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Operating safely and reliably despite continual distribution shifts is vital for high-stakes machine learning applications.
The complexity of markov decision processes
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Sample complexity of multi-task reinforcement learning
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Matrix completion has no spurious local minimum
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Generalization bounds for non-stationary mixing processes
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Leave no trace: Learning to reset for safe and autonomous reinforcement learning
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Partially observable markov decision processes and robotics
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Pareto policy adaptation
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In-context reinforcement learning with algorithm distillation
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Pareto set learning for expensive multi-objective optimization
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Discovered policy optimisation
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The fundamentals of heavy tails: Properties, emergence, and estimation , volume 53
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A generalist agent
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Neural continuous-discrete state space models for irregularly-sampled time series
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