I am a Senior Research Scientist at DeepMind. My research focuses Multi-agent Reinforcement Learning, Social Perception and Relational Reasoning.
I obtained my PhD from MIT where I was part of the Computer Science and Artificial Intelligence Laboratory. My thesis advisor was Tomaso Poggio.
Before Graduate School I was part of the Equipment Controls and Electronic section in the Engineering Department at CERN.
Contacts and Links
- 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 W. 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
- 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 PW. 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
- Invariant Recognition Shapes Neural Representations of Visual Input. Tacchetti A, Isik L, Poggio T. Annual Reviews of Vision Science
- 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.
- 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.
- Learning Invariant Representations of Actions and Faces. Tacchetti A. PhD Thesis, MIT, Department of Electrical Engineering and Computer Science, 2018.
- 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)