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Contrastingly, others have observed that larger batch sizes tend to converge to "sharper" optimization

Since the policy used to collect transitions is changing throughout learning, the replay memory contains data coming from a mixture of policies (that differ from the agent's current policy), and

Classical approaches are based on adetect-thendescribeparadigm where separate handcrafted methods areusedtofirstidentify repeatable keypoints and then represent them with a local descriptor.

Extensive experimental results show that our method generalizes well for arbitrary groups ofclients and provides largeperformance improvements giventhe same overall communication/computation resources, compared to other baselines relying on knownpretrainingmethods.

The techniques of machine unlearning, also known as concept erasing, have been developed to address these risks.

Weprovide aunified analysis for MobILE, and demonstrate that MobILE enjoys strong performance guarantees for classes of MDP dynamics that satisfy certain well studied notions of structural complexity. We also show that the ILFO problem isstrictly harder than the standard IL problem by presenting an exponential sample complexity separation between ILand ILFO.

Weprovide aunified analysis for MobILE, and demonstrate that MobILE enjoys strong performance guarantees for classes of MDP dynamics that satisfy certain well studied notions of structural complexity. We also show that the ILFO problem isstrictly harder than the standard IL problem by presenting an exponential sample complexity separation between ILand ILFO.

Neural Information Processing Systems http://nips.cc/