Amulya Yadav

AAAI Conference 2023 Conference Paper

A Continual Pre-training Approach to Tele-Triaging Pregnant Women in Kenya

• Wenbo Zhang
• Hangzhi Guo
• Prerna Ranganathan
• Jay Patel
• Sathyanath Rajasekharan
• Nidhi Danayak
• Manan Gupta
• Amulya Yadav

Access to high-quality maternal health care services is limited in Kenya, which resulted in ∼36,000 maternal and neonatal deaths in 2018. To tackle this challenge, Jacaranda Health (a non-profit organization working on maternal health in Kenya) developed PROMPTS, an SMS based tele-triage system for pregnant and puerperal women, which has more than 350,000 active users in Kenya. PROMPTS empowers pregnant women living far away from doctors and hospitals to send SMS messages to get quick answers (through human helpdesk agents) to questions about their medical symptoms and pregnancy status. Unfortunately, ∼1.1 million SMS messages are received by PROMPTS every month, which makes it challenging for helpdesk agents to ensure that these messages can be interpreted correctly and evaluated by their level of emergency to ensure timely responses and/or treatments for women in need. This paper reports on a collaborative effort with Jacaranda Health to develop a state-of-the-art natural language processing (NLP) framework, TRIM-AI (TRIage for Mothers using AI), which can automatically predict the emergency level (or severity of medical condition) of a pregnant mother based on the content of their SMS messages. TRIM-AI leverages recent advances in multi-lingual pre-training and continual pre-training to tackle code-mixed SMS messages (between English and Swahili), and achieves a weighted F1 score of 0.774 on real-world datasets. TRIM-AI has been successfully deployed in the field since June 2022, and is being used by Jacaranda Health to prioritize the provision of services and care to pregnant women with the most critical medical conditions. Our preliminary A/B tests in the field show that TRIM-AI is ∼17% more accurate at predicting high-risk medical conditions from SMS messages sent by pregnant Kenyan mothers, which reduces the helpdesk’s workload by ∼12%.

IJCAI Conference 2022 Conference Paper

Forecasting the Number of Tenants At-Risk of Formal Eviction: A Machine Learning Approach to Inform Public Policy

• Maryam Tabar
• Wooyong Jung
• Amulya Yadav
• Owen Wilson Chavez
• Ashley Flores
• Dongwon Lee

Eviction of tenants has reached a crisis level in the U. S. and its consequences pose significant challenges to society. To tackle this eviction crisis, policymakers have been allocating financial resources but a more efficient resource allocation would need an accurate forecast of the number of tenants at-risk of evictions ahead of time. To help enhance the existing eviction prevention/diversion programs, in this work, we propose a multi-view deep neural network model, named as MARTIAN, that forecasts the number of tenants at-risk of getting formally evicted (at the census tract level) n months into the future. Then, we evaluate MARTIAN’s predictive performance under various conditions using real-world eviction cases filed across Dallas County, TX. The results of empirical evaluation show that MARTIAN outperforms an extensive set of baseline models in terms of predictive performance. Additionally, MARTIAN’s superior predictive performance is generalizable to unseen census tracts, for which no labeled data is available in the training set. This research has been done in collaboration with Child Poverty Action Lab (CPAL), which is a pioneering non-governmental organization (NGO) working for tackling poverty-related issues across Dallas County, TX. The usability of MARTIAN is under review by subject matter experts. We release our codebase at https: //github. com/maryam-tabar/MARTIAN.

AAAI Conference 2021 Conference Paper

Clinical Trial of an AI-Augmented Intervention for HIV Prevention in Youth Experiencing Homelessness

• Bryan Wilder
• Laura Onasch-Vera
• Graham Diguiseppi
• Robin Petering
• Chyna Hill
• Amulya Yadav
• Eric Rice
• Milind Tambe

Youth experiencing homelessness (YEH) are subject to substantially greater risk of HIV infection, compounded both by their lack of access to stable housing and the disproportionate representation of youth of marginalized racial, ethnic, and gender identity groups among YEH. A key goal for health equity is to improve adoption of protective behaviors in this population. One promising strategy for intervention is to recruit peer leaders from the population of YEH to promote behaviors such as condom usage and regular HIV testing to their social contacts. This raises a computational question: which youth should be selected as peer leaders to maximize the overall impact of the intervention? We developed an artificial intelligence system to optimize such social network interventions in a community health setting. We conducted a clinical trial enrolling 713 YEH at drop-in centers in a large US city. The clinical trial compared interventions planned with the algorithm to those where the highest-degree nodes in the youths’ social network were recruited as peer leaders (the standard method in public health) and to an observation-only control group. Results from the clinical trial show that youth in the AI group experience statistically significant reductions in key risk behaviors for HIV transmission, while those in the other groups do not. This provides, to our knowledge, the first empirical validation of the usage of AI methods to optimize social network interventions for health. We conclude by discussing lessons learned over the course of the project which may inform future attempts to use AI in community-level interventions.

AAAI Conference 2021 Conference Paper

Harnessing Social Media to Identify Homeless Youth At-Risk of Substance Use

• Zi-Yi Dou
• Anamika Barman-Adhikari
• Fei Fang
• Amulya Yadav

Homeless youth are a highly vulnerable population and report highly elevated rates of substance use. Prior work on mitigating substance use among homeless youth has primarily relied on survey data to get information about substance use among homeless youth, which can then be used to inform the design of targeted intervention programs. However, such survey data is often onerous to collect, is limited by its reliance on selfreports and retrospective recall, and quickly becomes dated. The advent of social media has provided us with an important data source for understanding the health behaviors of homeless youth. In this paper, we target this specific population and demonstrate how to detect substance use based on texts from social media. We collect 135K Facebook posts and comments together with survey responses from a group of homeless youth and use this data to build novel substance use detection systems with machine learning and natural language processing techniques. Experimental results show that our proposed methods achieve ROC-AUC scores of 0. 77 on identifying certain kinds of substance use among homeless youth using Facebook conversations only, and ROC-AUC scores of 0. 83 when combined with answers to four survey questions that are not about their demographic characteristics or substance use. Furthermore, we investigate connections between the characteristics of people’s Facebook posts and substance use and provide insights about the problem.

AAMAS Conference 2021 Conference Paper

Let the DOCTOR Decide Whom to Test: Adaptive Testing Strategies to Tackle the COVID-19 Pandemic

• Yu Liang
• Amulya Yadav

A robust testing program is necessary for containing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in lowresource developing countries), designing an optimal testing program/strategy is a challenging problem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symptomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing program’s effectiveness. In this paper, we address these limitations by making five novel contributions. (i) We formally define the optimal testing problem and propose the DOCTOR POMDP model to tackle it. (ii) We solve the DOCTOR POMDP using a scalable Monte Carlo tree search based algorithm. (iii) We provide a rigorous experimental analysis of DOCTOR’s testing strategies against static baselines - our results show that when applied to the city of Santiago in Panama, DOCTOR’s strategies result in ∼40% fewer COVID-19 infections (over one month) as compared to state-of-theart static baselines. (iv) In addition, we analyze DOCTOR’s testing policy to derive insights about the reasons behind the optimality of DOCTOR’s testing policy. (v) Finally, we characterize conditions (of the real world) under which DOCTOR’s optimization would be of most benefit to government policy makers, and thus requires significant attention from researchers in this area. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19.

ECAI Conference 2020 Conference Paper

Decoding the Imitation Security Game: Handling Attacker Imitative Behavior Deception

• Thanh Hong Nguyen
• Nam Vu
• Amulya Yadav
• Uy Nguyen

Real-world security problems are generally characterized by uncertainty about attackers’ preferences, behavior, or other characteristics. To handle such uncertainties, security agencies (defender) typically rely on historical attack data to build a behavior model of the attacker, and incorporate this model into generating an effective defense strategy. For example, in wildlife protection, rangers can collect poaching signs (e. g. , snares) to learn the behavior of poachers. However, in the real-world, a clever attacker can manipulate its attacks to fool the learning algorithm of the defender towards its own benefit. Unfortunately, existing state-of-the-art algorithms for generating defense strategies are not equipped to handle such deceptive behavior by the attacker, and this could lead to arbitrary losses for the defender. To address these challenges, this paper investigates a basic deception strategy of the attacker, termed imitative behavior deception, in which the attacker intentionally pretends to follow a specific behavior model and consistently plays according to that model, in order to optimize its utility. We have three main contributions. First, built upon previous work on attacker-behavior modeling, we introduce new algorithms to compute an optimal imitative behavior deception strategy of the attacker. Second, we propose a novel game-theoretic counter-deception algorithm which determines effective defense strategies, taking into account the deceptive behavior of the attacker. Third, we conduct extensive experiments, which shows that under the attacker’s deception, the defender accrues a significant loss whereas the attacker achieves a significant gain in utility. Our experimental results also demonstrate the impact of our counter-deception algorithm on substantially diminishing the attacker’s deception.

IJCAI Conference 2020 Conference Paper

Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth

• Amulya Yadav
• Roopali Singh
• Nikolas Siapoutis
• Anamika Barman-Adhikari
• Yu Liang

This paper presents CORTA, a software agent that designs personalized rehabilitation programs for homeless youth suffering from opioid addiction. Many rehabilitation centers treat opioid addiction in homeless youth by prescribing rehabilitation programs that are tailored to the underlying causes of addiction. To date, rehabilitation centers have relied on ad-hoc assessments and unprincipled heuristics to deliver rehabilitation programs to homeless youth suffering from opioid addiction, which greatly undermines the effectiveness of the delivered programs. CORTA addresses these challenges via three novel contributions. First, CORTA utilizes a first-of-its-kind real-world dataset collected from ~1400 homeless youth to build causal inference models which predict the likelihood of opioid addiction among these youth. Second, utilizing counterfactual predictions generated by our causal inference models, CORTA solves novel optimization formulations to assign appropriate rehabilitation programs to the correct set of homeless youth in order to minimize the expected number of homeless youth suffering from opioid addiction. Third, we provide a rigorous experimental analysis of CORTA along different dimensions, e. g. , importance of causal modeling, importance of optimization, and impact of incorporating fairness considerations, etc. Our simulation results show that CORTA outperforms baselines by ~110% in minimizing the number of homeless youth suffering from opioid addiction.

AAMAS Conference 2019 Conference Paper

Don't Put All Your Strategies in One Basket: Playing Green Security Games with Imperfect Prior Knowledge

• Shahrzad Gholami
• Amulya Yadav
• Long Tran-Thanh
• Bistra Dilkina
• Milind Tambe

Security efforts for wildlife monitoring and protection of endangered species (e. g. , elephants, rhinos, etc.) are constrained by limited resources available to law enforcement agencies. Recent progress in Green Security Games (GSGs) has led to patrol planning algorithms for strategic allocation of limited patrollers to deter adversaries in environmental settings. Unfortunately, previous approaches to these problems suffer from several limitations. Most notably, (i) previous work in GSG literature relies on exploitation of error-prone machine learning (ML) models of poachers’ behavior trained on (spatially) biased historical data; and (ii) online learning approaches for repeated security games (similar to GSGs) do not account for spatio-temporal scheduling constraints while planning patrols, potentially causing significant shortcomings in the effectiveness of the planned patrols. Thus, this paper makes the following novel contributions: (I) We propose MINION-sm, a novel online learning algorithm for GSGs which does not rely on any prior error-prone model of attacker behavior, instead, it builds an implicit model of the attacker on-the-fly while simultaneously generating schedulingconstraint-aware patrols. MINION-sm achieves a sublinear regret against an optimal hindsight patrol strategy. (II) We also propose MINION, a hybrid approach where our MINION-sm model and an ML model (based on historical data) are considered as two patrol planning experts and we obtain a balance between them based on their observed empirical performance. (III) We show that our online learning algorithms significantly outperform existing state-of-theart solvers for GSGs.

NeurIPS Conference 2019 Conference Paper

Exploring Algorithmic Fairness in Robust Graph Covering Problems

• Aida Rahmattalabi
• Phebe Vayanos
• Anthony Fulginiti
• Eric Rice
• Bryan Wilder
• Amulya Yadav
• Milind Tambe

Fueled by algorithmic advances, AI algorithms are increasingly being deployed in settings subject to unanticipated challenges with complex social effects. Motivated by real-world deployment of AI driven, social-network based suicide prevention and landslide risk management interventions, this paper focuses on a robust graph covering problem subject to group fairness constraints. We show that, in the absence of fairness constraints, state-of-the-art algorithms for the robust graph covering problem result in biased node coverage: they tend to discriminate individuals (nodes) based on membership in traditionally marginalized groups. To remediate this issue, we propose a novel formulation of the robust covering problem with fairness constraints and a tractable approximation scheme applicable to real world instances. We provide a formal analysis of the price of group fairness (PoF) for this problem, where we show that uncertainty can lead to greater PoF. We demonstrate the effectiveness of our approach on several real-world social networks. Our method yields competitive node coverage while significantly improving group fairness relative to state-of-the-art methods.

AAMAS Conference 2018 Conference Paper

Activating the "Breakfast Club": Modeling Influence Spread in Natural-World Social Networks

• Lily Hu
• Bryan Wilder
• Amulya Yadav
• Eric Rice
• Milind Tambe

While reigning models of diffusion have privileged the structure of a given social network as the key to informational exchange, real human interactions do not appear to take place on a single graph of connections. Using data collected from a pilot study of the spread of HIV awareness in social networks of homeless youth, we show that health information did not diffuse in the field according to the processes outlined by dominant models. Since physical network diffusion scenarios often diverge from their more well-studied counterparts on digital networks, we propose an alternative Activation Jump Model (AJM) that describes information diffusion on physical networks from a multi-agent team perspective. Our model exhibits two main differentiating features from leading cascade and threshold models of influence spread: 1) The structural composition of a seed set team impacts each individual node’s influencing behavior, and 2) an influencing node may spread information to non-neighbors. We show that the AJM significantly outperforms existing models in its fit to the observed node-level influence data on the youth networks. We then prove theoretical results, showing that the AJM exhibits many well-behaved properties shared by dominant models. Our results suggest that the AJM presents a flexible and more accurate model of network diffusion that may better inform influence maximization in the field.

IJCAI Conference 2018 Conference Paper

Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth

• Amulya Yadav
• Bryan Wilder
• Eric Rice
• Robin Petering
• Jaih Craddock
• Amanda Yoshioka-Maxwell
• Mary Hemler
• Laura Onasch-Vera

This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key "seed" nodes in social networks, i. e. , homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ~160% in terms of information spread about HIV among homeless youth.

AAMAS Conference 2018 Conference Paper

Please be an Influencer? Contingency-Aware Influence Maximization

• Amulya Yadav
• Ritesh Noothigattu
• Eric Rice
• Laura Onasch-Vera
• Leandro Soriano Marcolino
• Milind Tambe

Most previous work on influence maximization in social networks assumes that the chosen influencers (or seed nodes) can be influenced with certainty (i. e. , with no contingencies). In this paper, we focus on using influence maximization in public health domains for assisting low-resource communities, where contingencies are common. It is very difficult in these domains to ensure that the seed nodes are influenced, as influencing them entails contacting/convincing them to attend training sessions, which may not always be possible. Unfortunately, previous state-of-the-art algorithms for influence maximization are unusable in this setting. This paper tackles this challenge via the following four contributions: (i) we propose the Contingency Aware Influence Maximization problem and analyze it theoretically; (ii) we cast this problem as a Partially Observable Markov Decision Process and propose CAIMS (a novel POMDP planner) to solve it, which leverages a natural action space factorization associated with real-world social networks; and (iii) we provide extensive simulation results to compare CAIMS with existing state-of-the-art influence maximization algorithms. Finally, (iv) we provide results from a real-world feasibility trial conducted to evaluate CAIMS, in which key influencers in homeless youth social networks were influenced in order to spread awareness about HIV.

AAMAS Conference 2017 Conference Paper

Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application

• Amulya Yadav
• Bryan Wilder
• Eric Rice
• Robin Petering
• Jaih Craddock
• Amanda Yoshioka-Maxwell
• Mary Hemler
• Laura Onasch-Vera

IJCAI Conference 2017 Conference Paper

Maximizing Awareness about HIV in Social Networks of Homeless Youth with Limited Information

• Amulya Yadav
• Hau Chan
• Albert Xin Jiang
• Haifeng Xu
• Eric Rice
• Milind Tambe

This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER's sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. While previous work presents influence maximizing techniques to choose intervention participants, they do not address two real-world issues: (i) they completely fail to scale up to real-world sizes; and (ii) they do not handle deviations in execution of intervention plans. HEALER handles these issues via two major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; and (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviation-tolerant manner. HEALER was deployed in the real world in Spring 2016 with considerable success.

AAMAS Conference 2017 Conference Paper

Uncharted but not Uninfluenced: Influence Maximization with an Uncertain Network

• Bryan Wilder
• Amulya Yadav
• Nicole Immorlica
• Eric Rice
• Milind Tambe

This paper focuses on new challenges in influence maximization inspired by non-profits’ use of social networks to effect behavioral change in their target populations. Influence maximization is a multiagent problem where the challenge is to select the most influential agents from a population connected by a social network. Specifically, our work is motivated by the problem of spreading messages about HIV prevention among homeless youth using their social network. We show how to compute solutions which are provably close to optimal when the parameters of the influence process are unknown. We then extend our algorithm to a dynamic setting where information about the network is revealed at each stage. Simulation experiments using real world networks collected by the homeless shelter show the advantages of our approach.

AAMAS Conference 2016 Conference Paper

HEALER: POMDP Planning for Scheduling Interventions Among Homeless Youth (Demonstration)

• Amulya Yadav
• Ece Kamar
• Barbara Grosz
• Milind Tambe

AAMAS Conference 2016 Conference Paper

Simultaneous Influencing and Mapping Social Networks (Extended Abstract)

• Leandro Soriano Marcolino
• Aravind Lakshminarayanan
• Amulya Yadav
• Milind Tambe

AAMAS Conference 2016 Conference Paper

Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty

• Amulya Yadav
• Hau Chan
• Albert Xin Jiang
• Haifeng Xu
• Eric Rice
• Milind Tambe

AAAI Conference 2014 Conference Paper

Regret-Based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty

• Thanh Nguyen
• Amulya Yadav
• Bo An
• Milind Tambe
• Craig Boutilier

Stackelberg security games (SSGs) have been deployed in a number of real-world domains. One key challenge in these applications is the assessment of attacker payoffs, which may not be perfectly known. Previous work has studied SSGs with uncertain payoffs modeled by interval uncertainty and provided maximin-based robust solutions. In contrast, in this work we propose the use of the less conservative minimax regret decision criterion for such payoff-uncertain SSGs and present the first algorithms for computing minimax regret for SSGs. We also address the challenge of preference elicitation, using minimax regret to develop the first elicitation strategies for SSGs. Experimental results validate the effectiveness of our approaches.

ECAI Conference 2014 Conference Paper

Unleashing Dec-MDPs in Security Games: Enabling Effective Defender Teamwork

• Eric Anyung Shieh
• Albert Xin Jiang
• Amulya Yadav
• Pradeep Varakantham
• Milind Tambe

Multiagent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multiagent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork research in security games. This paper is the first to remedy this situation by integrating the powerful teamwork mechanisms offered by Dec-MDPs into security games. We offer the following novel contributions in this paper: (i) New models of security games where a defender team's pure strategy is defined as a Dec-MDP policy for addressing coordination under uncertainty; (ii) New algorithms based on column generation that enable efficient generation of mixed strategies given this new model; (iii) Handling global events during defender execution for effective teamwork; (iv) Exploration of the robustness of randomized pure strategies. The paper opens the door to a potentially new area combining computational game theory and multiagent teamwork.