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
- 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
- Relational Forward Models for Multi-agent learning. Tacchetti A, Song HF, Mediano, PAM, Zambaldi V, Krámar, 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.
- 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)