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Publications

Theory & foundations

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Universal Agent Mixtures and the Geometry of Intelligence
Samuel Allen Alexander *, David Quarel *, Len Du *, Marcus Hutter
AISTATS
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Learnable Commutative Monoids for Graph Neural Networks
Euan Ong *, Petar Veličković
LoG
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Towards combinatorial invariance for Kazhdan-Lusztig polynomials
Charles Blundell, Lars Buesing, Alex Davies, Petar Veličković, Geordie Williamson *
Representation Theory
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Testing Independence of Exchangeable Random Variables
Marcus Hutter
arXiv
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Beyond Bayes-optimality: meta-learning what you know you don’t know
Jordi Grau, Grégoire Delétang, Markus Kunesch, Tim Genewein, Kevin Li, Elliot Catt, Anian Ruoss, Chris Cundy, Joel Veness, Jane Wang, Marcus Hutter, Christopher Summerfield, Shane Legg
arXiv
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Game Theoretic Rating in n-player general-sum games with Equilibria
Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen Mcaleer, Jerome Connor, Karl Tuyls, Thore Graepel *
arXiv
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Optimistic posterior sampling for reinforcement learning with few samples and tight guarantees
Daniil Tiapkin *, Denis Belomestny *, Daniele Calandriello, Eric Moulines *, Alexey Naumov *, Remi Munos, Mark Rowland, Pierre Menard *, Michal Valko
NeurIPS
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Reinforcement Learning with Information Theoretic Actuation
Elliot Catt, Marcus Hutter, Joel Veness
arXiv
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Generalised Policy Improvement with Geometric Policy Composition
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Remi Munos, Andre Barreto
ICML
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From Dirichlet to Rubin: Optimistic exploration in RL without bonuses
Daniil Tiapkin *, Denis Belomestny *, Éric Moulines *, Alexey Naumov *, Sergey Samsonov *, Yunhao Tang, Michal Valko, Pierre Menard *
ICML
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Scaling Gaussian process optimization by evaluating a few unique candidates multiple times
Daniele Calandriello, Luigi Carratino *, Alessandro Lazaric *, Lorenzo Rosasco *, Michal Valko
ICML
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Publication
Uniqueness and Complexity of Inverse MDP Models
Marcus Hutter
arXiv
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Graph Neural Networks are Dynamic Programmers
Andrew Dudzik, Petar Veličković
NeurIPS
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An adaptive and efficient multi-goal exploration
Jean Tarbouriech *, Omar Darwiche Domingues *, Pierre Ménard *, Matteo Pirotta *, Michal Valko, Alessandro Lazaric *
AISTATS
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Marginalized operators for off-policy reinforcement learning
Yunhao Tang *, Mark Rowland, Remi Munos, Michal Valko
AISTATS
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Your Policy Regularizer is Secretly an Adversary
Rob Brekelmans *, Tim Genewein, Jordi Grau, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro Ortega
arXiv
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Publication
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent
Ian Gemp, Rahul Savani *, Marc Lanctot, Yoram Bachrach, Thomas Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
AAMAS
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Publication
Reward-Punishment Symmetric Universal Intelligence
Marcus Hutter, Samuel Allen Alexander *
arXiv
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Learning Optimal Conformal Classifiers
David Stutz, Taylan Cemgil, Krishnamurthy Dvijotham *, Arnaud Doucet
ICLR
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Publication
Feature and Parameter Selection in Stochastic Linear Bandits
Ahmadreza Moradipari *, Berkay Turan *, Yasin Abbasi Yadkori, Mahnoosh Alizadeh *, Mohammad Ghavamzadeh *
ICML
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On the Role of Neural Collapse in Transfer Learning
Andras Gyorgy, Marcus Hutter
ICLR
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ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
Luigi Carratino *, Daniele Calandriello, Stefano Vigogna *, Lorenzo Rosasco *
NeurIPS
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On the Expressivity of Markov Reward
David Abel, Doina Precup, Anna Harutyunyan, Mark Ho *, Michael Littman *, Will Dabney, Satinder Baveja
NeurIPS
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Asymptotically Best Casual Effect Identification with Multi-Armed Bandits
Alan Malek, Silvia Chiappa
NeurIPS
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Statistical discrimination in learning agents
Joel Leibo, Yiran Mao, Kevin McKee, William Isaac, Silvia Chiappa, Ben Coppin, Karl Tuyls, Yoram Bachrach, Suzanne Sadedin *, Sasha Vezhnevets, Michiel Bakker
arXiv
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