Naveen Nair

Naveen Nair
Amazon

PhD Computer Science And Eng. (Machine Learning)

About

9
Publications
141
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11
Citations
Introduction
I work on improving machine learning and inductive logic programming algorithms. I would like to contribute to the scientific advancements by inventing new methodologies, products and services. My goal includes publishing in top conferences/journals and/or getting patents. I am currently working with the Machine Learning team of Amazon. Prior to that, I have completed my PhD from IIT Bombay, India and Monash University, Australia (dual badged PhD programme).

Publications

Publications (9)
Article
The goal in Rule Ensemble Learning (REL) is simultaneous discovery of a small set of simple rules and their optimal weights that lead to good generalization. Rules are assumed to be conjunctions of basic propositions concerning the values taken by the input features. It has been shown that rule ensembles for classification can be learnt optimally a...
Article
Full-text available
Discovering relational structure between input features in sequence labeling models has shown to improve their accuracy in several problem settings. However, the search space of relational features is exponential in the number of basic input features. Consequently, approaches that learn relational features, tend to follow a greedy search strategy....
Conference Paper
Building relational models for the structured output classification problem of sequence labeling has been recently explored in a few research works. The models built in such a manner are interpretable and capture much more information about the domain (than models built directly from basic attributes), resulting in accurate predictions. On the othe...
Conference Paper
Discovering relational structure between input features in sequence labeling models has shown to improve their accuracies in several problem settings. The problem of learning relational structure for sequence labeling can be posed as learning Markov Logic Networks (MLN) for sequence labeling, which we abbreviate as Markov Logic Chains (MLC). This o...
Conference Paper
Non intrusive activity recognition systems typically read values from sensors deployed in an environment and combine them with user annotated activities to build a probabilistic model. Recently, features constructed from activity specific conjunctions of binary sensor values have been shown to improve the classification accuracy. Such systems emplo...
Conference Paper
Hidden Markov Models (HMMs) are widely used in activity recognition. Ideally, the current activity should be determined using the vector of all sensor readings; however, this results in an exponentially large space of observations. The current fix to this problem is to assume conditional independence between individual sensors, given an activity, a...
Conference Paper
Many SRL models pose logical inference as weighted satisfiability solving. Performing logical inference after completely grounding clauses with all possible constants is computationally expensive and approaches such as LazySAT [8] utilize the sparseness of the domain to deal with this. Here, we investigate the efficiency of restricting the Knowledg...

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