Learn about sample complexity in machine learning and how to assess the efficiency of a learning algorithm to determine the data needed for a specific learning goal. 31. Juli 2023 · 9 Min. Lesezeit Inhalt Sampl
This metric is the measure of complexity for hypothesis space in the average. Similarly in the worst case, we obtain the minimum metric. We make clear the relationship between these measures and the Vapnik-Chervonenkis (VC) dimension. Finally, we show the upper bound on sample complexity ...
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Rosasco, "On the sample complexity of subspace learning," in Conference on Neural Information Processing Systems (NIPS), 2013.Alessandro Rudi, Guillermo D Canas, and Lorenzo Rosasco. On the sample complexity of subspace learning. In Advances in Neural Information Processing Systems, pages 2067-...
In this paper, we ask the following question: given t and δ, what is the minimum n (number of copies of ρ) necessary to implement the unitary e −iρt on an unknown state σ to trace distance at most δ? We call this the sample complexity of Hamiltonian simulation. While the LMR...
We prove new PAC bounds on the sample-complexity of two well-known model-based reinforcement learning (RL) algorithms in the presence of a generative model of the MDP: value iter- ation and policy iteration. The first result indicates that for an MDP with N state-action pairs and the ...
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Embracing such complexity requires a nuanced approach. Unlike unregularized approaches (e.g., linear regression), supervised machine learning methods allow the opportunity to aggregate disparate small variable effects to inform clinical outcomes while also accounting for complex, interactive, or non-linear...
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the sample complexity of a standard RL algorithm can be prohibitively...To reduce sample complexity, we reduce the top-k list reward to a set of item-wise rewards and reformulate...To address this, we consider enhancing the learning capability of language model-driven recommendation......