3 citations · 19 across the 50 of their papers we have counts for
54 papers
DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies
Haichuan Wang, Anagha Satish, Jiale Zheng +4
Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something conventional neural policies strugg…
Large Language Model Orchestration under Heterogeneous Preferences via Explicit Persona Inference
Shuqing Shi, Ziyan Wang, Milind Tambe +1
LLM orchestration investigates how an orchestrator coordinates a group of autonomous agents to achieve common goals or maximize collective welfare. The agents are typically heterog…
Tight Efficiency Guarantees for Strategyproof Linear Regression
Yichen Huang, Yuqi Pan, Michael Mitzenmacher +2
We study the trade-off between squared-error accuracy and incentive compatibility in linear regression. Agents report private labels associated with publicly known features and pre…
Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals
Lingkai Kong, Hezi Jiang, Andrew Ma +3
Peer-referral recruitment systems such as respondent-driven sampling are critical for studying and intervening on hidden populations affected by infectious diseases. To accelerate…
Generating Robust Portfolios of Optimization Models using Large Language Models
Eleni Straitouri, Cheol Woo Kim, Milind Tambe
Mathematical optimization is a powerful tool for structured decision-making across domains such as resource allocation and planning. Formulating optimization models faithful to rea…
Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning
Shresth Verma, Mauricio Tec, Cheol Woo Kim +2
While LLMs excel at single-turn generation, they struggle with long-horizon, multi-turn interactions. Offline reinforcement learning (RL) offers a scalable approach, yet its perfor…
Learning to Persuade a Biased Receiver
Yuqi Pan, Sadie Zhao, Milind Tambe +1
We study a repeated information design setting in which the receiver, who is also the decision-maker, updates beliefs in a systematically biased way. More specifically, a distorted…
Many Preferences, Few Policies: Compact Portfolios for Multi-Objective LLM Alignment
Cheol Woo Kim, Jai Moondra, Roozbeh Nahavandi +3
Aligning large language models (LLMs) requires balancing competing objectives such as helpfulness, harmlessness, and conciseness. The appropriate balance varies across users and ap…
Decisions and Deployment: The Five-Year SAHELI Project (2020-2025) on Restless Multi-Armed Bandits for Improving Maternal and Child Health
Shresth Verma, Arpan Dasgupta, Neha Madhiwalla +2
Maternal and child health is a critical concern around the world. In many global health programs disseminating preventive care and health information, limited healthcare worker res…
Incentive-Aware AI Safety via Strategic Resource Allocation: A Stackelberg Security Games Perspective
Cheol Woo Kim, Davin Choo, Tzeh Yuan Neoh +1
As AI systems grow more capable and autonomous, ensuring their safety and reliability requires not only model-level alignment but also strategic oversight of the humans and institu…
LLM Active Alignment: A Nash Equilibrium Perspective
Tonghan Wang, Yuqi Pan, Xinyi Yang +3
We develop a game-theoretic framework for predicting and steering the behavior of populations of large language models (LLMs) through Nash equilibrium (NE) analysis. To avoid the i…
Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective
Haichuan Wang, Tao Lin, Lingkai Kong +3
Existing alignment methods directly use the reward model learned from user preference data to optimize an LLM policy, subject to KL regularization with respect to the base policy.…
Health Facility Location in Ethiopia: Leveraging LLMs to Integrate Expert Knowledge into Algorithmic Planning
Yohai Trabelsi, Guojun Xiong, Fentabil Getnet +2
Ethiopia's Ministry of Health is upgrading health posts to improve access to essential services, particularly in rural areas. Limited resources, however, require careful prioritiza…
Generative AI for Social Impact
Lingkai Kong, Cheol Woo Kim, Davin Choo +1
AI for Social Impact (AI4SI) has achieved compelling results in public health, conservation, and security, yet scaling these successes remains difficult due to a persistent deploym…
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…
Policy-Embedded Graph Expansion: Networked HIV Testing with Diffusion-Driven Network Samples
Akseli Kangaslahti, Davin Choo, Lingkai Kong +3
HIV is a retrovirus that attacks the human immune system and can lead to death without proper treatment. In collaboration with the WHO and the University of Witwatersrand, we study…
Lightweight Robust Direct Preference Optimization
Cheol Woo Kim, Shresth Verma, Mauricio Tec +1
Direct Preference Optimization (DPO) has become a popular method for fine-tuning large language models (LLMs) due to its stability and simplicity. However, it is also known to be s…
Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
Rui Ai, Yuqi Pan, David Simchi-Levi +2
With the rapid progress of multi-agent large language model (LLM) reasoning, how to effectively aggregate answers from multiple LLMs has emerged as a fundamental challenge. Standar…
VORTEX: Aligning Task Utility and Human Preferences through LLM-Guided Reward Shaping
Guojun Xiong, Milind Tambe
In social impact optimization, AI decision systems often rely on solvers that optimize well-calibrated mathematical objectives. However, these solvers cannot directly accommodate e…
Preference Robustness for DPO with Applications to Public Health
Cheol Woo Kim, Shresth Verma, Mauricio Tec +1
We study an LLM fine-tuning task for designing reward functions for sequential resource allocation problems in public health, guided by human preferences expressed in natural langu…
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…
Beyond Listenership: AI-Predicted Interventions Drive Improvements in Maternal Health Behaviours
Arpan Dasgupta, Sarvesh Gharat, Neha Madhiwalla +3
Automated voice calls with health information are a proven method for disseminating maternal and child health information among beneficiaries and are deployed in several programs a…
Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health
Arpan Dasgupta, Mizhaan Maniyar, Awadhesh Srivastava +7
Mobile health (mHealth) programs utilize automated voice messages to deliver health information, particularly targeting underserved communities, demonstrating the effectiveness of…
Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
Lingkai Kong, Haichuan Wang, Tonghan Wang +2
Incorporating pre-collected offline data can substantially improve the sample efficiency of reinforcement learning (RL), but its benefits can break down when the transition dynamic…