Andrea Tacchetti

ORCID

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 29

Top Venues

Massachusetts Institute of Technology, Cambridge, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, USA

PHD

Publications

Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Scaling Opponent Shaping to High Dimensional Games. CoRR (2023)
Denizalp Goktas, David C. Parkes, Ian Gemp, Luke Marris, Georgios Piliouras, Romuald Elie, Guy Lever, Andrea Tacchetti
Generative Adversarial Equilibrium Solvers. CoRR (2023)
Luke Marris, Ian Gemp, Thomas W. Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers. CoRR (2022)
Ian M. Gemp, Kevin R. McKee, Richard Everett, Edgar A. Duéñez-Guzmán, Yoram Bachrach, David Balduzzi, Andrea Tacchetti
D3C: Reducing the Price of Anarchy in Multi-Agent Learning. AAMAS (2022)
Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers. NeurIPS (2022)
Jan Balaguer, Raphael Koster, Ari Weinstein, Lucy Campbell-Gillingham, Christopher Summerfield, Matthew Botvinick, Andrea Tacchetti
HCMD-zero: Learning Value Aligned Mechanisms from Data. CoRR (2022)
Ian M. Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A. Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Pérolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls
Developing, evaluating and scaling learning agents in multi-agent environments. AI Commun. 35 (4) (2022)
Ian M. Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. AAMAS (2022)
Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew Botvinick, Christopher Summerfield
Human-centered mechanism design with Democratic AI. CoRR (2022)
Ian M. Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Pérolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments. CoRR (2022)
Jan Balaguer, Raphael Koster, Christopher Summerfield, Andrea Tacchetti
The Good Shepherd: An Oracle Agent for Mechanism Design. CoRR (2022)
Justin Fu, Andrea Tacchetti, Julien Pérolat, Yoram Bachrach
Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning. J. Artif. Intell. Res. 71 (2021)
Ian M. Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. CoRR (2021)
Ian M. Gemp, Kevin R. McKee, Richard Everett, Edgar A. Duéñez-Guzmán, Yoram Bachrach, David Balduzzi, Andrea Tacchetti
D3C: Reducing the Price of Anarchy in Multi-Agent Learning. CoRR (2020)
Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian M. Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS (2020)
János Kramár, Neil C. Rabinowitz, Tom Eccles, Andrea Tacchetti
Should I Tear down This Wall? Optimizing Social Metrics by Evaluating Novel Actions. COIN(E)@AAMAS (2020)
János Kramár, Neil C. Rabinowitz, Tom Eccles, Andrea Tacchetti
Should I tear down this wall? Optimizing social metrics by evaluating novel actions. CoRR (2020)
Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian M. Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR (2020)
Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, János Kramár, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia
Relational Forward Models for Multi-Agent Learning. ICLR (Poster) (2019)
Andrea Tacchetti, DJ Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach
A Neural Architecture for Designing Truthful and Efficient Auctions. CoRR (2019)
Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia
Relational Forward Models for Multi-Agent Learning. CoRR (2018)
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu
Relational inductive biases, deep learning, and graph networks. CoRR (2018)
Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
Trading robust representations for sample complexity through self-supervised visual experience. NeurIPS (2018)
Andrea Tacchetti, Leyla Isik, Tomaso A. Poggio
Invariant recognition drives neural representations of action sequences. PLoS Comput. Biol. 13 (12) (2017)
Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos, Tomaso A. Poggio
Representation Learning from Orbit Sets for One-Shot Classification. AAAI Spring Symposia (2017)
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter W. Battaglia, Razvan Pascanu, Andrea Tacchetti
Visual Interaction Networks: Learning a Physics Simulator from Video. NIPS (2017)
Nicholas Watters, Andrea Tacchetti, Theophane Weber, Razvan Pascanu, Peter W. Battaglia, Daniel Zoran
Visual Interaction Networks. CoRR (2017)
Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets. CoRR (2017)
Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio
Unsupervised learning of invariant representations. Theor. Comput. Sci. 633 (2016)
Lorenzo Rosasco, Andrea Tacchetti, Silvia Villa
Regularization by Early Stopping for Online Learning Algorithms. CoRR (2014)
Andrea Tacchetti, Pavan Kumar Mallapragada, Matteo Santoro, Lorenzo Rosasco
GURLS: a Least Squares Library for Supervised Learning CoRR (2013)
Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio
Unsupervised Learning of Invariant Representations in Hierarchical Architectures. CoRR (2013)
Andrea Tacchetti, Pavan Kumar Mallapragada, Matteo Santoro, Lorenzo Rosasco
GURLS: a least squares library for supervised learning. J. Mach. Learn. Res. 14 (1) (2013)

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Publication Activity (10 Years)

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Andrea Tacchetti