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Janardhan Doppa

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3 papers

1 author row

AAAI Conference 2018 Short Paper

Bayesian Optimization Meets Search Based Optimization: A Hybrid Approach for Multi-Fidelity Optimization

Ellis Hoag
Janardhan Doppa

Many real-life problems require optimizing functions with expensive evaluations. Bayesian Optimization (BO) and Searchbased Optimization (SO) are two broad families of algorithms that try to ﬁnd the global optima of a function with the goal of minimizing the number of function evaluations. A large body of existing work deals with the single-ﬁdelity setting, where function evaluations are very expensive but accurate. However, in many applications, we have access to multiple-ﬁdelity functions that vary in their cost and accuracy of evaluation. In this paper, we propose a novel approach called Multi-ﬁdelity Hybrid (MF-Hybrid) that combines the best attributes of both BO and SO methods to discover the global optima of a blackbox function with minimal cost. Our experiments on multiple benchmark functions show that the MF-Hybrid algorithm outperforms existing single-ﬁdelity and multi-ﬁdelity optimization algorithms.

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AAAI Conference 2014 Conference Paper

Learning Scripts as Hidden Markov Models

John Orr
Prasad Tadepalli
Janardhan Doppa
Xiaoli Fern
Thomas Dietterich

Scripts have been proposed to model the stereotypical event sequences found in narratives. They can be applied to make a variety of inferences including filling gaps in the narratives and resolving ambiguous references. This paper proposes the first formal framework for scripts based on Hidden Markov Models (HMMs). Our framework supports robust inference and learning algorithms, which are lacking in previous clustering models. We develop an algorithm for structure and parameter learning based on Expectation Maximization and evaluate it on a number of natural datasets. The results show that our algorithm is superior to several informed baselines for predicting missing events in partial observation sequences.

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NeurIPS Conference 2011 Conference Paper

Inverting Grice's Maxims to Learn Rules from Natural Language Extractions

Mohammad Sorower
Janardhan Doppa
Walker Orr
Prasad Tadepalli
Thomas Dietterich
Xiaoli Fern

We consider the problem of learning rules from natural language text sources. These sources, such as news articles and web texts, are created by a writer to communicate information to a reader, where the writer and reader share substantial domain knowledge. Consequently, the texts tend to be concise and mention the minimum information necessary for the reader to draw the correct conclusions. We study the problem of learning domain knowledge from such concise texts, which is an instance of the general problem of learning in the presence of missing data. However, unlike standard approaches to missing data, in this setting we know that facts are more likely to be missing from the text in cases where the reader can infer them from the facts that are mentioned combined with the domain knowledge. Hence, we can explicitly model this "missingness" process and invert it via probabilistic inference to learn the underlying domain knowledge. This paper introduces a mention model that models the probability of facts being mentioned in the text based on what other facts have already been mentioned and domain knowledge in the form of Horn clause rules. Learning must simultaneously search the space of rules and learn the parameters of the mention model. We accomplish this via an application of Expectation Maximization within a Markov Logic framework. An experimental evaluation on synthetic and natural text data shows that the method can learn accurate rules and apply them to new texts to make correct inferences. Experiments also show that the method out-performs the standard EM approach that assumes mentions are missing at random.

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