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pro vyhledávání: '"Lemhadri, Ismael"'
We introduce region-based explanations (RbX), a novel, model-agnostic method to generate local explanations of scalar outputs from a black-box prediction model using only query access. RbX is based on a greedy algorithm for building a convex polytope
Externí odkaz:
http://arxiv.org/abs/2210.08721
Autor:
Qu, Ao, Lemhadri, Ismael
The rapid growth of social media has been witnessed during recent years as a result of the prevalence of the internet. This trend brings an increasing interest in simulating social media which can provide valuable insights to both academic researcher
Externí odkaz:
http://arxiv.org/abs/2108.04818
Publikováno v:
Journal of Machine Learning Research 22 (2021) 1-29
Much work has been done recently to make neural networks more interpretable, and one obvious approach is to arrange for the network to use only a subset of the available features. In linear models, Lasso (or $\ell_1$-regularized) regression assigns z
Externí odkaz:
http://arxiv.org/abs/1907.12207
Autor:
Lemhadri, Ismael
Publikováno v:
Market Microstructure and Liquidity 2020
The latent order book of \cite{donier2015fully} is one of the most promising agent-based models for market impact. This work extends the minimal model by allowing agents to exhibit mean-reversion, a commonly observed pattern in real markets. This mod
Externí odkaz:
http://arxiv.org/abs/1802.06101
Autor:
Lemhadri, Ismael1 lemhadri@stanford.edu
Publikováno v:
Market Microstructure & Liquidity. Dec2021, Vol. 5 Issue 1n4, p1-23. 23p.
Publikováno v:
Proc Mach Learn Res
Much work has been done recently to make neural networks more interpretable, and one approach is to arrange for the network to use only a subset of the available features. In linear models, Lasso (or ℓ(1)-regularized) regression assigns zero weight
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::f16a6535ef66faa7985b148492671c70
https://europepmc.org/articles/PMC9453696/
https://europepmc.org/articles/PMC9453696/
Akademický článek
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Autor:
Lemhadri I; Stanford University., Ruan F; Stanford University., Tibshirani R; Stanford University.
Publikováno v:
Proceedings of machine learning research [Proc Mach Learn Res] 2021 Apr; Vol. 130, pp. 10-18.