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Gautam Kunapuli

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4 papers

1 author row

AAAI Conference 2019 Conference Paper

Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs

Mayukh Das
Devendra Singh Dhami
Gautam Kunapuli
Kristian Kersting
Sriraam Natarajan

Counting the number of true instances of a clause is arguably a major bottleneck in relational probabilistic inference and learning. We approximate counts in two steps: (1) transform the fully grounded relational model to a large hypergraph, and partially-instantiated clauses to hypergraph motifs; (2) since the expected counts of the motifs are provably the clause counts, approximate them using summary statistics (in/outdegrees, edge counts, etc). Our experimental results demonstrate the efficiency of these approximations, which can be applied to many complex statistical relational models, and can be significantly faster than state-of-the-art, both for inference and learning, without sacrificing effectiveness.

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IJCAI Conference 2018 Conference Paper

On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach

Sriraam Natarajan
Srijita Das
Nandini Ramanan
Gautam Kunapuli
Predrag Radivojac

We consider the problem of actively feature elicitation in which given a few examples with all the features (say the full EHR) and a few examples with some of the features (say demographics), the goal is to identify the set of examples on whom more informationÂ (say the lab tests) needs to be collected. The observation is that some set of features may be more expensive, personal or cumbersome to collect. We propose an active learning approach which identifies examples that are dissimilar to the ones with the full set of data and acquire the complete set of features for these examples. Motivated by real clinical tasks, our extensive evaluation on three clinical tasks demonstrate the effectiveness of this approach.

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KR Conference 2018 Short Paper

Structure Learning for Relational Logistic Regression: An Ensemble Approach

Nandini Ramanan
Gautam Kunapuli
Tushar Khot
Bahare Fatemi
Seyed Mehran Kazemi
David Poole
Kristian Kersting
Sriraam Natarajan

We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on functional-gradient boosting methods for probabilistic logic models. Our empirical evaluation on standard data sets demonstrates the superiority of our approach over other methods for learning RLR.

NeurIPS Conference 2011 Conference Paper

Advice Refinement in Knowledge-Based SVMs

Gautam Kunapuli
Richard Maclin
Jude Shavlik

Knowledge-based support vector machines (KBSVMs) incorporate advice from domain experts, which can improve generalization significantly. A major limitation that has not been fully addressed occurs when the expert advice is imperfect, which can lead to poorer models. We propose a model that extends KBSVMs and is able to not only learn from data and advice, but also simultaneously improve the advice. The proposed approach is particularly effective for knowledge discovery in domains with few labeled examples. The proposed model contains bilinear constraints, and is solved using two iterative approaches: successive linear programming and a constrained concave-convex approach. Experimental results demonstrate that these algorithms yield useful refinements to expert advice, as well as improve the performance of the learning algorithm overall.

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