Timezone: »
Deploying AI-powered systems requires trustworthy models supporting effective human interactions, going beyond raw prediction accuracy. Concept bottleneck models promote trustworthiness by conditioning classification tasks on an intermediate level of human-like concepts. This enables human interventions which can correct mispredicted concepts to improve the model's performance. However, existing concept bottleneck models are unable to find optimal compromises between high task accuracy, robust concept-based explanations, and effective interventions on concepts---particularly in real-world conditions where complete and accurate concept supervisions are scarce. To address this, we propose Concept Embedding Models, a novel family of concept bottleneck models which goes beyond the current accuracy-vs-interpretability trade-off by learning interpretable high-dimensional concept representations. Our experiments demonstrate that Concept Embedding Models (1) attain better or competitive task accuracy w.r.t. standard neural models without concepts, (2) provide concept representations capturing meaningful semantics including and beyond their ground truth labels, (3) support test-time concept interventions whose effect in test accuracy surpasses that in standard concept bottleneck models, and (4) scale to real-world conditions where complete concept supervisions are scarce.
Author Information
Mateo Espinosa Zarlenga (University of Cambridge)
Pietro Barbiero (University of Cambridge)
Gabriele Ciravegna (LABORATOIRE I3S UCA)

I am a Post Doc in the MAASAI (Models and Algorithms for Artificial Intelligence) research team of Inria. I received the Ph.D. degree with honours from the University of Florence in 2022 under the supervision of Professor Marco Gori. In 2018, I received a master’s degree in Computer Engineering with honours at the Polytechnic of Turin. Besides machine learning, I also like football, volleyball, and playing the piano.
Giuseppe Marra (KU Leuven)
Francesco Giannini (CINI - University of Siena)
Michelangelo Diligenti (Department of Information Engineering and Mathematical Sciences)
Zohreh Shams (Babylon Health, University of Cambridge)
Frederic Precioso (Universite Cote d'Azur)
Stefano Melacci (University of Siena)
Adrian Weller (Cambridge, Alan Turing Institute)
Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading work on safe and ethical AI. He is a Principal Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.
Pietro Lió (University of Cambridge)
Mateja Jamnik (University of Cambridge)
More from the Same Authors
-
2020 : Learning to Identify Drilling Defects in TurbineBlades with Single Stage Detectors »
Andrea Panizza · Szymon Tomasz Stefanek · Stefano Melacci · Giacomo Veneri · Marco Gori -
2021 : Efficient Decompositional Rule Extraction for Deep Neural Networks »
Mateo Espinosa Zarlenga · Mateja Jamnik -
2021 : Interpretable Data Analysis for Bench-to-Bedside Research »
Zohreh Shams · Botty Dimanov · Nikola Simidjievski · Helena Andres-Terre · Paul Scherer · Urška Matjašec · Mateja Jamnik · Pietro Lió -
2021 : Structure-aware generation of drug-like molecules »
Pavol Drotar · Arian Jamasb · Ben Day · Catalina Cangea · Pietro Lió -
2021 : 3D Pre-training improves GNNs for Molecular Property Prediction »
Hannes Stärk · Dominique Beaini · Gabriele Corso · Prudencio Tossou · Christian Dallago · Stephan Günnemann · Pietro Lió -
2021 : 3D Pre-training improves GNNs for Molecular Property Prediction »
Hannes Stärk · Gabriele Corso · Christian Dallago · Stephan Günnemann · Pietro Lió -
2021 : Approximate Latent Force Model Inference »
Jacob Moss · Felix Opolka · Pietro Lió -
2022 Poster: Scalable Infomin Learning »
Yanzhi Chen · weihao sun · Yingzhen Li · Adrian Weller -
2022 : Learning Feynman Diagrams using Graph Neural Networks »
Alexander Norcliffe · Harrison Mitchell · Pietro Lió -
2022 : A physics-informed search for metric solutions to Ricci flow, their embeddings, and visualisation »
Aarjav Jain · Challenger Mishra · Pietro Lió -
2022 : Improving Classification and Data Imputation for Single-Cell Transcriptomics with Graph Neural Networks »
Han-Bo Li · Ramon Viñas Torné · Pietro Lió -
2022 : Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs »
Albert Jiang · Sean Welleck · Jin Peng Zhou · Timothee Lacroix · Jiacheng Liu · Wenda Li · Mateja Jamnik · Guillaume Lample · Yuhuai Wu -
2022 : Structure-based Drug Design with Equivariant Diffusion Models »
Arne Schneuing · Yuanqi Du · Charles Harris · Arian Jamasb · Ilia Igashov · weitao Du · Tom Blundell · Pietro Lió · Carla Gomes · Max Welling · Michael Bronstein · Bruno Correia -
2022 : A Federated Learning benchmark for Drug-Target Interaction »
Filip Svoboda · Gianluca Mittone · Nicholas Lane · Pietro Lió -
2022 : Benchmarking Graph Neural Network-based Imputation Methods on Single-Cell Transcriptomics Data »
Han-Bo Li · Ramon Viñas Torné · Pietro Lió -
2022 : Sheaf Attention Networks »
Federico Barbero · Cristian Bodnar · Haitz Sáez de Ocáriz Borde · Pietro Lió -
2022 : Human Interventions in Concept Graph Networks »
Lucie Charlotte Magister · Pietro Barbiero · Dmitry Kazhdan · Federico Siciliano · Gabriele Ciravegna · Fabrizio Silvestri · Mateja Jamnik · Pietro Lió -
2022 : Knowledge-driven Active Learning »
Gabriele Ciravegna · Frederic Precioso · Marco Gori -
2022 : Conformal Prediction for Resource Prioritisation in Predicting Rare and Dangerous Outcomes »
Varun Babbar · Umang Bhatt · Miri Zilka · Adrian Weller -
2023 Poster: Quasi-Monte Carlo Graph Random Features »
Isaac Reid · Adrian Weller · Krzysztof M Choromanski -
2023 Poster: Graph Denoising Diffusion for Inverse Protein Folding »
Kai Yi · Bingxin Zhou · Yiqing Shen · Pietro Lió · Yuguang Wang -
2023 Poster: Use perturbations when learning from explanations »
Juyeon Heo · Vihari Piratla · Matthew Wicker · Adrian Weller -
2023 Poster: Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel »
Valerii Likhosherstov · Krzysztof M Choromanski · Kumar Avinava Dubey · Frederick Liu · Tamas Sarlos · Adrian Weller -
2023 Poster: Interpretable Graph Networks Formulate Universal Algebra Conjectures »
Francesco Giannini · Stefano Fioravanti · Oguzhan Keskin · Alisia Lupidi · Lucie Charlotte Magister · Pietro Lió · Pietro Barbiero -
2023 Poster: Sheaf Hypergraph Networks »
Iulia Duta · Giulia Cassarà · Fabrizio Silvestri · Pietro Lió -
2023 Poster: Diffused Redundancy in Pre-trained Representations »
Vedant Nanda · Till Speicher · John Dickerson · Krishna Gummadi · Soheil Feizi · Adrian Weller -
2023 Poster: Controlling Text-to-Image Diffusion by Orthogonal Finetuning »
Zeju Qiu · Weiyang Liu · Haiwen Feng · Yuxuan Xue · Yao Feng · Zhen Liu · Dan Zhang · Adrian Weller · Bernhard Schölkopf -
2023 Poster: Certification of Distributional Individual Fairness »
Matthew Wicker · Vihari Piratla · Adrian Weller -
2023 Poster: Learning to Receive Help: Intervention-Aware Concept Embedding Models »
Mateo Espinosa Zarlenga · Katie Collins · Krishnamurthy Dvijotham · Adrian Weller · Zohreh Shams · Mateja Jamnik -
2022 : Sheaf Attention Networks »
Federico Barbero · Cristian Bodnar · Haitz Sáez de Ocáriz Borde · Pietro Lió -
2022 : Dynamic outcomes-based clustering of disease progression in mechanically ventilated patients »
Emma Rocheteau · Ioana Bica · Pietro Lió · Ari Ercole -
2022 Poster: Autoformalization with Large Language Models »
Yuhuai Wu · Albert Q. Jiang · Wenda Li · Markus Rabe · Charles Staats · Mateja Jamnik · Christian Szegedy -
2022 Poster: Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs »
Cristian Bodnar · Francesco Di Giovanni · Benjamin Chamberlain · Pietro Lió · Michael Bronstein -
2022 Poster: Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers »
Albert Q. Jiang · Wenda Li · Szymon Tworkowski · Konrad Czechowski · Tomasz Odrzygóźdź · Piotr Miłoś · Yuhuai Wu · Mateja Jamnik -
2022 Poster: Chefs' Random Tables: Non-Trigonometric Random Features »
Valerii Likhosherstov · Krzysztof M Choromanski · Kumar Avinava Dubey · Frederick Liu · Tamas Sarlos · Adrian Weller -
2022 Poster: A Survey and Datasheet Repository of Publicly Available US Criminal Justice Datasets »
Miri Zilka · Bradley Butcher · Adrian Weller -
2022 Poster: VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming »
Eleonora Misino · Giuseppe Marra · Emanuele Sansone -
2022 Poster: Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks »
Arian Jamasb · Ramon Viñas Torné · Eric Ma · Yuanqi Du · Charles Harris · Kexin Huang · Dominic Hall · Pietro Lió · Tom Blundell -
2022 Poster: Composite Feature Selection Using Deep Ensembles »
Fergus Imrie · Alexander Norcliffe · Pietro Lió · Mihaela van der Schaar -
2022 Poster: Generalised Mutual Information for Discriminative Clustering »
Louis Ohl · Pierre-Alexandre Mattei · Charles Bouveyron · Warith HARCHAOUI · Mickaël Leclercq · Arnaud Droit · Frederic Precioso -
2022 Poster: SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks »
Davide Buffelli · Pietro Lió · Fabio Vandin -
2021 : [S12] Efficient Decompositional Rule Extraction for Deep Neural Networks »
Mateo Espinosa Zarlenga · Mateja Jamnik -
2021 : Neural ODE Processes: A Short Summary »
Alexander Norcliffe · Cristian Bodnar · Ben Day · Jacob Moss · Pietro Lió -
2021 : On Second Order Behaviour in Augmented Neural ODEs: A Short Summary »
Alexander Norcliffe · Cristian Bodnar · Ben Day · Nikola Simidjievski · Pietro Lió -
2021 Workshop: Privacy in Machine Learning (PriML) 2021 »
Yu-Xiang Wang · Borja Balle · Giovanni Cherubin · Kamalika Chaudhuri · Antti Honkela · Jonathan Lebensold · Casey Meehan · Mi Jung Park · Adrian Weller · Yuqing Zhu -
2021 : Structure-aware generation of drug-like molecules »
Pavol Drotar · Arian Jamasb · Ben Day · Catalina Cangea · Pietro Lió -
2021 Workshop: Human Centered AI »
Michael Muller · Plamen P Angelov · Shion Guha · Marina Kogan · Gina Neff · Nuria Oliver · Manuel Rodriguez · Adrian Weller -
2021 Workshop: AI for Science: Mind the Gaps »
Payal Chandak · Yuanqi Du · Tianfan Fu · Wenhao Gao · Kexin Huang · Shengchao Liu · Ziming Liu · Gabriel Spadon · Max Tegmark · Hanchen Wang · Adrian Weller · Max Welling · Marinka Zitnik -
2020 Workshop: Privacy Preserving Machine Learning - PriML and PPML Joint Edition »
Borja Balle · James Bell · Aurélien Bellet · Kamalika Chaudhuri · Adria Gascon · Antti Honkela · Antti Koskela · Casey Meehan · Olga Ohrimenko · Mi Jung Park · Mariana Raykova · Mary Anne Smart · Yu-Xiang Wang · Adrian Weller -
2020 Poster: Constraining Variational Inference with Geometric Jensen-Shannon Divergence »
Jacob Deasy · Nikola Simidjievski · Pietro Lió -
2020 Poster: On Second Order Behaviour in Augmented Neural ODEs »
Alexander Norcliffe · Cristian Bodnar · Ben Day · Nikola Simidjievski · Pietro Lió -
2020 Poster: Ode to an ODE »
Krzysztof Choromanski · Jared Quincy Davis · Valerii Likhosherstov · Xingyou Song · Jean-Jacques Slotine · Jacob Varley · Honglak Lee · Adrian Weller · Vikas Sindhwani -
2020 Poster: Focus of Attention Improves Information Transfer in Visual Features »
Matteo Tiezzi · Stefano Melacci · Alessandro Betti · Marco Maggini · Marco Gori -
2019 Workshop: Privacy in Machine Learning (PriML) »
Borja Balle · Kamalika Chaudhuri · Antti Honkela · Antti Koskela · Casey Meehan · Mi Jung Park · Mary Anne Smart · Mary Anne Smart · Adrian Weller -
2019 : Poster Session »
Jonathan Scarlett · Piotr Indyk · Ali Vakilian · Adrian Weller · Partha P Mitra · Benjamin Aubin · Bruno Loureiro · Florent Krzakala · Lenka Zdeborová · Kristina Monakhova · Joshua Yurtsever · Laura Waller · Hendrik Sommerhoff · Michael Moeller · Rushil Anirudh · Shuang Qiu · Xiaohan Wei · Zhuoran Yang · Jayaraman Thiagarajan · Salman Asif · Michael Gillhofer · Johannes Brandstetter · Sepp Hochreiter · Felix Petersen · Dhruv Patel · Assad Oberai · Akshay Kamath · Sushrut Karmalkar · Eric Price · Ali Ahmed · Zahra Kadkhodaie · Sreyas Mohan · Eero Simoncelli · Carlos Fernandez-Granda · Oscar Leong · Wesam Sakla · Rebecca Willett · Stephan Hoyer · Jascha Sohl-Dickstein · Sam Greydanus · Gauri Jagatap · Chinmay Hegde · Michael Kellman · Jonathan Tamir · Nouamane Laanait · Ousmane Dia · Mirco Ravanelli · Jonathan Binas · Negar Rostamzadeh · Shirin Jalali · Tiantian Fang · Alex Schwing · Sébastien Lachapelle · Philippe Brouillard · Tristan Deleu · Simon Lacoste-Julien · Stella Yu · Arya Mazumdar · Ankit Singh Rawat · Yue Zhao · Jianshu Chen · Xiaoyang Li · Hubert Ramsauer · Gabrio Rizzuti · Nikolaos Mitsakos · Dingzhou Cao · Thomas Strohmer · Yang Li · Pei Peng · Gregory Ongie -
2019 Workshop: Workshop on Human-Centric Machine Learning »
Plamen P Angelov · Nuria Oliver · Adrian Weller · Manuel Rodriguez · Isabel Valera · Silvia Chiappa · Hoda Heidari · Niki Kilbertus -
2019 Poster: Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models »
Yunfei Teng · Wenbo Gao · François Chalus · Anna Choromanska · Donald Goldfarb · Adrian Weller -
2018 Workshop: Privacy Preserving Machine Learning »
Adria Gascon · Aurélien Bellet · Niki Kilbertus · Olga Ohrimenko · Mariana Raykova · Adrian Weller -
2018 Poster: Geometrically Coupled Monte Carlo Sampling »
Mark Rowland · Krzysztof Choromanski · François Chalus · Aldo Pacchiano · Tamas Sarlos · Richard Turner · Adrian Weller -
2018 Spotlight: Geometrically Coupled Monte Carlo Sampling »
Mark Rowland · Krzysztof Choromanski · François Chalus · Aldo Pacchiano · Tamas Sarlos · Richard Turner · Adrian Weller -
2017 : Invited talk: Challenges for Transparency »
Adrian Weller -
2017 : Closing remarks »
Adrian Weller -
2017 Symposium: Kinds of intelligence: types, tests and meeting the needs of society »
José Hernández-Orallo · Zoubin Ghahramani · Tomaso Poggio · Adrian Weller · Matthew Crosby -
2017 Poster: From Parity to Preference-based Notions of Fairness in Classification »
Muhammad Bilal Zafar · Isabel Valera · Manuel Rodriguez · Krishna Gummadi · Adrian Weller -
2017 Poster: The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings »
Krzysztof Choromanski · Mark Rowland · Adrian Weller -
2017 Poster: Uprooting and Rerooting Higher-Order Graphical Models »
Mark Rowland · Adrian Weller -
2016 Workshop: Reliable Machine Learning in the Wild »
Dylan Hadfield-Menell · Adrian Weller · David Duvenaud · Jacob Steinhardt · Percy Liang -
2016 Symposium: Machine Learning and the Law »
Adrian Weller · Thomas D. Grant · Conrad McDonnell · Jatinder Singh -
2015 Symposium: Algorithms Among Us: the Societal Impacts of Machine Learning »
Michael A Osborne · Adrian Weller · Murray Shanahan -
2014 Poster: Clamping Variables and Approximate Inference »
Adrian Weller · Tony Jebara -
2014 Oral: Clamping Variables and Approximate Inference »
Adrian Weller · Tony Jebara