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Haoran Yu

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12 papers

2 author rows

AAAI Conference 2026 Conference Paper

Automated Human Strategic Behavior Modeling via Large Language Models

Xiaohan Xie
Haoran Yu
Biying Shou
Jianwei Huang

What if machines could discover human behavioral patterns better than experts? Traditional behavioral modeling in economics depends on costly manual refinement by domain experts, severely limiting scalability and discovery potential. We introduce AutoBM, an automated behavioral modeling framework leveraging large language models (LLMs) to systematically generate, evaluate, and refine interpretable behavioral models directly from human behavior data. AutoBM represents candidate models as structured natural language specifications, explicitly defining symbolic terms along with their tunable parameters, interpretations, and design rationales. AutoBM leverages LLMs to automatically translate each language specification into executable code, optimize tunable parameters, and evaluate model performance. Utilizing LLM-guided search strategies, AutoBM iteratively recombines and improves models at the term level, closely mirroring human expert practices. Experiments conducted across three distinct strategic environments (the ultimatum game, repeated rock-paper-scissors, and continuous double auctions) demonstrate that AutoBM-generated models consistently outperform leading manually crafted models, achieving significant improvements in prediction accuracy while maintaining clear interpretability. Our results demonstrate that automated frameworks can not only match but systematically exceed human expertise in behavioral modeling, fundamentally changing how we understand strategic human behavior.

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AAAI Conference 2026 Conference Paper

Inferring Heterogeneous Private Valuations from Offline Market Data via Entropic Risk-Sensitive Utility Maximization

Xingyu Qian
Haoran Yu

Inferring humans' private valuations for goods from their observed market behavior is essential for evaluating market efficiency and improving trading mechanism design. A core challenge lies in uncovering the human decision function that maps private valuations and observed market states to actions. In complex market settings where humans make sequential decisions in stochastic environments, neural networks offer the flexibility to model this decision function. However, training them without access to private valuations or environment dynamics remains challenging. We tackle this challenge and study how to infer heterogeneous human valuations from offline decision data in continuous double auctions. We propose learning the decision function via risk‑sensitive utility maximization. First, we train a generative model on offline bid and ask data to simulate individual trading behavior. Using this generative model, we instantiate simulated markets composed of randomly generated buyers and sellers. Second, we introduce an agent into these simulated markets and use reinforcement learning to learn a risk-sensitive utility-maximizing decision function for the agent. Third, we formulate a bilevel optimization to jointly recover private valuations and risk preference parameters. Our extensive experiments on a large‑scale continuous double auction dataset demonstrate that our framework significantly reduces errors in inferring real human valuations.

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AAAI Conference 2025 Conference Paper

Integrating Inference and Experimental Design for Contextual Behavioral Model Learning

Gongtao Zhou
Haoran Yu

The strategic behavior of users is significantly influenced by their hidden information such as private valuations, risk preferences, and price sensitivities. Contextual behavioral model learning refers to learning the dependence of users' hidden information on their observable context information. While many existing studies use offline data to learn contextual behavioral models, we study how to design sequential experiments to collect the most informative user behavioral data for learning. We propose a basic inference-then-design method. In each experimental period, it infers a probabilistic contextual behavioral model using historical experimental data, and then designs the new experiment to maximize the gain of information about the probabilistic model. We further improve the basic method in two aspects. First, we improve the inference step by specifying a more informative prior for learning the probabilistic contextual behavioral model. Second, we integrate the inference and design steps instead of conducting them separately. Our rigorous theoretic analysis reveals that the optimization objective of the inference step can be modified to account for the downstream experimental design step. Numerical experiments show that our methods lead to more effective experiments, i.e., the collected experimental data can help in learning a more accurate behavioral model.

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IJCAI Conference 2025 Conference Paper

Inverse Game Theory: An Incenter-Based Approach

Lvye Cui
Haoran Yu
Pierre Pinson
Dario Paccagnan

Estimating player utilities from observed equilibria is crucial for many applications. Existing approaches to tackle this problem are either limited to specific games or do not scale well with the number of players. Our work addresses these issues by proposing a novel utility estimation method for general multi-player non-cooperative games. Our main idea consists in reformulating the inverse game problem as an inverse variational inequality problem and in selecting among all utility parameters consistent with the data, the so-called incenter. We show that the choice of the incenter can produce parameters that are most robust to the observed equilibrium behaviors. However, its computation is challenging, as the number of constraints in the corresponding optimization problem increases with the number of players and the behavior space size. To tackle this challenge, we propose a loss function-based algorithm, making our method scalable to games with many players or a continuous action space. Furthermore, we show that our method can be extended to incorporate prior knowledge of player utilities, and that it can handle inconsistent data, i. e. , data where players do not play exact equilibria. Numerical experiments on three game applications demonstrate that our methods outperform the state of the art. The code, datasets, and supplementary material are available at https: //github. com/cuilvye/Incenter-Project.

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NeurIPS Conference 2025 Conference Paper

Learning Preferences without Interaction for Cooperative AI: A Hybrid Offline-Online Approach

Haitong Ma
Haoran Yu
Haobo Fu
Shuai Li

Reinforcement learning (RL) for collaborative agents capable of cooperating with humans to accomplish tasks has long been a central goal in the RL community. While prior approaches have made progress in adapting collaborative agents to diverse human partners, they often focus solely on optimizing task performance and overlook human preferences—despite the fact that such preferences often diverge from the reward-maximization objective of the environment. Addressing this discrepancy poses significant challenges: humans typically provide only a small amount of offline, preference-related feedback and are unable to engage in online interactions, resulting in a distributional mismatch between the agent’s online learning process and the offline human data. To tackle this, we formulate the problem as an online&offline reinforcement learning problem that jointly integrates online generalization and offline preference learning, entirely under an offline training regime. We propose a simple yet effective training framework built upon existing RL algorithms that alternates between offline preference learning and online generalization recovery, ensuring the stability and alignment of both learning objectives. We evaluate our approach on a benchmark built upon the Overcooked environment—a standard environment for human-agent collaboration—and demonstrate remarkable performance across diverse preference styles and cooperative scenarios.

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AAAI Conference 2024 Conference Paper

Data-Driven Knowledge-Aware Inference of Private Information in Continuous Double Auctions

Lvye Cui
Haoran Yu

Inferring the private information of humans from their strategic behavioral data is crucial and challenging. The main approach is first obtaining human behavior functions (which map public information and human private information to behavior), enabling subsequent inference of private information from observed behavior. Most existing studies rely on strong equilibrium assumptions to obtain behavior functions. Our work focuses on continuous double auctions, where multiple traders with heterogeneous rationalities and beliefs dynamically trade commodities and deriving equilibria is generally intractable. We develop a knowledge-aware machine learning-based framework to infer each trader's private cost vectors for producing different units of its commodity. Our key idea is to learn behavior functions by incorporating the statistical knowledge about private costs given the observed trader asking behavior across the population. Specifically, we first use a neural network to characterize each trader's behavior function. Second, we leverage the statistical knowledge to derive the posterior distribution of each trader's private costs given its observed asks. Third, through designing a novel loss function, we utilize the knowledge-based posterior distributions to guide the learning of the neural network. We conduct extensive experiments on a large experimental dataset, and demonstrate the superior performance of our framework over baselines in inferring the private information of humans.

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AAAI Conference 2024 Conference Paper

Predicting Real-World Penny Auction Durations by Integrating Game Theory and Machine Learning

Yujia Wang
Haoran Yu

Game theory and machine learning are two widely used techniques for predicting the outcomes of strategic interactions among humans. However, the game theory-based approach often relies on strong rationality and informational assumptions, while the machine learning-based approach typically requires the testing data to come from the same distribution as the training data. Our work studies how to integrate the two techniques to address these weaknesses. We focus on the interactions among real bidders in penny auctions, and develop a three-stage framework to predict the distributions of auction durations, which indicate the numbers of bids and auctioneer revenues. Specifically, we first leverage a pre-trained neural network to encode the descriptions of products in auctions into embeddings. Second, we apply game theory models to make preliminary predictions of auction durations. In particular, we tackle the challenge of accurately inferring parameters in game theory models. Third, we develop a Multi-Branch Mixture Density Network to learn the mapping from product embeddings and game-theoretic predictions to the distributions of actual auction durations. Experiments on real-world penny auction data demonstrate that our framework outperforms both game theory-based and machine learning-based prediction approaches.

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EAAI Journal 2024 Journal Article

Research on data-driven model for power grid fault diagnosis fusing topological quantification information

Xu Zhang
Zirui Wang
Mingxuan Du
Xuekui Mao
Ruiting Ding
Haoran Yu
Ziqi Zhang

In increasingly complex power grid operation scenarios and fault modes, rapid and accurate fault identification is highly important for improving the reliability of power systems. Faced with the massive amount of available power grid data, the rapid development of artificial intelligence technology provides a powerful tool for power grid fault diagnosis. However, existing data-driven diagnosis methods lack quantitative power grid topology change representations, cannot integrate power grid fault topology with alarm information, and have limited effectiveness in terms of diagnosing complex faults. To address these issues, a data-driven power grid fault diagnosis model that integrates topological quantitative features is proposed. By studying the changes in the topological connection relationships of equipment before and after a power grid fault and the topological connectivity in a power failure zone, based on the basic topological network indicators in graph theory, a representation of the quantitative features of the power grid fault topology is achieved, and these quantitative features and the alarm information are integrated to construct a data-driven power grid fault diagnosis model. The model uses the light gradient boosting machine algorithm to determine the types of complex faults and accurately identify faulty equipment, addressing the lack of model diagnosis effects considered in previous studies. Finally, the accuracy and effectiveness of the model are verified by using simulated fault cases.

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ECAI Conference 2023 Conference Paper

Active Finetuning Protein Language Model: A Budget-Friendly Method for Directed Evolution

Ming Qin
Keyan Ding
Bin Wu 0025
Zhenping Li
Haihong Yang
Zeyuan Wang
Hongbin Ye
Haoran Yu

Directed evolution is a widely-used strategy of protein engineering to improve protein function via mimicking natural mutation and selection. Machine learning-assisted directed evolution (MLDE) approaches aim to learn a fitness predictor, thereby efficiently searching for optimal mutants within the vast combinatorial mutation space. Since annotating mutants is both costly and labor-intensive, how to efficiently sample and utilize informative protein mutants to train the predictor is a critical problem in MLDE. Previous MLDE works just simply utilized pre-trained protein language models (PPLMs) for sampling without tailoring to the specific target protein of interest, which has not fully exploited the potential of PPLMs. In this work, we propose a novel method, the Actively-Finetuned Protein language model for Directed Evolution(AFP-DE), which leverages PPLMs to actively sample and fine-tune themselves, continuously improving the model’s sampling and overall performance through iterations, to achieve efficient directed protein evolution. Extensive experiments have shown the effectiveness of our method in generating optimal mutants with minimal annotation effort, outperforming previous works even with fewer annotated mutants, making it budget-friendly for biological experiments.

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IJCAI Conference 2023 Conference Paper

Inferring Private Valuations from Behavioral Data in Bilateral Sequential Bargaining

Lvye Cui
Haoran Yu

Inferring bargainers' private valuations on items from their decisions is crucial for analyzing their strategic behaviors in bilateral sequential bargaining. Most existing approaches that infer agents' private information from observable data either rely on strong equilibrium assumptions or require a careful design of agents' behavior models. To overcome these weaknesses, we propose a Bayesian Learning-based Valuation Inference (BLUE) framework. Our key idea is to derive feasible intervals of bargainers' private valuations from their behavior data, using the fact that most bargainers do not choose strictly dominated strategies. We leverage these feasible intervals to guide our inference. Specifically, we first model each bargainer's behavior function (which maps his valuation and bargaining history to decisions) via a recurrent neural network. Second, we learn these behavior functions by utilizing a novel loss function defined based on feasible intervals. Third, we derive the posterior distributions of bargainers' valuations according to their behavior data and learned behavior functions. Moreover, we account for the heterogeneity of bargainer behaviors, and propose a clustering algorithm (K-Loss) to improve the efficiency of learning these behaviors. Experiments on both synthetic and real bargaining data show that our inference approach outperforms baselines.

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ICLR Conference 2023 Conference Paper

Multi-level Protein Structure Pre-training via Prompt Learning

Zeyuan Wang
Qiang Zhang 0026
Shuangwei Hu
Haoran Yu
Xurui Jin
Zhichen Gong
Huajun Chen

A protein can focus on different structure levels to implement its functions. Each structure has its own merit and driving forces in describing some specific characteristics, and they cannot replace each other. Most existing function prediction methods take the tertiary structure as input, unintentionally ignoring the other levels of protein structures. Considering protein sequences can determine multi-level structures, in this paper, we aim to realize the comprehensive potential of protein sequences for function prediction. Specifically, we propose a new prompt-guided multi-task pre-training and fine-tuning framework, and the resulting protein model is called PromptProtein. Through the prompt-guided multi-task pre-training, we learn multiple prompt signals to steer the model to focus on different structure levels. We also design a prompt fine-tuning module to provide downstream tasks the on-demand flexibility of utilizing respective levels of structure information. Extensive experiments on function prediction and protein engineering show that PromptProtein outperforms state-of-the-art methods by large margins.

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ICRA Conference 2013 Conference Paper

Design, calibration and preliminary testing of a robotic telemanipulator for OCT guided retinal surgery

Haoran Yu
Jin-Hui Shen
Karen M. Joos
Nabil Simaan

This paper presents an experimental system for demonstrating a new concept for retinal micro-vascular surgery. This concept involves the use of stents to maintain the structural integrity in artery/vein crossings. A design of an 11 degree of freedom robot that includes a 6 DoF Stewart-Gough platform, a two DoF differential wrist, and a three DoF actuator for deployment of stent and bridge vessel separators is proposed as a validated robotic system for ophthalmic microsurgery. The robot also allows for quick exchange of surgical graspers and the integration of a custom made B-mode OCT probe. The paper presents the kinematic modeling and calibration of the robot for demonstration of ocular and intraocular manipulation. The system telemanipulation framework is constructed and experimental evaluations of stent deployment and membrane peeling are shown with a verification of results using OCT probe images. We believe these preliminary results demonstrate new technology that may enable micro-vascular stenting for treatment of branch retinal vein occlusion while offering a general platform for dexterous retinal surgery.

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