Publications

Pre-prints

• Scaffolding cooperation in human groups with deep reinforcement learning, McKee K, Tacchetti A, Bakker M, Balaguer J, Campbell-Gillingham L, Everett R, Botvinick M, 2022

• The Good Shepherd: An Oracle Agent for Mechanism Design. Balaguer J, Koster R, Summerfield C, Tacchetti A; 2022

• HCMD-zero: Learning Value Aligned Mechanisms from Data. Balaguer J, Koster R, Weinstein A, Campbell-Gillingham L, Summerfield C, Botvinick M, Tacchetti A; 2022

• Human-centered mechanism design with Democratic AI. Koster R, Balaguer J, Tacchetti A, Weinstein A, Zhu T, Hauser O, Williams D, Campbell-Gillingham L, Thacher P, Botvinick M, Summerfield C' 2022

• Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. Gemp I, Savani R, Lanctot M, Bachrach Y, Anthony T, Everett R,  Tacchetti A, Eccles T, Kramár J; 2021

• D3C: Reducing the Price of Anarchy in Multi-Agent Learning. Gemp I, McKee K, Everett R, Duéñez-Guzmán E, Bachrach Y, Balduzzi D, Tacchetti A; 2021

• Learning to Play No-Press Diplomacy with Best Response Policy Iteration. Anthony T*, Eccles T*, Tacchetti A, Kramár J, Gemp I, Hudson TC, Porcel N, Lanctot M, Pérolat J, Everett J, Singh S, Graepel T, Bachrach Y; 2020

• A Neural Architecture for Designing Truthful and Efficient Auctions. Tacchetti A, Strouse DJ, Garnelo M, Graepel T, Bachrach Y; 2019

• Relational Forward Models for Multi-Agent Learning. Tacchetti A*, Song H F*, Mediano P A M*, Zambaldi V, Rabinowitz N C, Graepel T, Botvinick M, Battaglia P; 2018

• Visual Interaction Networks. Watters N, Tacchetti A, Weber T, Pascanu R, Battaglia P, Zoran D; 2017

• Invariant recognition drives neural representations of action sequences. Tacchetti A*, Isik L* Poggio T; 2017

• Discriminiate-and-rectify Encoders: Learning from Image Transfromation Sets. Tacchetti A*, Voinea S*, Evangelopoulos G; 2017

• Fast, invariant representations for human actions in the visual system. Isik L*, Tacchetti A*, Poggio T; 2016

• Regularization by Early Stopping for Online Learning Algorithms. Rosasco L, Tacchetti A, Villa S; 2014 (updated version by Rosasco L, and Villa S. 2015)

• Does invariant recognition predict tuning of neurons in sensory cortex? Poggio T, Mutch J, Anselmi F, Tacchetti A, Rosasco R, Leibo J; 2013

• Unsupervised learning of invariant representations in hierarchical architectures. Anselmi F, Joel L, Rosasco L, Mutch J, Tacchetti A, Poggio T; 2013

Conference Papers

• Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers, Marris L, Gemp I, Anthony T, Tacchetti A, Liu S, Tuyls K,  Neural Information Processing Systems (NeurIPS) 2022

• Teamwork Reinforcement Learning with Concave Utilities. Yu Z, Zhang J, Wen Z, Tacchetti A, Wang M, Gemp I, ICLR 2022 Workshop on Gamification and Multiagent Solutions. 2022

• Learning to Play No-Press Diplomacy with Best Response Policy Iteration. Anthony T, Eccles T, Tacchetti A, Kramár J, Gemp I, Hudson T, Porcel N, Lanctot M, Perolat J, Everett R, Singh S, Graepel T, Bachrach Y; Neural Information Processing Systems (NeurIPS) 2020 (Spotlight talk)

• Should I tear down this wall? Optimizing social metrics by evaluating novel actions. Kramár J, Rabinowitz N, Eccles T, Tacchetti A; International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE) 2020.

• Relational Forward Models for Multi-agent learning. Tacchetti A, Song HF, Mediano, PAM, Zambaldi V, Kramár, Rabinowitz NC, Graepel T, Botvinick M, Battaglia P; International Conference on Learning Representations (ICLR) 2019.

• Trading robust representations for sample complexity through self-supervised visual experience. Tacchetti A, Voinea S, Evangelopoulos G; Neural Information Processing Systems (NeurIPS) 2018

• Visual Interaction Networks: Learning a Physics Simulator from Video. Watters N, Tacchetti A, Weber T, Pascanu R, Battaglia P, Zoran D; Neural Information Processing Systems (NeurIPS) 2017

• Invariant recognition drives neural representations of action sequences. Isik L*, Tacchetti A*, Poggio T; Cognitive Computational Neuroscience; 2017

• Representation Learning from Orbit Sets for One-Shot Classification. Tacchetti A*, Voinea S*, Evangelopoulos G, Poggio T; 2017 AAAI Spring Symposium Series; 2017

• Invariant representations for action recognition in the visual system. Tacchetti A*, Isik L*, Poggio, T. Journal of Vision (Oral Presentation at VSS); 2015

• Invariant representations for action recognition in the visual system. Isik L*, Tacchetti A*, Poggio, T. Computational and Systems Neuroscience (COSYNE); 2015

• Invariant representations of action recognition. Isik L*, Tacchetti A*, Poggio T; Society for Neuroscience 2014.

• Readout of dynamic action sequences using MEG decoding. Isik L, Tacchetti A, Poggio T; Biomagnetism, 2014.

• Implementation and tuning of the Extended Kalman Filter for a sensorless drive working with arbitrary stepper motors and cable lengths. Butcher M, Masi A, Martino M, Tacchetti A; International Conference on Electrical Machines (ICEM). 2012.

• GURLS: a Toolbox for Large Scale Multiclass Learning. Tacchetti A, Mallapragada P, Santoro M, Rosasco L. NeurIPS Workshop on Parallel and Large-Scale Machine Learning; 2011

Journal Papers

• Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy, Kramár J, Eccles T,  Gemp I, Tacchetti A, McKee K, Malinowski M, Graepel T, Bachrach Y, Nature Communications 2022

• Designing all-pay auctions using deep learning and multi-agent simulation, Gemp I, Anthony T, Kramar J, Eccles T, Tacchetti A, Bachrach Y, Nature Scientific Reports 2022

• Human-centred mechanism design with Democratic AI, Koster R, Balaguer J, Tacchetti A, Weinstein A, Zhu T, Hauser O, Williams D, Campbell-Gillingham L, Thacker P, Botvinick M, Summerfield C. Nature Human Behavior 2022

• Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning. Fu J, Tacchetti A, Perolat J, Bachrach Y; JAIR, 2021

• Invariant Recognition Shapes Neural Representations of Visual Input. Tacchetti A, Isik L, Poggio T. Annual Reviews of Vision Science; 2018

• Invariant recognition drives neural representations of action sequences. Tacchetti A*, Isik L*, Poggio T. PLOS Computational Biology; 2017

• A fast, invariant representation for human action in the visual system. Isik L*, Tacchetti A*, Poggio T; Journal of Neurophysiology, 2017 (selected for APSselect, a collection from the APS that showcases some of the best recently published articles in physiological research).

• Unsupervised learning or invariant representations. Anselmi F, Leibo J, Rosasco L, Mutch J, Tacchetti A, Poggio T; Theoretical Computer Science, 2015.

• GURLS: A Least Squares Library for Supervised Learining. Tacchetti A, Mallapragada P, Santoro M, Rosasco L; Journal of Machine Learning Research 14, 3201-3205, 2013.

Book Chapters

• Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex. Mutch J, Anselmi F, Tacchetti A, Rosasco L, Leibo J, Poggio T; Computational and Cognitive Neuroscience of Vision, 2016.

Thesis

• Learning Invariant Representations of Actions and Faces. Tacchetti A. PhD Thesis, MIT, Department of Electrical Engineering and Computer Science, 2018.

Popular Press

• Google’s ‘Democratic AI’ Is Better at Redistributing Wealth Than America. Janus Rose, VICE 2022

• DeepMind sets AI loose on Diplomacy board game, and collaboration is key.  R. Dallon Adams. TechRepublic. 2020

• DeepMind hopes to teach AI to cooperate by playing Diplomacy. Kyle Wiggers. Venture Beat. 2020

• Want to understand the mind of another? Get relational!. Jack Clark. Import AI. 2018

• Forget AlphaGo — DeepMind Has a More Interesting Step Toward General AI. Will Knight, MIT Technology Review. 2017

• Machines that learn like people. Larry Hardesty, MIT News. 2015 (picked up by PHYS.ORG)