Search Results for author: Milind Tambe

Found 55 papers, 15 papers with code

Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks

no code implementations1 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.

Fairness Multi-Armed Bandits +1

Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation

no code implementations6 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).

Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits

no code implementations19 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.

Decision Making

Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence

no code implementations31 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.

Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless Bandits

1 code implementation31 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.

Multi-Armed Bandits

Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits

1 code implementation30 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.

Optimistic Whittle Index Policy: Online Learning for Restless Bandits

1 code implementation30 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.

Multi-Armed Bandits

Ranked Prioritization of Groups in Combinatorial Bandit Allocation

1 code implementation11 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.

Evolutionary Approach to Security Games with Signaling

no code implementations29 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.

ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria

no code implementations28 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.

Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses

no code implementations30 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.

Decision Making

Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022

no code implementations28 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.

Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health

no code implementations2 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.

Multi-Armed Bandits Scheduling

Networked Restless Multi-Armed Bandits for Mobile Interventions

no code implementations28 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.

Multi-Armed Bandits

Facilitating human-wildlife cohabitation through conflict prediction

no code implementations22 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).

Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning

no code implementations4 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.

Multi-agent Reinforcement Learning Multi-Armed Bandits +1

Q-Learning Lagrange Policies for Multi-Action Restless Bandits

1 code implementation22 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.

Multi-Armed Bandits Q-Learning

Robust Reinforcement Learning Under Minimax Regret for Green Security

1 code implementation15 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.

Decision Making reinforcement-learning +1

Contingency-Aware Influence Maximization: A Reinforcement Learning Approach

1 code implementation13 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.

Combinatorial Optimization reinforcement-learning +1

AI-driven Prices for Externalities and Sustainability in Production Markets

1 code implementation10 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).


Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning

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.

Reinforcement Learning (RL)

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning

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.

Decision Making Reinforcement Learning (RL)

Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare

no code implementations17 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.


Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems

no code implementations8 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.

Multi-Armed Bandits

Active Screening for Recurrent Diseases: A Reinforcement Learning Approach

no code implementations7 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.

Combinatorial Optimization reinforcement-learning +1

Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty

no code implementations18 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

Collapsing Bandits and Their Application to Public Health Intervention

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.

Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery

no code implementations20 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.

Dual-Mandate Patrols: Multi-Armed Bandits for Green Security

2 code implementations14 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).

Multi-Armed Bandits

Tracking disease outbreaks from sparse data with Bayesian inference

no code implementations12 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.

Bayesian Inference Epidemiology +1

Collapsing Bandits and Their Application to Public Health Interventions

no code implementations5 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.

Automatically Learning Compact Quality-aware Surrogates for Optimization Problems

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.

Portfolio Optimization

Fair Influence Maximization: A Welfare Optimization Approach

no code implementations14 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.

Fairness Management

AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

no code implementations16 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.

Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning

no code implementations20 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).

reinforcement-learning Reinforcement Learning (RL)

MIPaaL: Mixed Integer Program as a Layer

no code implementations12 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.

Decision Making

Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling

1 code implementation8 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.

Graph Sampling

End to end learning and optimization on graphs

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.

Link Prediction

Group-Fairness in Influence Maximization

1 code implementation3 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

End-to-End Game-Focused Learning of Adversary Behavior in Security Games

no code implementations3 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.

Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

no code implementations5 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.

Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

no code implementations14 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.

Combinatorial Optimization

Mitigating the Curse of Correlation in Security Games by Entropy Maximization

no code implementations11 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.


Pilot Testing an Artificial Intelligence Algorithm That Selects Homeless Youth Peer Leaders Who Promote HIV Testing

no code implementations19 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.

Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version

no code implementations30 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.

Learning Adversary Behavior in Security Games: A PAC Model Perspective

no code implementations30 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.

Security Games with Information Leakage: Modeling and Computation

no code implementations23 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.

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