no code implementations • 11 Mar 2017 • Haifeng Xu, Milind Tambe, Shaddin Dughmi, Venil Loyd Noronha
To mitigate this issue, we propose to design entropy-maximizing defending strategies for spatio-temporal security games, which frequently suffer from CoC.
no code implementations • 19 Aug 2016 • Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Amulya Yadav, Milind Tambe
To pilot test an artificial intelligence (AI) algorithm that selects peer change agents (PCA) to disseminate HIV testing messaging in a population of homeless youth.
no code implementations • 30 Jan 2016 • Amulya Yadav, Hau Chan, Albert 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.
no code implementations • 30 Oct 2015 • Arunesh Sinha, Debarun Kar, Milind Tambe
We provide four main contributions: (1) a PAC model of learning adversary response functions in SSGs; (2) PAC-model analysis of the learning of key, existing bounded rationality models in SSGs; (3) an entirely new approach to adversary modeling based on a non-parametric class of response functions with PAC-model analysis and (4) identification of conditions under which computing the best defender strategy against the learned adversary behavior is indeed the optimal strategy.
no code implementations • 23 Apr 2015 • Haifeng Xu, Albert X. Jiang, Arunesh Sinha, Zinovi Rabinovich, Shaddin Dughmi, Milind Tambe
Our experiments confirm the necessity of handling information leakage and the advantage of our algorithms.
no code implementations • 14 Sep 2018 • Bryan Wilder, Bistra Dilkina, Milind Tambe
These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision.
no code implementations • NeurIPS 2014 • Albert Jiang, Leandro Soriano Marcolino, Ariel D. Procaccia, Tuomas Sandholm, Nisarg Shah, Milind Tambe
We investigate the power of voting among diverse, randomized software agents.
no code implementations • 5 Feb 2019 • Jackson A. Killian, Bryan Wilder, Amit Sharma, Daksha Shah, Vinod Choudhary, Bistra Dilkina, Milind Tambe
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.
no code implementations • 3 Mar 2019 • Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, Milind Tambe
Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary.
no code implementations • 12 Jul 2019 • Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe
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.
no code implementations • 20 Nov 2019 • Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Milind Tambe
To solve the online problem with a hard bound on risk, we formulate it as a Reinforcement Learning (RL) problem with constraints on the action space (hard bound on risk).
no code implementations • 16 Dec 2019 • Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe
With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems.
no code implementations • 14 Jun 2020 • Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe
Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter.
no code implementations • 13 Jun 2020 • Siddharth Nishtala, Harshavardhan Kamarthi, Divy Thakkar, Dhyanesh Narayanan, Anirudh Grama, Aparna Hegde, Ramesh Padmanabhan, Neha Madhiwalla, Suresh Chaudhary, Balaraman Ravindran, Milind Tambe
India accounts for 11% of maternal deaths globally where a woman dies in childbirth every fifteen minutes.
no code implementations • 5 Jul 2020 • Aditya Mate, Jackson A. Killian, Haifeng Xu, Andrew Perrault, Milind Tambe
(ii) We exploit the optimality of threshold policies to build fast algorithms for computing the Whittle index, including a closed-form.
no code implementations • 12 Sep 2020 • Bryan Wilder, Michael J. Mina, Milind Tambe
For example, case counts may be sparse when only a small fraction of infections are caught by a testing program.
no code implementations • 5 Nov 2020 • Ramesha Karunasena, Mohammad Sarparajul Ambiya, Arunesh Sinha, Ruchit Nagar, Saachi Dalal, Divy Thakkar, Dhyanesh Narayanan, Milind Tambe
In this work, we define and test a data collection diligence score.
no code implementations • 20 Nov 2020 • Rachel Guo, Lily Xu, Drew Cronin, Francis Okeke, Andrew Plumptre, Milind Tambe
To ensure under-resourced parks have access to meaningful poaching predictions, we introduce the use of publicly available remote sensing data to extract features for parks.
no code implementations • 7 Dec 2020 • Xinrun Wang, Tarun Nair, Haoyang Li, Yuh Sheng Reuben Wong, Nachiket Kelkar, Srinivas Vaidyanathan, Rajat Nayak, Bo An, Jagdish Krishnaswamy, Milind Tambe
Dams impact downstream river dynamics through flow regulation and disruption of upstream-downstream linkages.
no code implementations • 18 Dec 2020 • Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi, Keval Dholakia, Balaraman Ravindran, Milind Tambe
We therefore first propose a novel GSG model that combines defender allocation, patrolling, real-time drone notification to human patrollers, and drones sending warning signals to attackers.
Decision Making Multiagent Systems
no code implementations • 7 Jan 2021 • Han-Ching Ou, Haipeng Chen, Shahin Jabbari, Milind Tambe
However, given the limited number of health workers, only a small subset of the population can be visited in any given time period.
no code implementations • 8 Mar 2021 • Aditya Mate, Arpita Biswas, Christoph Siebenbrunner, Susobhan Ghosh, Milind Tambe
Our contributions are as follows: (1) We derive conditions under which our problem satisfies indexability, a precondition that guarantees the existence and asymptotic optimality of the Whittle Index solution for RMABs.
no code implementations • 7 Mar 2021 • Siddharth Nishtala, Lovish Madaan, Aditya Mate, Harshavardhan Kamarthi, Anirudh Grama, Divy Thakkar, Dhyanesh Narayanan, Suresh Chaudhary, Neha Madhiwalla, Ramesh Padmanabhan, Aparna Hegde, Pradeep Varakantham, Balaraman Ravindran, Milind Tambe
India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100, 000 live births.
no code implementations • 17 May 2021 • Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe
In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks.
no code implementations • NeurIPS 2021 • Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe
In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.
no code implementations • 4 Jul 2021 • Jackson A. Killian, Lily Xu, Arpita Biswas, Milind Tambe
Our approach uses a double oracle framework (oracles for \textit{agent} and \textit{nature}), which is often used for single-process robust planning but requires significant new techniques to accommodate the combinatorial nature of RMABs.
no code implementations • 7 Jul 2021 • Kai Wang, Bryan Wilder, Sze-chuan Suen, Bistra Dilkina, Milind Tambe
We introduce a novel decomposed GP regression to incorporate the subgroup decomposed feedback.
no code implementations • 16 Sep 2021 • Aditya Mate, Lovish Madaan, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hegde, Pradeep Varakantham, Milind Tambe
Our second major contribution is evaluation of our RMAB system in collaboration with an NGO, via a real-world service quality improvement study.
no code implementations • 22 Sep 2021 • Susobhan Ghosh, Pradeep Varakantham, Aniket Bhatkhande, Tamanna Ahmad, Anish Andheria, Wenjun Li, Aparna Taneja, Divy Thakkar, Milind Tambe
With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (animal and human) and livelihoods (economic).
no code implementations • NeurIPS 2021 • Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe
In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.
no code implementations • 28 Jan 2022 • Han-Ching Ou, Christoph Siebenbrunner, Jackson Killian, Meredith B Brooks, David Kempe, Yevgeniy Vorobeychik, Milind Tambe
Motivated by a broad class of mobile intervention problems, we propose and study restless multi-armed bandits (RMABs) with network effects.
no code implementations • 2 Feb 2022 • Kai Wang, Shresth Verma, Aditya Mate, Sanket Shah, Aparna Taneja, Neha Madhiwalla, Aparna Hegde, Milind Tambe
To address this shortcoming, we propose a novel approach for decision-focused learning in RMAB that directly trains the predictive model to maximize the Whittle index solution quality.
no code implementations • 28 Feb 2022 • James Holt, Edward Raff, Ahmad Ridley, Dennis Ross, Arunesh Sinha, Diane Staheli, William Streilen, Milind Tambe, Yevgeniy Vorobeychik, Allan Wollaber
These challenges are widely studied in enterprise networks, but there are many gaps in research and practice as well as novel problems in other domains.
no code implementations • 30 Mar 2022 • Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe
Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better on that specific task.
no code implementations • 28 Apr 2022 • Vineet Nair, Kritika Prakash, Michael Wilbur, Aparna Taneja, Corinne Namblard, Oyindamola Adeyemo, Abhishek Dubey, Abiodun Adereni, Milind Tambe, Ayan Mukhopadhyay
More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake.
no code implementations • 29 Apr 2022 • Adam Żychowski, Jacek Mańdziuk, Elizabeth Bondi, Aravind Venugopal, Milind Tambe, Balaraman Ravindran
Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife.
1 code implementation • 30 Sep 2022 • Siddhartha Banerjee, Sean R. Sinclair, Milind Tambe, Lily Xu, Christina Lee Yu
How best to incorporate historical data to "warm start" bandit algorithms is an open question: naively initializing reward estimates using all historical samples can suffer from spurious data and imbalanced data coverage, leading to computational and storage issues $\unicode{x2014}$ particularly salient in continuous action spaces.
no code implementations • 31 Oct 2022 • Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, Astro Teller
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society.
no code implementations • 19 Jan 2023 • Paritosh Verma, Shresth Verma, Aditya Mate, Aparna Taneja, Milind Tambe
Restless multi-arm bandits (RMABs) is a popular decision-theoretic framework that has been used to model real-world sequential decision making problems in public health, wildlife conservation, communication systems, and beyond.
no code implementations • 6 Feb 2023 • Aditya Mate, Bryan Wilder, Aparna Taneja, Milind Tambe
We consider the task of evaluating policies of algorithmic resource allocation through randomized controlled trials (RCTs).
no code implementations • 1 Mar 2023 • Arpita Biswas, Jackson A. Killian, Paula Rodriguez Diaz, Susobhan Ghosh, Milind Tambe
The goal is to plan an intervention schedule that maximizes the expected reward while satisfying budget constraints on each worker as well as fairness in terms of the load assigned to each worker.
no code implementations • 22 May 2023 • Panayiotis Danassis, Shresth Verma, Jackson A. Killian, Aparna Taneja, Milind Tambe
The success of many healthcare programs depends on participants' adherence.
no code implementations • 26 May 2023 • Sanket Shah, Andrew Perrault, Bryan Wilder, Milind Tambe
In this paper, we propose solutions to these issues, avoiding the aforementioned assumptions and utilizing the ML model's features to increase the sample efficiency of learning loss functions.
no code implementations • 17 Jul 2023 • Lily Xu, Esther Rolf, Sara Beery, Joseph R. Bennett, Tanya Berger-Wolf, Tanya Birch, Elizabeth Bondi-Kelly, Justin Brashares, Melissa Chapman, Anthony Corso, Andrew Davies, Nikhil Garg, Angela Gaylard, Robert Heilmayr, Hannah Kerner, Konstantin Klemmer, Vipin Kumar, Lester Mackey, Claire Monteleoni, Paul Moorcroft, Jonathan Palmer, Andrew Perrault, David Thau, Milind Tambe
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.
no code implementations • 23 Oct 2023 • Yunfan Zhao, Nikhil Behari, Edward Hughes, Edwin Zhang, Dheeraj Nagaraj, Karl Tuyls, Aparna Taneja, Milind Tambe
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently been studied from a multi-agent reinforcement learning perspective.
no code implementations • 13 Nov 2023 • Arshika Lalan, Shresth Verma, Kumar Madhu Sudan, Amrita Mahale, Aparna Hegde, Milind Tambe, Aparna Taneja
Mobile health programs are becoming an increasingly popular medium for dissemination of health information among beneficiaries in less privileged communities.
no code implementations • 15 Dec 2023 • Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez
Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems.
no code implementations • 19 Feb 2024 • Niclas Boehmer, Yash Nair, Sanket Shah, Lucas Janson, Aparna Taneja, Milind Tambe
When resources are scarce, an allocation policy is needed to decide who receives a resource.
no code implementations • 22 Feb 2024 • Nikhil Behari, Edwin Zhang, Yunfan Zhao, Aparna Taneja, Dheeraj Nagaraj, Milind Tambe
Efforts to reduce maternal mortality rate, a key UN Sustainable Development target (SDG Target 3. 1), rely largely on preventative care programs to spread critical health information to high-risk populations.
1 code implementation • 21 Feb 2024 • Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain, Henry Gasztowtt, Stephan Zheng, David C. Parkes, Milind Tambe, YiLing Chen
Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making.
no code implementations • 8 Mar 2024 • Sanket Shah, Arun Suggala, Milind Tambe, Aparna Taneja
However, the availability and time of these health workers are limited resources.
no code implementations • 26 Mar 2024 • David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important.
1 code implementation • 10 Jun 2021 • Panayiotis Danassis, Aris Filos-Ratsikas, Haipeng Chen, Milind Tambe, Boi Faltings
Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone).
1 code implementation • 30 May 2022 • Kai Wang*, Lily Xu, Aparna Taneja, Milind Tambe
Restless multi-armed bandits (RMABs) extend multi-armed bandits to allow for stateful arms, where the state of each arm evolves restlessly with different transitions depending on whether that arm is pulled.
1 code implementation • 7 Feb 2024 • Biyonka Liang, Lily Xu, Aparna Taneja, Milind Tambe, Lucas Janson
Restless multi-armed bandits (RMABs) are used to model sequential resource allocation in public health intervention programs.
1 code implementation • 22 Jun 2021 • Jackson A. Killian, Arpita Biswas, Sanket Shah, Milind Tambe
Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which $N$ independent processes are managed.
1 code implementation • 11 May 2022 • Lily Xu, Arpita Biswas, Fei Fang, Milind Tambe
Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land.
2 code implementations • 14 Sep 2020 • Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, Milind Tambe
Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i. e., patrollers), who must patrol vast areas to protect from attackers (e. g., poachers or illegal loggers).
1 code implementation • 8 Mar 2019 • 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, Eric Enyel
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.
1 code implementation • 15 Jun 2021 • Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe
We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy.
1 code implementation • NeurIPS 2020 • Aditya Mate, Jackson Killian, Haifeng Xu, Andrew Perrault, Milind Tambe
Our main contributions are as follows: (i) Building on the Whittle index technique for RMABs, we derive conditions under which the Collapsing Bandits problem is indexable.
1 code implementation • 13 Jun 2021 • Haipeng Chen, Wei Qiu, Han-Ching Ou, Bo An, Milind Tambe
Empirical results show that our method achieves influence as high as the state-of-the-art methods for contingency-aware IM, while having negligible runtime at test phase.
1 code implementation • 31 Oct 2022 • Abheek Ghosh, Dheeraj Nagaraj, Manish Jain, Milind Tambe
Whittle index policies, which are based on Lagrangian relaxations, are widely used in these settings due to their simplicity and near-optimality under certain conditions.
1 code implementation • 17 Aug 2023 • Jackson A. Killian, Manish Jain, Yugang Jia, Jonathan Amar, Erich Huang, Milind Tambe
RMABs are increasingly being used for sensitive decisions such as in public health, treatment scheduling, anti-poaching, and -- the motivation for this work -- digital health.
2 code implementations • NeurIPS 2020 • Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe
Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.
1 code implementation • 3 Mar 2019 • Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, Yair Zick
Influence maximization is a widely used model for information dissemination in social networks.
Computer Science and Game Theory Social and Information Networks
1 code implementation • 8 Jul 2019 • Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network.
1 code implementation • NeurIPS 2019 • Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe
However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization.