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Manuel Blum

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3 papers

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AAAI Conference 2016 Conference Paper

ClaimEval: Integrated and Flexible Framework for Claim Evaluation Using Credibility of Sources

Mehdi Samadi
Partha Talukdar
Manuela Veloso
Manuel Blum

The World Wide Web (WWW) has become a rapidly growing platform consisting of numerous sources which provide supporting or contradictory information about claims (e. g. , “Chicken meat is healthy”). In order to decide whether a claim is true or false, one needs to analyze content of different sources of information on the Web, measure credibility of information sources, and aggregate all these information. This is a tedious process and the Web search engines address only part of the overall problem, viz. , producing only a list of relevant sources. In this paper, we present ClaimEval, a novel and integrated approach which given a set of claims to validate, extracts a set of pro and con arguments from the Web information sources, and jointly estimates credibility of sources and correctness of claims. ClaimEval uses Probabilistic Soft Logic (PSL), resulting in a ﬂexible and principled framework which makes it easy to state and incorporate different forms of prior-knowledge. Through extensive experiments on realworld datasets, we demonstrate ClaimEval’s capability in determining validity of a set of claims, resulting in improved accuracy compared to state-of-the-art baselines.

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NeurIPS Conference 2015 Conference Paper

Efficient and Robust Automated Machine Learning

Matthias Feurer
Aaron Klein
Katharina Eggensperger
Jost Springenberg
Manuel Blum
Frank Hutter

The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. In this work we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub auto-sklearn, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of auto-sklearn.

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AAAI Conference 2013 Conference Paper

OpenEval: Web Information Query Evaluation

Mehdi Samadi
Manuela Veloso
Manuel Blum

In this paper, we investigate information validation tasks that are initiated as queries from either automated agents or humans. We introduce OpenEval, a new online information validation technique, which uses information on the web to automatically evaluate the truth of queries that are stated as multiargument predicate instances (e. g. , DrugHasSideEffect(Aspirin, GI Bleeding))). OpenEval gets a small number of instances of a predicate as seed positive examples and automatically learns how to evaluate the truth of a new predicate instance by querying the web and processing the retrieved unstructured web pages. We show that OpenEval is able to respond to the queries within a limited amount of time while also achieving high F1 score. In addition, we show that the accuracy of responses provided by OpenEval is increased as more time is given for evaluation. We have extensively tested our model and shown empirical results that illustrate the effectiveness of our approach compared to related techniques.

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