110 citations · 183 across the 17 of their papers we have counts for
17 papers
Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
Lingkai Kong, Anagha Satish, Hezi Jiang +6
Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraint…
Generative AI Against Poaching: Latent Composite Flow Matching for Wildlife Conservation
Lingkai Kong, Haichuan Wang, Charles A. Emogor +3
Poaching poses significant threats to wildlife and biodiversity. A valuable step in reducing poaching is to forecast poacher behavior, which can inform patrol planning and other co…
Reinforcement learning with combinatorial actions for coupled restless bandits
Lily Xu, Bryan Wilder, Elias B. Khalil +1
Reinforcement learning (RL) has increasingly been applied to solve real-world planning problems, with progress in handling large state spaces and time horizons. However, a key bott…
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits
Biyonka Liang, Lily Xu, Aparna Taneja +2
Public health programs often provide interventions to encourage program adherence, and effectively allocating interventions is vital for producing the greatest overall health outco…
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
Lily Xu, Esther Rolf, Sara Beery +21
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 Res…
Bridging adaptive management and reinforcement learning for more robust decisions
Melissa Chapman, Lily Xu, Marcus Lapeyrolerie +1
From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and…
Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits
Siddhartha Banerjee, Sean R. Sinclair, Milind Tambe +2
Most real-world deployments of bandit algorithms exist somewhere in between the offline and online set-up, where some historical data is available upfront and additional data is co…
Ranked Prioritization of Groups in Combinatorial Bandit Allocation
Lily Xu, Arpita Biswas, Fei Fang +1
Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land. Combinatorial bandits have…
Optimistic Whittle Index Policy: Online Learning for Restless Bandits
Kai Wang, Lily Xu, Aparna Taneja +1
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 w…
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning
Jackson A. Killian, Lily Xu, Arpita Biswas +1
We introduce robustness in \textit{restless multi-armed bandits} (RMABs), a popular model for constrained resource allocation among independent stochastic processes (arms). Nearly…
Envisioning Communities: A Participatory Approach Towards AI for Social Good
Elizabeth Bondi, Lily Xu, Diana Acosta-Navas +1
Research in artificial intelligence (AI) for social good presupposes some definition of social good, but potential definitions have been seldom suggested and never agreed upon. The…
Robust Reinforcement Learning Under Minimax Regret for Green Security
Lily Xu, Andrew Perrault, Fei Fang +2
Green security domains feature defenders who plan patrols in the face of uncertainty about the adversarial behavior of poachers, illegal loggers, and illegal fishers. Importantly,…
Coordinating Followers to Reach Better Equilibria: End-to-End Gradient Descent for Stackelberg Games
Kai Wang, Lily Xu, Andrew Perrault +2
A growing body of work in game theory extends the traditional Stackelberg game to settings with one leader and multiple followers who play a Nash equilibrium. Standard approaches f…
Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery
Rachel Guo, Lily Xu, Drew Cronin +3
Illegal wildlife poaching is driving the loss of biodiversity. To combat poaching, rangers patrol expansive protected areas for illegal poaching activity. However, rangers often ca…
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
Lily Xu, Elizabeth Bondi, Fei Fang +3
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 are…
Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers
Lily Xu, Andrew Perrault, Andrew Plumptre +4
Applications of artificial intelligence for wildlife protection have focused on learning models of poacher behavior based on historical patterns. However, poachers' behaviors are d…
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations
Lily Xu, Shahrzad Gholami, Sara Mc Carthy +11
Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of…