Teaching an Active Learner with Contrastive Examples

We study the problem of active learning with the added twist that the learner is assisted by a helpful teacher. We consider the following natural interaction protocol: At each round, the learner proposes a query asking for the label of an instance $x^q$, the teacher provides the requested label $\{x^q, y^q\}$ along with explanatory information to guide the learning process. In this paper, we view this information in the form of an additional contrastive example ($\{x^c, y^c\}$) where $x^c$ is picked from a set constrained by $x^q$ (e.g., dissimilar instances with the same label). Our focus is to design a teaching algorithm that can provide an informative sequence of contrastive examples to the learner to speed up the learning process. We show that this leads to a challenging sequence optimization problem where the algorithm's choices at a given round depend on the history of interactions. We investigate an efficient teaching algorithm that adaptively picks these contrastive examples. We derive strong performance guarantees for our algorithm based on two problem-dependent parameters and further show that for specific types of active learners (e.g., a generalized binary search learner), the proposed teaching algorithm exhibits strong approximation guarantees. Finally, we illustrate our bounds and demonstrate the effectiveness of our teaching framework via two numerical case studies.Comment: Fix the illustrative exampl

Learning User Preferences to Incentivize Exploration in the Sharing Economy

We study platforms in the sharing economy and discuss the need for incentivizing users to explore options that otherwise would not be chosen. For instance, rental platforms such as Airbnb typically rely on customer reviews to provide users with relevant information about different options. Yet, often a large fraction of options does not have any reviews available. Such options are frequently neglected as viable choices, and in turn are unlikely to be evaluated, creating a vicious cycle. Platforms can engage users to deviate from their preferred choice by offering monetary incentives for choosing a different option instead. To efficiently learn the optimal incentives to offer, we consider structural information in user preferences and introduce a novel algorithm - Coordinated Online Learning (CoOL) - for learning with structural information modeled as convex constraints. We provide formal guarantees on the performance of our algorithm and test the viability of our approach in a user study with data of apartments on Airbnb. Our findings suggest that our approach is well-suited to learn appropriate incentives and increase exploration on the investigated platform

On Learning with LAD

The logical analysis of data, LAD, is a technique that yields two-class classifiers based on Boolean functions having disjunctive normal form (DNF) representation. Although LAD algorithms employ optimization techniques, the resulting binary classifiers or binary rules do not lead to overfitting. We propose a theoretical justification for the absence of overfitting by estimating the Vapnik-Chervonenkis dimension (VC dimension) for LAD models where hypothesis sets consist of DNFs with a small number of cubic monomials. We illustrate and confirm our observations empirically

Drug utilization study in diabetic patients seeking medical treatment in a north Indian rural medical college hospital

Background: Diabetes Mellitus is a chronic disease and its life-long management causes burden on lifestyle and financial condition of the patients. Drug utilization studies provide useful insights into the current prescribing practices.Methods: To evaluate the drug utilization pattern of anti-diabetic drugs in diabetic patients. A prospective observational study was carried out in adult diabetic patients visiting the Wards and Outpatient Department of General Medicine of a tertiary care hospital. The demographic data and utilization of different classes of anti-diabetic agents as well as individual drugs were analyzed.Results: In 125 patients (Male-65, Female-60), a total of 379 drugs (average 3.032±2.05) were used per day, out of which 76 (20.05%) were rational fixed dose combinations (FDCs) and 261 (68.86%) were prescribed from National List of Essential Medicines (NLEM) 2015. The number of drugs prescribed to be ingested was 326 (86.01%) and 63 (16.62%) were injectables.Conclusions: It was found that the prescription tendencies of the doctors were quite rational. More improvement can be done by sensitizing them to prescribe more drugs from NLEM. The limitations in the affordability of rural population should be taken care of while prescribing drugs for this chronic disease

Enhanced Security Protocol in Wireless Sensor Networks

The need for security in communications is in fact not new. This need has existed in military communications for thousands of years. In this paper, we focus on network protocols that provide security services. Wireless sensor network is an emerging technology that shows applications both for public as well as military purposes. Monitoring is one of the main applications. A large amount of redundant data is generated by sensor nodes. This paper compares all the protocols which are designed for security of wireless sensor network on the basis of security services and propose an improved protocol that reduces communication overhead by removing data redundancy from the network. By using the message sequence number we can check whether it is old message or new message. If the message is old then no need to send that message thereby reducing overhead. It also integrates security by data freshness in the protocol