IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Primary Caregivers are the fulcrum in the physician-caregiver-child triad. Existing literature discusses static multi-component interventions in detail. In long-term treatments, dynamic intervention design is needed as the environment and situations of the families are dynamic. The objectives of this study are (a) to identify the components of the primary caregiver’s perception of the physician’s value with reference to the effectiveness of consultation and relationships with the former and with the child; (b) to establish the role of this perception in designing dynamic interventions, and (c) to describe the perception’s potential influence on adherence. A PRISMA, chronological, and morphological analysis of the literature is carried out about caregivers’ adherence in the pediatric long-term treatment context. We define communication and consultation as the functional, whereas relationship as the emotional component of the caregiver’s perception of the physician. We propose a theoretical model that incorporates intervention as an integral component of care. Adherence happens as a response to changing situations and hence fluctuates. Hence, a dynamic intervention design to benefit the child should be incorporated into care through the caregiver-physician bridge. Future research should explore how intervention needs change and the driving reasons for understanding the static and dynamic components of interventions. © 2020 Taylor &amp; Francis Group, LLC.

Department of Management Studies

World University root node

Health Communication

Designing Dynamic Interventions to Improve Adherence in Pediatric Long-term Treatment – The Role of Perceived Value of the Physician by Primary Caregivers

Data from Global Positioning Systems (GPS) and fare-meters in For-Hire vehicles (FHVs) have been used for various applications - both in research as well as organizational decision-making. The utility of such exercises largely depend on the accuracy of the data. This study looks at an environment where the data is partially mislabeled. Specifically, we take a common real-world setting where vehicle operators choose to render transportation services to customers without the use of a fare-meter, often by negotiating a fixed rate with the customer. This practice, which to different degrees, has been observed and documented across urban areas in the world, leads to various undesirable effects. In this study, we seek to identify cases of such behavior in the dataset. Typically, a supervised learning classifier could be built to predict the occupancy status from GPS traces, which can then be used, to look for anomalies between the predicted and stated behaviors. However, in our case the training dataset also contains instances of incorrect tagging. We address this problem by casting it as one of learning from Positive and Unlabeled instances (PU Learning) . This is owing to the fact that we observe the phenomenon of one-sided label noise, where trips tagged 'vacant' by the taximeter could be truly vacant or occupied, whereas trips tagged 'occupied' are expected to be occupied in reality as well. To support this novel formulation, we apply three state-of-the-art PU Learning algorithms on a real-world trajectory data set from an organization plying 170 active vehicles over a period of two months. We compare these to the baselines of standard supervised learning. Validation is carried out by the organization through alternate channels of investigation which is not indicated in the data set. The results show that the PU Learners provide a significant improvement in classification across a range of metrics when compared to the baseline approaches. This translates to a significant increase in identifying or reclassifying the mislabeled rides. © 2021 ACM.

Department of Computer Science and Engineering

Department of Humanities and Social Sciences

ACM International Conference Proceeding Series

Inferring customer occupancy status in for-hire vehicles using PU Learning

This paper explores the use of designed experiments in an online environment. Motivated by real-world examples, we model a scenario where the practitioner is given a finite set of units and needs to select a subset of these which are expended toward a one-shot, multi-factor designed experiment. Following this phase, the designer is left with the remaining set of unused units to implement any learnings from the experiments. With this setting, we answer the key design question of how much to experiment, which translates to choosing the number of replicates for a given design. We construct a Bayesian framework that captures the expected cumulative gain across the entire set of units. We derive theoretical results for the optimal number of replicates for all two-level, full and fractional factorial designs with seven factors or fewer. We conduct simulations that serve as validation of the theoretical results, as well as enabling us to explore scenarios and techniques of analysis that are not captured in the theoretical studies. Our overall results indicate that the optimal allocation of units for experimentation varies from 1 to 20 % of the total units available, which is mainly governed by the experimental environment and the total number of units. We conclude that experimenting with the optimal number of replicates recommended by our study can lead to a cumulative improvement which is 80–95% greater than the expected cumulative improvement gained when a practitioner chooses the number of replicates randomly. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Research in Engineering Design

Optimal replicates for designed experiments under the online framework

The use of association rule mining techniques in diverse contexts and domains has resulted in the creation of numerous interestingness measures. This, in turn, has motivated researchers to come up with various classification schemes for these measures. One popular approach to classify the objective measures is to assess the set of mathematical properties they satisfy in order to help practitioners select the right measure for a given problem. In this research, we discuss the insufficiency of the existing properties in the literature to capture certain behaviors of interestingness measures. This motivates us to adopt an approach where a measure is described by how it varies if there is a unit change in the frequency count (f 11 , f 10 , f 01 , f 00 ) , at different preexisting states of the counts. This rate of change analysis is formally defined as the first partial derivative of the measure with respect to the various frequency counts. We use this analysis to define two novel properties, unit-null asymptotic invariance (UNAI) and unit-null zero rate (UNZR). UNAI looks at the asymptotic effect of adding frequency patterns, while UNZR looks at the initial effect of adding frequency patterns when they do not preexist in the dataset. We present a comprehensive analysis of 50 interestingness measures and classify them in accordance with the two properties. We also present multiple empirical studies, involving both synthetic and real-world datasets, which are used to cluster various measures according to the rule ranking patterns of the measures. The study concludes with the observation that classification of measures using the empirical clusters shares significant similarities to the classification of measures done through the properties presented in this research. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Fulltext

Knowledge and Information Systems

Rate of change analysis for interestingness measures

This research studies the use of predetermined experimental plans in a live setting with a finite implementation horizon. In this context, we seek to determine the optimal experimental budget in different environments using a Bayesian framework. We derive theoretical results on the optimal allocation of resources to treatments with the objective of minimizing cumulative regret, a metric commonly used in online statistical learning. Our base case studies a setting with two treatments assuming Gaussian priors for the treatment means and noise distributions. We extend our study through analytical and semi-analytical techniques which explore worst-case bounds and the generalization to k treatments. We determine theoretical limits for the experimental budget across all possible scenarios. The optimal level of experimentation that is recommended by this study varies extensively and depends on the experimental environment as well as the number of available units. This highlights the importance of such an approach which incorporates these factors to determine the budget. Copyright © 2019 ASME.

Proceedings of the ASME Design Engineering Technical Conference

Conducting non-adaptive experiments in a live setting: A Bayesian approach to determining optimal sample size

We propose a novel variant of the UCB algorithm (referred to as Efficient-UCB-Variance (EUCBV)) for minimizing cumulative regret in the stochastic multi-armed bandit (MAB) setting. EUCBV incorporates the arm elimination strategy proposed in UCB-Improved (Auer and Ortner 2010), while taking into account the variance estimates to compute the arms' confidence bounds, similar to UCBV (Audibert, Munos, and Szepesvári 2009). Through a theoretical analysis we establish that EUCBV incurs a gap-dependent regret bound of O Kσ max 2 log( T Δ2/K) after T trials, where Δ is the min-Δ imal gap between optimal and sub-optimal arms; the above bound is an improvement over that of existing state-of-the-art UCB algorithms (such as UCB1, UCB-Improved, UCBV, MOSS). Further, EUCBV incurs a gap-independent regret bound of O KT which is an improvement over that of UCB1, UCBV and UCB-Improved, while being comparable with that of MOSS and OCUCB. Through an extensive numerical study we show that EUCBV significantly outperforms the popular UCB variants (like MOSS, OCUCB, etc.) as well as Thompson sampling and Bayes-UCB algorithms. Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Department of Metallurgical and Materials Engineering

32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Efficient-UCBV: An almost optimal algorithm using variance estimates

In this study, we seek to automate the categorization of attributes from online reviews based on the Kano model. We present a framework which relates customer feedback at an attribute specific level with overall feedback at the product or service level and therefore infers the nature of the attribute’s influence on customer experience. We validate this framework on a large-scale data set of a popular portal that covers restaurant ratings globally. Our data covers approximately 65,000 reviews across ten cities, ten different cuisines, and four attributes. Our analysis results in various location-cuisine specific insights regarding the attributes. At a high-level, our analyses show that the food quality as an attribute tends to behave like a necessity driver when the cuisine is native, and as a luxury driver when it is non-native. We present three case studies which illustrate that managerial insights at location-cuisine specific level can be inferred on each of the aspects, and would, therefore, allow for a restaurant-specific prioritization of the aspects. © 2018, Curran Associates Inc. All rights reserved.

Proceedings of International Conference on Computers and Industrial Engineering, CIE

Automated kano model categorization of aspects from online ratings

There is a growing interest in understanding, as opposed to predicting, the repayment behavior of customers of financial institutions that provide loans. This study proposes a modified Hidden Markov Model (HMM) based clustering, which clusters repayment sequences across selected subsets of the HMM parameters. We demonstrate that different implementations of this modified adaptation helps us gain an in-depth understanding of various drivers that are hard to directly observe but nevertheless govern repayment. These include drivers such as the ability to repay (financial health of the customer) or the intention to repay independent of the ability (willful defaulting and unintentional delinquency). Algorithmically, we achieve this partially constrained HMM clustering (PC-HMM) by placing constraints on the expectation-maximization (EM) algorithm where a subset of parameters are used to cluster the repayments via the estimation process, while the other parameters are learned globally across all repayments. We compare our approach with three other baselines on a real-world loan repayment data set. We use an exogenous variable to validate and benchmark the clusters. We conclude our study with the observation that the ability to cluster on selective parameters, in conjunction with the structural construct of HMMs, enables the discovery of substantially more meaningful business insights than the baselines. © 2018 Association for Computing Machinery. All rights reserved.

Data Base for Advances in Information Systems

Improved insights on financial health through partially constrained hidden Markov model clustering on loan repayment data

A designed experiment is typically followed by a statistical analysis of the results, using which the preferred settings of the inputs are selected for operation. In this paper, we motivate real-world scenarios, where it could be advantageous to succeed the experiment with continued exploration upon deployment in the online context. We propose the use of Linear Bandits to conduct sequential experiments in the online setting. The linear bandit algorithms, which utilize results from the designed experiment as an initial seed, are then used to select a treatment combination in each step or trial. Specifically, the study analyzes two linear bandit algorithms and compares them to three different baselines. The two linear bandit algorithms are OFUL, which is shown in literature to have one of the best theoretical performances, and LGUCBand, a novel contribution of this research, which uses the statistical concept of upper confidence bands for linear models. The baselines are different designs and data analyses without any form of online experimentation. The results are compared using simulations of a model built on meta-data from published experiments on real engineering applications. An analytical derivation of the default baselines is also an important contribution of this research and is intended to provide theoretical validation of the simulation results. The results indicate that, across different environments, substantial long-term improvements can be made by following designed experiments with linear bandits, with minimal short term costs. © 2017 Elsevier Ltd

Computers and Industrial Engineering

Using Linear Stochastic Bandits to extend traditional offline Designed Experiments to online settings

In this paper we propose the Augmented-UCB (AugUCB) algorithm for a fixed-budget version of the thresholding bandit problem (TBP), where the objective is to identify a set of arms whose quality is above a threshold. A key feature of AugUCB is that it uses both mean and variance estimates to eliminate arms that have been sufficiently explored; to the best of our knowledge this is the first algorithm to employ such an approach for the considered TBP. Theoretically, we obtain an upper bound on the loss (probability of mis-classification) incurred by AugUCB. Although UCBEV in literature provides a better guarantee, it is important to emphasize that UCBEV has access to problem complexity (whose computation requires arms' mean and variances), and hence is not realistic in practice; this is in contrast to AugUCB whose implementation does not require any such complexity inputs. We conduct extensive simulation experiments to validate the performance of AugUCB. Through our simulation work, we establish that AugUCB, owing to its utilization of variance estimates, performs significantly better than the state-of-the-art APT, CSAR and other non variance-based algorithms.

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

Thresholding Bandits with Augmented UCB

Ensemble Methods are proposed as a means to extendbiAdaptive One-Factor-at-a-Time (aOFAT) experimentation. The proposed method executes multiple aOFAT experiments on the same system with minor differences in experimental setup, such as ‘starting points’. Experimental conclusions are arrived at by aggregating the multiple, individual aOFATs. A comparison is made to test the performance of the new method with that of a traditional form of experimentation, namely a single fractional factorial design which is equally resource intensive. The comparisons between the two experimental algorithms are conducted using a hierarchical probability meta-model and an illustrative case study. The case is a wet clutch system with the goal of minimizing drag torque. In this study, the proposed procedure was superior in performance to using fractional factorial arrays consistently across various experimental settings. At the best, the proposed algorithm provides an expected value of improvement that is 15% higher than the traditional approach; at the worst, the two methods are equally effective, and on average the improvement is about 10% higher with the new method. These findings suggest that running multiple adaptive experiments in parallel can be an effective way to make improvements in quality and performance of engineering systems and also provides a reasonable aggregation procedure by which to bring together the results of the many separate experiments. Copyright © 2011 John Wiley &amp; Sons, Ltd.&nbsp;

Quality and Reliability Engineering International

Using ensemble techniques to advance adaptive one-factor-at-a-time experimentation

High performance analog (HPA) devices in high-k metal gate (HKMG) scheme with innovative halo engineering have been successfully demonstrated to produce superior analog and digital performance for low power applications. HPA device was processed "freely" with no extra mask, no extra litho, and no extra process step. This paper details a comprehensive study of the analog and digital characteristics of these HPA devices in comparison with analog control (conventional digital devices with matched geometry). Analog properties such as output voltage gain (also called self-gain), trans-conductance Gm, conductance Gds, Gm/Id, mismatching (MM) behavior, flicker noise (1=f noise) and current linearity have clearly reflected the advantage of HPA devices over analog control, while DC performance (e.g., Ion-Ioff, Ioff-Vtsat , DIBL, C jswg) and reliability (HCI) have also shown the comparability of HPA devices over control. © 2011 The Japan Society of Applied Physics.

Department of Electrical Engineering

Japanese Journal of Applied Physics

Novel high-performance analog devices for advanced low-power high-k metal gate complementary metal-oxide-semiconductor technology

This paper presents a conceptually simple and resource efficient method for robust parameter design. The method varies control factors according to an adaptive one-factor-at-a-time plan while varying noise factors using a two-level resolution III fractional factorial array. The method is assessed and compared against a crossed array method which uses fractional factorial inner arrays. The comparison is made by looking at the improvements in robustness across four case studies. The proposed method improves system robustness to more than 80% of the potentially attainable improvement on average. This figure improves to over 90% if prior knowledge of the system is used to define a promising starting point for the search. The results vary across the case studies, but, in general, both the average amount of improvement and the consistency of the results are better than those provided by a crossed array approach as long as experimental error is low enough. This is despite the fact that the proposed method typically uses fewer, and at most equal, number of experiments as the crossed array method. The case studies reveal that the proposed method provides these benefits by exploiting both control by noise interactions, and higher order effects such as control by control by noise interactions, with probability substantially better than random chance. Copyright © 2006 by ASME.

An adaptive one-factor-at-a-time method for robust parameter design: Comparison with crossed arrays via case studies

This article documents a meta-analysis of 113 data sets from published factorial experiments. The study quantifies regularities observed among factor effects and multifactor interactions. Such regularities are known to be critical to efficient planning and analysis of experiments and to robust design of engineering systems. Three previously observed properties are analyzed: effect sparsity, hierarchy, and heredity. A new regularity is introduced and shown to be statistically significant. It is shown that a preponderance of active two-factor interaction effects are synergistic, meaning that when main effects are used to increase the system response, the interaction provides an additional increase and that when main effects are used to decrease the response, the interactions generally counteract the main effects. © 2006 Wiley Periodicals, Inc.&nbsp;