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ORCIDAndrea Tacchetti
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2020 – today
- 2024
[j6]Zheng Yu
, Junyu Zhang, Zheng Wen, Andrea Tacchetti, Mengdi Wang, Ian Gemp:
Teamwork Reinforcement Learning With Concave Utilities. IEEE Trans. Mob. Comput. 23(5): 5709-5721 (2024)- [c11]Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster:
Scaling Opponent Shaping to High Dimensional Games. AAMAS 2024: 1001-1010 - [c10]Denizalp Goktas, David C. Parkes, Ian Gemp, Luke Marris, Georgios Piliouras, Romuald Elie, Guy Lever, Andrea Tacchetti:
Generative Adversarial Equilibrium Solvers. ICLR 2024 - [i22]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.:
Gemma: Open Models Based on Gemini Research and Technology. CoRR abs/2403.08295 (2024) - [i21]Manfred Diaz, Liam Paull, Andrea Tacchetti:
Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience. CoRR abs/2404.03084 (2024) - [i20]Raphael Koster, Miruna Pîslar, Andrea Tacchetti, Jan Balaguer, Leqi Liu, Romuald Elie, Oliver P. Hauser, Karl Tuyls, Matt M. Botvinick, Christopher Summerfield:
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem. CoRR abs/2404.15059 (2024) - 2023
- [i19]Denizalp Goktas, David C. Parkes, Ian Gemp, Luke Marris, Georgios Piliouras, Romuald Elie, Guy Lever, Andrea Tacchetti:
Generative Adversarial Equilibrium Solvers. CoRR abs/2302.06607 (2023) - [i18]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 abs/2312.12568 (2023) - 2022
- [j5]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:
Developing, evaluating and scaling learning agents in multi-agent environments. AI Commun. 35(4): 271-284 (2022) - [c9]Ian 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: 498-506 - [c8]Ian 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: 507-515 - [c7]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 - [i17]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:
Human-centered mechanism design with Democratic AI. CoRR abs/2201.11441 (2022) - [i16]Jan Balaguer, Raphael Koster, Ari Weinstein, Lucy Campbell-Gillingham, Christopher Summerfield, Matthew M. Botvinick, Andrea Tacchetti:
HCMD-zero: Learning Value Aligned Mechanisms from Data. CoRR abs/2202.10122 (2022) - [i15]Jan Balaguer, Raphael Koster, Christopher Summerfield, Andrea Tacchetti:
The Good Shepherd: An Oracle Agent for Mechanism Design. CoRR abs/2202.10135 (2022) - [i14]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:
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments. CoRR abs/2209.10958 (2022) - [i13]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 abs/2210.09257 (2022) - 2021
- [j4]Justin Fu, Andrea Tacchetti, Julien Pérolat, Yoram Bachrach:
Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning. J. Artif. Intell. Res. 71: 925-951 (2021) - [i12]Ian 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 abs/2106.01285 (2021) - 2020
- [c6]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: 114-130 - [c5]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:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS 2020 - [i11]János Kramár, Neil C. Rabinowitz, Tom Eccles, Andrea Tacchetti:
Should I tear down this wall? Optimizing social metrics by evaluating novel actions. CoRR abs/2004.07625 (2020) - [i10]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:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR abs/2006.04635 (2020) - [i9]Ian 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 abs/2010.00575 (2020)
2010 – 2019
- 2019
- [c4]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:
Relational Forward Models for Multi-Agent Learning. ICLR (Poster) 2019 - [i8]Andrea Tacchetti, DJ Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach:
A Neural Architecture for Designing Truthful and Efficient Auctions. CoRR abs/1907.05181 (2019) - 2018
- [c3]Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos:
Trading robust representations for sample complexity through self-supervised visual experience. NeurIPS 2018: 9640-9650 - [i7]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:
Relational inductive biases, deep learning, and graph networks. CoRR abs/1806.01261 (2018) - [i6]Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia:
Relational Forward Models for Multi-Agent Learning. CoRR abs/1809.11044 (2018) - 2017
- [b1]Andrea Tacchetti:
Learning invariant representations of actions and faces. Massachusetts Institute of Technology, Cambridge, USA, 2017 - [j3]Andrea Tacchetti, Leyla Isik, Tomaso A. Poggio:
Invariant recognition drives neural representations of action sequences. PLoS Comput. Biol. 13(12) (2017) - [c2]Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos, Tomaso A. Poggio:
Representation Learning from Orbit Sets for One-Shot Classification. AAAI Spring Symposia 2017 - [c1]Nicholas Watters, Daniel Zoran, Theophane Weber, Peter W. Battaglia, Razvan Pascanu, Andrea Tacchetti:
Visual Interaction Networks: Learning a Physics Simulator from Video. NIPS 2017: 4539-4547 - [i5]Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos:
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets. CoRR abs/1703.04775 (2017) - [i4]Nicholas Watters, Andrea Tacchetti, Theophane Weber, Razvan Pascanu, Peter W. Battaglia, Daniel Zoran:
Visual Interaction Networks. CoRR abs/1706.01433 (2017) - 2016
- [j2]Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio:
Unsupervised learning of invariant representations. Theor. Comput. Sci. 633: 112-121 (2016) - 2014
- [i3]Lorenzo Rosasco, Andrea Tacchetti, Silvia Villa:
Regularization by Early Stopping for Online Learning Algorithms. CoRR abs/1405.0042 (2014) - 2013
- [j1]Andrea Tacchetti, Pavan Kumar Mallapragada, Matteo Santoro, Lorenzo Rosasco:
GURLS: a least squares library for supervised learning. J. Mach. Learn. Res. 14(1): 3201-3205 (2013) - [i2]Andrea Tacchetti, Pavan Kumar Mallapragada, Matteo Santoro, Lorenzo Rosasco:
GURLS: a Least Squares Library for Supervised Learning. CoRR abs/1303.0934 (2013) - [i1]Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio:
Unsupervised Learning of Invariant Representations in Hierarchical Architectures. CoRR abs/1311.4158 (2013)
Coauthor Index
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last updated on 2024-10-07 22:20 CEST by the dblp team
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