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pro vyhledávání: '"Madani, Omid"'
Autor:
Madani, Omid
Consider a predictor, a learner, whose input is a stream of discrete items. The predictor's task, at every time point, is probabilistic multiclass prediction, i.e., to predict which item may occur next by outputting zero or more candidate items, each
Externí odkaz:
http://arxiv.org/abs/2402.10142
Autor:
Madani, Omid
We present a system for bottom-up cumulative learning of myriad concepts corresponding to meaningful character strings, and their part-related and prediction edges. The learning is self-supervised in that the concepts discovered are used as predictor
Externí odkaz:
http://arxiv.org/abs/2112.09348
Autor:
Madani, Omid, Ngo, Thanh, Zeng, Weifei, Averine, Sai Ankith, Evuru, Sasidhar, Malhotra, Varun, Gandham, Shashidhar, Yadav, Navindra
An important task of community discovery in networks is assessing significance of the results and robust ranking of the generated candidate groups. Often in practice, numerous candidate communities are discovered, and focusing the analyst's time on t
Externí odkaz:
http://arxiv.org/abs/2012.09968
Autor:
Jeyakumar, Vimalkumar, Madani, Omid, Parandeh, Ali, Kulshreshtha, Ashutosh, Zeng, Weifei, Yadav, Navindra
We present ExplainIt!, a declarative, unsupervised root-cause analysis engine that uses time series monitoring data from large complex systems such as data centres. ExplainIt! empowers operators to succinctly specify a large number of causal hypothes
Externí odkaz:
http://arxiv.org/abs/1903.08132
Autor:
Madani, Omid.
Publikováno v:
Connect to this title online; UW restricted.
Thesis (Ph. D.)--University of Washington, 2000.
Vita. Includes bibliographical references (p. 142-150).
Vita. Includes bibliographical references (p. 142-150).
Externí odkaz:
http://hdl.handle.net/1773/7016
Autor:
Madani, Omid
Value iteration is a commonly used and empirically competitive method in solving many Markov decision process problems. However, it is known that value iteration has only pseudo-polynomial complexity in general. We establish a somewhat surprising pol
Externí odkaz:
http://arxiv.org/abs/1301.0583
Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND th
Externí odkaz:
http://arxiv.org/abs/1212.2472
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins not with a training sample, but instead with resources that it can use
Externí odkaz:
http://arxiv.org/abs/1207.4138
Predicting the outcomes of future events is a challenging problem for which a variety of solution methods have been explored and attempted. We present an empirical comparison of a variety of online and offline adaptive algorithms for aggregating expe
Externí odkaz:
http://arxiv.org/abs/1206.6814
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