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Bootstrap Your Own Latent: A new approach to self-supervised learning

Jean-Bastien Grill
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Florian Strub
Florent Altché
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Corentin Tallec
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Pierre H Richemond
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Carl Doersch
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Bilal Piot
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Rémi Munos
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Michal Valko


We investigate and provide new insights on the sampling rule called Top-Two Thompson Sampling (TTTS). In particular, we justify its use for fixed-confidence best-arm identification. We further propose a variant of TTTS called Top-Two Transportation Cost (T3C), which disposes of the computational burden of TTTS. As our main contribution, we provide the first sample complexity analysis of TTTS and T3C when coupled with a very natural Bayesian stopping rule, for bandits with Gaussian rewards, solving one of the open questions raised by Russo (2016). We also provide new posterior convergence results for TTTS under two models that are commonly used in practice: bandits with Gaussian and Bernoulli rewards and conjugate priors.
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Dates and versions

hal-02869787 , version 1 (16-06-2020)
hal-02869787 , version 2 (25-02-2021)



Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, et al.. Bootstrap Your Own Latent: A new approach to self-supervised learning. Neural Information Processing Systems, 2020, Montréal, Canada. ⟨hal-02869787v2⟩
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