Andrea Tacchetti

According to our database, Andrea Tacchetti authored at least 31 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Teamwork Reinforcement Learning With Concave Utilities.

Zheng Yu, Junyu Zhang, Zheng Wen, Andrea Tacchetti, Mengdi Wang, Ian Gemp

IEEE Trans. Mob. Comput., May, 2024

Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem.

Raphael Koster, Miruna Pîslar, Andrea Tacchetti, Jan Balaguer, Leqi Liu, Romuald Elie, Oliver P. Hauser, Karl Tuyls, Matt M. Botvinick, Christopher Summerfield

CoRR, 2024

Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience.

Manfred Diaz, Liam Paull, Andrea Tacchetti

CoRR, 2024

Gemma: Open Models Based on Gemini Research and Technology.

Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Slone, Amélie Héliou, Andrea Tacchetti, Anna Bulanova, Antonia Paterson, Beth Tsai, Bobak Shahriari, Charline Le Lan, Christopher A. Choquette-Choo, Clément Crepy, Daniel Cer, Daphne Ippolito, David Reid, Elena Buchatskaya, Eric Ni, Eric Noland, Geng Yan, George Tucker, George-Cristian Muraru, Grigory Rozhdestvenskiy, Henryk Michalewski, Ian Tenney, Ivan Grishchenko, Jacob Austin, James Keeling, Jane Labanowski, Jean-Baptiste Lespiau, Jeff Stanway, Jenny Brennan, Jeremy Chen, Johan Ferret, Justin Chiu, et al.

CoRR, 2024

Generative Adversarial Equilibrium Solvers.

Denizalp Goktas, David C. Parkes, Ian Gemp, Luke Marris, Georgios Piliouras, Romuald Elie, Guy Lever, Andrea Tacchetti

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Scaling Opponent Shaping to High Dimensional Games.

Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

2022

The Good Shepherd: An Oracle Agent for Mechanism Design.

Jan Balaguer, Raphael Koster, Christopher Summerfield, Andrea Tacchetti

CoRR, 2022

HCMD-zero: Learning Value Aligned Mechanisms from Data.

Jan Balaguer, Raphael Koster, Ari Weinstein, Lucy Campbell-Gillingham, Christopher Summerfield, Matthew M. Botvinick, Andrea Tacchetti

CoRR, 2022

Human-centered mechanism design with Democratic AI.

Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver P. Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew M. Botvinick, Christopher Summerfield

CoRR, 2022

Developing, evaluating and scaling learning agents in multi-agent environments.

Ian 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

AI Commun., 2022

Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers.

Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent.

Ian Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

D3C: Reducing the Price of Anarchy in Multi-Agent Learning.

Ian Gemp, Kevin R. McKee, Richard Everett, Edgar A. Duéñez-Guzmán, Yoram Bachrach, David Balduzzi, Andrea Tacchetti

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021

Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning.

Justin Fu, Andrea Tacchetti, Julien Pérolat, Yoram Bachrach

J. Artif. Intell. Res., 2021

2020

Learning to Play No-Press Diplomacy with Best Response Policy Iteration.

Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Should I Tear down This Wall? Optimizing Social Metrics by Evaluating Novel Actions.

János Kramár, Neil C. Rabinowitz, Tom Eccles, Andrea Tacchetti

Proceedings of the Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XIII, 2020

2019

A Neural Architecture for Designing Truthful and Efficient Auctions.

Andrea Tacchetti, DJ Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach

CoRR, 2019

Relational Forward Models for Multi-Agent Learning.

Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, János Kramár, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Relational Forward Models for Multi-Agent Learning.

Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia

CoRR, 2018

Relational inductive biases, deep learning, and graph networks.

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 M. Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu

CoRR, 2018

Trading robust representations for sample complexity through self-supervised visual experience.

Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Learning invariant representations of actions and faces.

Andrea Tacchetti

PhD thesis, 2017

Invariant recognition drives neural representations of action sequences.

Andrea Tacchetti, Leyla Isik, Tomaso A. Poggio

PLoS Comput. Biol., 2017

Visual Interaction Networks.

Nicholas Watters, Andrea Tacchetti, Theophane Weber, Razvan Pascanu, Peter W. Battaglia, Daniel Zoran

CoRR, 2017

Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets.

Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos

CoRR, 2017

Visual Interaction Networks: Learning a Physics Simulator from Video.

Nicholas Watters, Daniel Zoran, Theophane Weber, Peter W. Battaglia, Razvan Pascanu, Andrea Tacchetti

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Representation Learning from Orbit Sets for One-Shot Classification.

Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos, Tomaso A. Poggio

Proceedings of the 2017 AAAI Spring Symposia, 2017

2016

Unsupervised learning of invariant representations.

Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio

Theor. Comput. Sci., 2016

2014

Regularization by Early Stopping for Online Learning Algorithms.

Lorenzo Rosasco, Andrea Tacchetti, Silvia Villa

CoRR, 2014

2013

GURLS: a least squares library for supervised learning.

Andrea Tacchetti, Pavan Kumar Mallapragada, Matteo Santoro, Lorenzo Rosasco

J. Mach. Learn. Res., 2013

Unsupervised Learning of Invariant Representations in Hierarchical Architectures.

Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio

CoRR, 2013