Workshop
Causal Inference & Machine Learning: Why now?
Elias Bareinboim · Bernhard Schölkopf · Terrence Sejnowski · Yoshua Bengio · Judea Pearl
Mon 13 Dec, 7 a.m. PST
Machine Learning has been extremely successful throughout many critical areas, including computer vision, natural language processing, and game-playing. Still, a growing segment of the machine learning community recognizes that there are still fundamental pieces missing from the AI puzzle, among them causal inference.
This recognition comes from the observation that even though causality is a central component found throughout the sciences, engineering, and many other aspects of human cognition, explicit reference to causal relationships is largely missing in current learning systems. This entails a new goal of integrating causal inference and machine learning capabilities into the next generation of intelligent systems, thus paving the way towards higher levels of intelligence and human-centric AI. The synergy goes in both directions; causal inference benefitting from machine learning and the other way around. Current machine learning systems lack the ability to leverage the invariances imprinted by the underlying causal mechanisms towards reasoning about generalizability, explainability, interpretability, and robustness. Current causal inference methods, on the other hand, lack the ability to scale up to high-dimensional settings, where current machine learning systems excel.
The goal of this workshop is to bring together researchers from both camps to initiate principled discussions about the integration of causal reasoning and machine learning perspectives to help tackle the challenging AI tasks of the coming decades. We welcome researchers from all relevant disciplines, including but not limited to computer science, cognitive science, robotics, mathematics, statistics, physics, and philosophy.
Schedule
Mon 7:00 a.m. - 7:10 a.m.
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Intro
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Intro
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SlidesLive Video |
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Mon 7:10 a.m. - 7:30 a.m.
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Uri Shalit - Calibration, out-of-distribution generalization and a path towards causal representations
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Invited Talk
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SlidesLive Video |
Uri Shalit 🔗 |
Mon 7:30 a.m. - 7:50 a.m.
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Julius von Kügelgen - Independent mechanism analysis, a new concept?
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Invited Talk
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SlidesLive Video |
Julius von Kügelgen 🔗 |
Mon 7:50 a.m. - 8:10 a.m.
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David Blei - On the Assumptions of Synthetic Control Methods
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Invited Talk
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SlidesLive Video |
David Blei 🔗 |
Mon 8:10 a.m. - 8:25 a.m.
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Session 1: Q&A
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Q&A
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SlidesLive Video |
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Mon 8:30 a.m. - 8:50 a.m.
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Ricardo Silva - The Road to Causal Programming
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Invited Talk
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SlidesLive Video |
Ricardo Silva 🔗 |
Mon 8:50 a.m. - 9:10 a.m.
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Aapo Hyvarinen - Causal discovery by generative modelling
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Invited Talk
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SlidesLive Video |
Aapo Hyvarinen 🔗 |
Mon 9:10 a.m. - 9:35 a.m.
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Tobias Gerstenberg - Going beyond the here and now: Counterfactual simulation in human cognition
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Invited Talk
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SlidesLive Video |
Tobias Gerstenberg 🔗 |
Mon 9:35 a.m. - 9:45 a.m.
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Session 2: Q&A
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Q&A
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SlidesLive Video |
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Mon 9:45 a.m. - 10:45 a.m.
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Poster Session ( Poster Session ) > link | 🔗 |
Mon 10:45 a.m. - 11:05 a.m.
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Thomas Icard - A (topo)logical perspective on causal inference
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Invited Talk
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SlidesLive Video |
Thomas Icard 🔗 |
Mon 11:05 a.m. - 11:25 a.m.
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Caroline Uhler: TBA
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Invited Talk
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SlidesLive Video |
Caroline Uhler 🔗 |
Mon 11:25 a.m. - 11:45 a.m.
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Rosemary Ke - From "What" to "Why": towards causal learning
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Invited Talk
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SlidesLive Video |
Nan Rosemary Ke 🔗 |
Mon 11:45 a.m. - 12:00 p.m.
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Session 3: Q&A
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Q&A
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SlidesLive Video |
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Mon 12:00 p.m. - 12:45 p.m.
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Judea Pearl - The logic of Causal Inference
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Keynote Speaker
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SlidesLive Video |
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Mon 12:45 p.m. - 1:00 p.m.
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Discussion Panel
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Discussion Panel
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Mon 1:00 p.m. - 1:15 p.m.
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Zaffalon, Antonucci, Cabañas - Causal Expectation-Maximisation
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Contributed Talk
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SlidesLive Video |
Marco Zaffalon · Alessandro Antonucci · Rafael Cabañas 🔗 |
Mon 1:15 p.m. - 1:30 p.m.
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Dominguez Olmedo, Karimi, Schölkopf - On the Adversarial Robustness of Causal Algorithmic Recourse
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Contributed Talk
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SlidesLive Video |
Ricardo Dominguez-Olmedo · Amir Karimi · Bernhard Schölkopf 🔗 |
Mon 1:30 p.m. - 1:45 p.m.
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Javidian, Pandey, Jamshidi - Scalable Causal Domain Adaptation
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Contributed Talk
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SlidesLive Video |
Mohammad Ali Javidian · Om Pandey · Pooyan Jamshidi 🔗 |
Mon 1:45 p.m. - 2:00 p.m.
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Cundy, Grover, Ermon - BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
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Contributed Talk
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SlidesLive Video |
Chris Cundy · Aditya Grover · Stefano Ermon 🔗 |
Mon 2:00 p.m. - 2:20 p.m.
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Alison Gopnik - Casual Learning in Children and Computational Models
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Invited Talk
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SlidesLive Video |
Alison Gopnik 🔗 |
Mon 2:20 p.m. - 2:40 p.m.
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Adèle Ribeiro - Effect Identification in Cluster Causal Diagrams
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Invited Talk
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SlidesLive Video |
Adèle Ribeiro 🔗 |
Mon 2:40 p.m. - 3:00 p.m.
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Victor Chernozhukov - Omitted Confounder Bias Bounds for Machine Learned Causal Models
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Invited Talk
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SlidesLive Video |
Victor Chernozhukov 🔗 |
Mon 3:00 p.m. - 3:15 p.m.
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Session 4: Q&A
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Q&A
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SlidesLive Video |
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Mon 3:15 p.m. - 3:30 p.m.
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Closing Remarks
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Closing Remarks
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Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation
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Poster
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Thien Tran · Kazuto Fukuchi · Youhei Akimoto · Jun Sakuma 🔗 |
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Encoding Causal Macrovariables
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Poster
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Benedikt Höltgen 🔗 |
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Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
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Poster
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Sindy Löwe · David Madras · Richard Zemel · Max Welling 🔗 |
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Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
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Poster
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Olivier Jeunen · Ciaran Gilligan-Lee · Rishabh Mehrotra · Mounia Lalmas 🔗 |
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Typing assumptions improve identification in causal discovery
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Poster
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Philippe Brouillard · Perouz Taslakian · Alexandre Lacoste · Sébastien Lachapelle · Alexandre Drouin 🔗 |
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Prequential MDL for Causal Structure Learning with Neural Networks
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Poster
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Jorg Bornschein · Silvia Chiappa · Alan Malek · Nan Rosemary Ke 🔗 |
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MANM-CS: Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data
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Poster
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Johannes Huegle · Christopher Hagedorn · Jonas Umland · Rainer Schlosser 🔗 |
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DiBS: Differentiable Bayesian Structure Learning
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Poster
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Lars Lorch · Jonas Rothfuss · Bernhard Schölkopf · Andreas Krause 🔗 |
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Learning Neural Causal Models with Active Interventions
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Poster
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Nino Scherrer · Olexa Bilaniuk · Yashas Annadani · Anirudh Goyal · Patrick Schwab · Bernhard Schölkopf · Michael Mozer · Yoshua Bengio · Stefan Bauer · Nan Rosemary Ke 🔗 |
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Identification of Latent Graphs: A Quantum Entropic Approach
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Poster
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Mohammad Ali Javidian · Vaneet Aggarwal · Zubin Jacob 🔗 |
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Reliable causal discovery based on mutual information supremum principle for finite datasets
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Poster
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Vincent Cabeli · Honghao Li · Marcel da Câmara Ribeiro Dantas · Herve Isambert 🔗 |
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Scalable Causal Domain Adaptation
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Poster
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Mohammad Ali Javidian · Om Pandey · Pooyan Jamshidi 🔗 |
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Learning preventative and generative causal structures from point events in continuous time
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Poster
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Tianwei Gong 🔗 |
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Building Object-based Causal Programs for Human-like Generalization
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Poster
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Bonan Zhao · Chris Lucas 🔗 |
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On the Robustness of Causal Algorithmic Recourse
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Poster
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Ricardo Dominguez-Olmedo · Amir Karimi · Bernhard Schölkopf 🔗 |
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Desiderata for Representation Learning: A Causal Perspective
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Poster
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Yixin Wang · Michael Jordan 🔗 |
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Scalable Variational Approaches for Bayesian Causal Discovery
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Poster
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Chris Cundy · Aditya Grover · Stefano Ermon 🔗 |
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Individual treatment effect estimation in the presence of unobserved confounding based on a fixed relative treatment effect
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Poster
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Wouter van Amsterdam · Rajesh Ranganath 🔗 |
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A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
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Poster
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Xiaoqing Tan · Lu Tang 🔗 |
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Multiple Environments Can Reduce Indeterminacy in VAEs
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Poster
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Quanhan (Johnny) Xi · Benjamin Bloem-Reddy 🔗 |
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Using Embeddings to Estimate Peer Influence on Social Networks
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Poster
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Irina Cristali · Victor Veitch 🔗 |
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Using Non-Linear Causal Models to StudyAerosol-Cloud Interactions in the Southeast Pacific
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Poster
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Andrew Jesson · Peter Manshausen · Alyson Douglas · Duncan Watson-Parris · Yarin Gal · Philip Stier 🔗 |
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Synthesis of Reactive Programs with Structured Latent State
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Poster
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Ria Das · Zenna Tavares · Armando Solar-Lezama · Josh Tenenbaum 🔗 |
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Causal Inference Using Tractable Circuits
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Poster
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Adnan Darwiche 🔗 |
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Causal Expectation-Maximisation
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Poster
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Marco Zaffalon · Alessandro Antonucci · Rafael Cabañas 🔗 |