Lily Xu*, Shahrzad Gholami *, Sara Mc Carthy, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Rohit Singh, Mustapha Nsubuga, Joshua Mabonga, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Tom Okello, and Eric Enyel. 4/20/2020. “Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations.” In IEEE International Conference on Data Engineering (ICDE-20).Abstract
Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an end-to-end approach to the data-to-deployment pipeline for anti-poaching. In doing so, we address challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS, and we apply our methodology to three national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to improve robustness of our prescribed patrols and increase detection of snares by an average of 30%. We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 800 parks around the world through integration with SMART conservation software. 
Aditya Mate, Jackson A. Killian, Bryan Wilder, Marie Charpignon, Ananya Awasthi, Milind Tambe, and Maimuna S. Majumder. 4/13/2020. “Evaluating COVID-19 Lockdown Policies For India: A Preliminary Modeling Assessment for Individual States.” SSRN. Publisher's VersionAbstract
Background: On March 24, India ordered a 3-week nationwide lockdown in an effort to control the spread of COVID-19. While the lockdown has been effective, our model suggests that completely ending the lockdown after three weeks could have considerable adverse public health ramifications. We extend our individual-level model for COVID-19 transmission [1] to study the disease dynamics in India at the state level for Maharashtra and Uttar Pradesh to estimate the effect of further lockdown policies in each region. Specifically, we test policies which alternate between total lockdown and simple physical distancing to find "middle ground" policies that can provide social and economic relief as well as salutary population-level health effects.

Methods: We use an agent-based SEIR model that uses population-specific age distribution, household structure, contact patterns, and comorbidity rates to perform tailored simulations for each region. The model is first calibrated to each region using publicly available COVID-19 death data, then implemented to simulate a range of policies. We also compute the basic reproduction number R0 and case documentation rate for both regions.

Results: After the initial lockdown, our simulations demonstrate that even policies that enforce strict physical distancing while returning to normal activity could lead to widespread outbreaks in both states. However, "middle ground" policies that alternate weekly between total lockdown and physical distancing may lead to much lower rates of infection while simultaneously permitting some return to normalcy.
Aida Rahmattalabi *, Shahin Jabbari *, Phebe Vayanos, Himabindu Lakkaraju, and Milind Tambe. 2/2/2020. “Fairness in Time-Critical Influence Maximizationwith Applications to Public Health Preventative Interventions.” In AAAI Health Intelligence Workshop. health_intelligence_workshop_aaai2019.pdf
Edward A. Cranford, Palvi Aggarwal, Cleotilde Gonzalez, Sarah Cooney, Milind Tambe, and Christian Lebiere. 2020. “Adaptive Cyber Deception: Cognitively Informed Signaling for Cyber Defense.” In 53rd Hawaii International Conference on System Sciences, Pp. 1885-1894. 0187.pdf
Andrew Perrault, Fei Fang, Arunesh Sinha, and Milind Tambe. 2020. “AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline.” AI Magazine. ai_magazine_article.pdf
Kai Wang. 2020. “Balance Between Scalability and Optimality in Network Security Games.” In Doctoral Consortium at International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).Abstract
Network security games (NSGs) are widely used in security related domain to model the interaction between the attacker and the defender. However, due to the complex graph structure of the entire network, finding a Nash equilibrium even when the attacker is fully rational is not well-studied yet. There is no efficient algorithms known with valid guarantees. We identify two major issues of NSGs: i) non-linearity ii) correlation between edges. NSGs with non-linear objective function are usually hard to optimize, while correlated edges might create exponentially many strategies and impact the scalability. In this paper, we analyze the distortion of linear and non-linear formulations of NSGs with fully rational attacker. We provide theoretical bounds on these different formulations, which can quantify the approximation ratio between linear and non-linear assumption. This result can help us understand how much loss will the linearization incur in exchange for the scalability.
Elizabeth Bondi, Raghav Jain, Palash Aggrawal, Saket Anand, Robert Hannaford, Ashish Kapoor, Jim Piavis, Shital Shah, Lucas Joppa, Bistra Dilkina, and Milind Tambe. 2020. “BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos.” In WACV. 2020_07_teamcore_wacv_birdsai.pdf
Shahin Jabbari, Han-Ching Ou, Himabindu Lakkaraju, and Milind Tambe. 2020. “An Empirical Study of the Trade-Offs Between Interpretability and Fairness.” In ICML 2020 Workshop on Human Interpretability in Machine Learning, preliminary version.Abstract
As machine learning models are increasingly being deployed in critical domains such as criminal justice and healthcare, there has been a growing interest in developing algorithms that are interpretable and fair. While there has been a lot of research on each of these topics in isolation, there has been little work on their intersection. In this paper, we present an empirical study for understanding the relationship between model interpretability and fairness. To this end, we propose a novel evaluation framework and outline appropriate evaluation metrics to determine this relationship across various classes of models in both synthetic and real world datasets.
Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, and Milind Tambe. 2020. “End-to-End Game-Focused Learning of Adversary Behavior in Security Games.” AAAI Conference on Artificial Intelligence. New York. 1903.00958.pdf
Ilkin Bayramli, Elizabeth Bondi, and Milind Tambe. 2020. “In the Shadow of Disaster: Finding Shadows to Improve Damage Detection.” Harvard CRCS Workshop on AI for Social Good. 2020_07_teamcore_harvard_crcs_shadow_disaster_detection_2020.pdf
Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, and Milind Tambe. 2020. “Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling.” In International Conference on Autonomous Agents and Multiagent Systems.Abstract
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art methods rely on hand-crafted sampling algorithms; these methods sample nodes and their neighbours in a carefully constructed order and choose opinion leaders from this discovered network to maximize influence spread in the (unknown) complete network. In this work, we propose a reinforcement learning framework for network discovery that automatically learns useful node and graph representations that encode important structural properties of the network. At training time, the method identifies portions of the network such that the nodes selected from this sampled subgraph can effectively influence nodes in the complete network. The realization of such transferable network structure based adaptable policies is attributed to the meticulous design of the framework that encodes relevant node and graph signatures driven by an appropriate reward scheme. We experiment with real-world social networks from four different domains and show that the policies learned by our RL agent provide a 10-36% improvement over the current state-of-the-art method.
Elizabeth Bondi, Andrew Perrault, Fei Fang, Benjamin L. Rice, Christopher D. Golden, and Milind Tambe. 2020. “Mapping for Public Health: Initial Plan for Using Satellite Imagery for Micronutrient Deficiency Prediction.” KDD 2020 Workshop on Humanitarian Mapping. kdd_humanitarian_mapping_workshop_2020_4.pdf
Aaron Ferber, Bryan Wilder, Bistra Dilkina, and Milind Tambe. 2020. “MIPaaL: Mixed Integer Program as a Layer.” In AAAI Conference on Artificial Intelligence.Abstract
Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures average accuracy between predicted values and ground truth values. Decision-focused learning explicitly integrates the downstream decision problem when training the predictive model, in order to optimize the quality of decisions induced by the predictions. It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization. However, these previous applications have uniformly focused on problems with simple constraints. Here, we enable decision-focused learning for the broad class of problems that can be encoded as a mixed integer linear program (MIP), hence supporting arbitrary linear constraints over discrete and continuous variables. We show how to differentiate through a MIP by employing a cutting planes solution approach, an algorithm that iteratively tightens the continuous relaxation by adding constraints removing fractional solutions. We evaluate our new end-to-end approach on several real world domains and show that it outperforms the standard two phase approaches that treat prediction and optimization separately, as well as a baseline approach of simply applying decision-focused learning to the LP relaxation of the MIP. Lastly, we demonstrate generalization performance in several transfer learning tasks.
Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel Kochenderfer, Milind Tambe, and Yevgeniy Vorobeychik. 2020. “Robust Spatial-Temporal Incident Prediction.” Conference on Uncertainty in Artificial Intelligence (UAI). Toronto. uai_1.pdf
Kai Wang, Andrew Perrault, Aditya Mate, and Milind Tambe. 2020. “Scalable Game-Focused Learning of Adversary Models: Data-to-Decisions in Network Security Games.” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).Abstract
Previous approaches to adversary modeling in network security games (NSGs) have been caught in the paradigm of first building a full adversary model, either from expert input or historical attack data, and then solving the game. Motivated by the need to disrupt the multibillion dollar illegal smuggling networks, such as wildlife and drug trafficking, this paper introduces a fundamental shift in learning adversary behavior in NSGs by focusing on the accuracy of the model using the downstream game that will be solved. Further, the paper addresses technical challenges in building such a game-focused learning model by i) applying graph convolutional networks to NSGs to achieve tractability and differentiability and ii) using randomized block updates of the coefficients of the defender's optimization in order to scale the approach to large networks. We show that our game-focused approach yields scalability and higher defender expected utility than models trained for accuracy only.
Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, and Milind Tambe. 2020. “Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning.” AAAI Conference on Artificial Intelligence. New York.
Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, and Milind Tambe. 2020. “To Signal or Not To Signal: Exploiting Uncertain Real-Time Information in Signaling Games for Security and Sustainability.” In AAAI conference on Artificial Intelligence. 2020_02_teamcore_aaai_signaluncertainty.pdf
Elizabeth Bondi. 2020. “Vision for Decisions: Utilizing Uncertain Real-Time Information and Signaling for Conservation.” In AAMAS Doctoral Consortium. 2020_12_teamcore_aamas_doctoral_consortium.pdf
Edward A. Cranford, Cleotilde Gonzalez, Palvi Aggarwal, Milind Tambe, and Christian Lebiere. 2020. “What Attackers Know and What They Have to Lose: Framing Effects on Cyber-attacker Decision Making.” In 64th Human Factors and Ergonomics Society (HFES) Annual Conference. cranford_hfes2020_final_20200602.pdf
Jackson Killian, Bryan Wilder, Amit Sharma, Vinod Choudhary, Bistra Dilkina, and Milind Tambe. 8/4/2019. “Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data.” In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 8/4/2019. Abstract
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.
We analyze data from one city served by 99DOTS, a phone-callbased DAT deployed for Tuberculosis (TB) treatment in India where
nearly 3 million people are afflicted with the disease each year. The
data contains nearly 17,000 patients and 2.1M dose records. We lay
the groundwork for learning from this real-world data, including
a method for avoiding the effects of unobserved interventions in
training data used for machine learning. We then construct a deep
learning model, demonstrate its interpretability, and show how it
can be adapted and trained in three different clinical scenarios to
better target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with
21% more patients and before 76% more missed doses than current
heuristic baselines. For outcome prediction, our model performs
40% better than baseline methods, allowing cities to target more
resources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our model
can be trained in an end-to-end decision focused learning setting to
achieve 15% better solution quality in an example decision problem
faced by health workers.