially Observable Causal Representation Learning ICML 2024
Smoke and Mirrors in Causal Downstream Tasks NIPS 2024
Identifying General Mechanism Shifts in Linear Causal Representations NIPS 2024
Latent Functional Maps: a spectral framework for representation alignment NIPS 2024
Marrying Causal Representation Learning with Dynamical Systems for Science NIPS 2024
Benign Overfitting in Deep Neural Networks under Lazy Training ICML 2023
Object-Centric Multiple Object Tracking ICCV 2023
Unsupervised Open-Vocabulary Object Localization in Videos ICCV 2023
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations ICLR 2023
Relative representations enable zero-shot latent space communication ICLR 2023
Bridging the Gap to Real-World Object-Centric Learning ICLR 2023
TeST: Test-Time Self-Training Under Distribution Shift WACV 2023
Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling NIPS 2023
ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training NIPS 2023
Leveraging sparse and shared feature activations for disentangled representation learning NIPS 2023
Assumption violations in causal discovery and the robustness of score matching NIPS 2023
Latent Space Translation via Semantic Alignment NIPS 2023
Rotating Features for Object Discovery NIPS 2023
Unsupervised Object Learning via Common Fate CLEAR 2023
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning CLEAR 2023
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise CLEAR 2023
Scalable Causal Discovery with Score Matching CLEAR 2023
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction ICLR 2022
Self-supervised Amodal Video Object Segmentation NIPS 2022
Assaying Out-Of-Distribution Generalization in Transfer Learning NIPS 2022
Neural Attentive Circuits NIPS 2022
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks NIPS 2022
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization AISTATS 2022
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers CVPR 2022
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain ICLR 2022
The Role of Pretrained Representations for the OOD Generalization of RL Agents ICLR 2022
Generalization and Robustness Implications in Object-Centric Learning ICML 2022
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models ICML 2022
On the Transfer of Disentangled Representations in Realistic Settings ICLR 2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style NIPS 2021
Dynamic Inference with Neural Interpreters NIPS 2021
Backward-Compatible Prediction Updates: A Probabilistic Approach NIPS 2021
On Disentangled Representations Learned from Correlated Data ICML 2021
Neighborhood Contrastive Learning Applied to Online Patient Monitoring ICML 2021
Boosting Variational Inference With Locally Adaptive Step-Sizes IJCAI 2021
Disentangling Factors of Variations Using Few Labels ICLR 2020
Weakly-Supervised Disentanglement Without Compromises ICML 2020
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization ICML 2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation JMLR 2020
Object-Centric Learning with Slot Attention NIPS 2020
Are Disentangled Representations Helpful for Abstract Visual Reasoning? NIPS 2019
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA UAI 2019
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset NIPS 2019
Stochastic Frank-Wolfe for Composite Convex Minimization NIPS 2019
SOM-VAE: Interpretable Discrete Representation Learning on Time Series ICLR 2019
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations ICML 2019
On the Fairness of Disentangled Representations NIPS 2019
Boosting Black Box Variational Inference NIPS 2018
On Matching Pursuit and Coordinate Descent ICML 2018
Boosting Variational Inference: an Optimization Perspective AISTATS 2018
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming ICML 2018
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees NIPS 2017
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe AISTATS 2017