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pro vyhledávání: '"John, Philips George"'
We revisit the problem of distribution learning within the framework of learning-augmented algorithms. In this setting, we explore the scenario where a probability distribution is provided as potentially inaccurate advice on the true, unknown distrib
Externí odkaz:
http://arxiv.org/abs/2411.12700
Reinforcement learning algorithms are usually stated without theoretical guarantees regarding their performance. Recently, Jin, Yang, Wang, and Jordan (COLT 2020) showed a polynomial-time reinforcement learning algorithm (namely, LSVI-UCB) for the se
Externí odkaz:
http://arxiv.org/abs/2411.10906
Autor:
Bhattacharyya, Arnab, Gayen, Sutanu, John, Philips George, Sen, Sayantan, Vinodchandran, N. V.
This work establishes a novel link between the problem of PAC-learning high-dimensional graphical models and the task of (efficient) counting and sampling of graph structures, using an online learning framework. We observe that if we apply the expone
Externí odkaz:
http://arxiv.org/abs/2405.07914
We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or unfair) if there is a pair of valid inputs which are close to each other
Externí odkaz:
http://arxiv.org/abs/2006.11737
We identify a new notion of pseudorandomness for randomness sources, which we call the average bias. Given a distribution $Z$ over $\{0,1\}^n$, its average bias is: $b_{\text{av}}(Z) =2^{-n} \sum_{c \in \{0,1\}^n} |\mathbb{E}_{z \sim Z}(-1)^{\langle
Externí odkaz:
http://arxiv.org/abs/1905.11612
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