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Active offline policy selection

Yutian Chen, Ksenia Konyushkova, Tom Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas

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ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions

Luigi Carratino *, Daniele Calandriello, Stefano Vigogna *, Lorenzo Rosasco *

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Is Bang-Bang Control All You Need?

Markus Wulfmeier, Martin Riedmiller, Tim Seyde *, Wilko Schwarting *, Igor Gilitschenski *, Bartolomeo Stellato *, Daniela Rus *

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On the Expressivity of Markov Reward

David Abel, Doina Precup, Anna Harutyunyan, Mark Ho *, Michael Littman *, Will Dabney, Satinder Baveja

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Which priors matter? Benchmarking models for learning latent dynamics

Alex Botev, Drew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins

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SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision

Irina Higgins, Drew Jaegle, Peter Wirnsberger, Alex Botev

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The Difficulty of Passive Learning in Deep Reinforcement Learning

Georg Ostrovski, P Castro *, Will Dabney

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Entropy-based adaptive Hamiltonian Monte Carlo

Marcel Hirt *, Michalis Titsias, Petros Dellaportas *

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Overcoming the Convex Barrier for Simplex Inputs

Harkirat Behl *, P Torr *, Pawan Mudigonda, Dj Dvijotham

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Asymptotically Best Casual Effect Identification with Multi-Armed Bandits

Alan Malek, Silvia Chiappa

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How to transfer algorithmic reasoning knowledge to learn new algorithms?

Louis-Pascal A. C. Xhonneux *, Andreea Deac, Petar Veličković, Jian Tang *

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Self-Consistent Models and Values

Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado van Hasselt, David Silver

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Towards mental time travel: a hierarchical memory for reinforcement learning agents

Andrew Lampinen, Stephanie Chan, Andrea Banino, Felix Hill

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Powerpropagation: A sparsity inducing weight reparameterisation

Jonathan Schwarz, Siddhant Jayakumar, Razvan Pascanu, Peter Latham *, Yee Whye Teh

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Temporally Abstract Partial Models

Khimya Khetarpal *, Zafarali Ahmed, Gheorghe Comanici, Doina Precup

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Drop, Swap, and Generate: A Self-SupervisedApproach for Disentangling Neural Activity

Ran Liu *, Mehdi Azabou *, Max Dabagia *, Mohammad Gheshlaghi Azar, E Dyer *, Michal Valko

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No Regrets for Learning the Prior in Bandits

Soumya Basu *, Manzil Zaheer *, Branislav Kveton *, Csaba Szepesvari

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A Provably Efficient Sample Collection Strategy for Reinforcement Learning

Jean Tarbouriech *, Matteo Pirotta *, Michal Valko, Alessandro Lazaric *

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Multimodal Few-Shot Learning with Frozen Language Models

Jacob Menick, Maria Tsimpoukelli, Serkan Cabi, Ali Eslami, Oriol Vinyals, Felix Hill

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Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Rosemary Ke, Aniket Didolkar *, Sarthak Mittal *, Anirudh Goyal *, Guillaume Lajoie *, Stefan Bauer *, Danilo Jimenez Rezende, Yoshua Bengio *, M Mozer *, C Pal *

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Laplace Redux -- Effortless Bayesian Deep Learning

Erik Daxberger *, Matthias Bauer, Runa Eschenhagen *, Alexander Immer *, Agustinus Kristiadi *, Philipp Hennig *

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On the Role of Optimization in Double Descent: A Least Squares Study

Omar Rivasplata, Amal Rannen-Triki, Ilja Kuzborskij, Razvan Pascanu, Csaba Szepesvari

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Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel

Dominic Richards *, Ilja Kuzborskij

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Unifying gradient estimators for meta-reinforcement learning via off-policy evaluation

Yunhao Tang, Tadashi Kozuno *, Mark Rowland, Remi Munos, Michal Valko

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The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning

Timothy Lillicrap, B Richards *

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