NeurIPS 2024 Past Other
NeurIPS 2024 Workshop on Behavioral Machine Learning
NeurIPS 2024 Workshop on Behavioral ML
- Submission deadline
- Sep 17, 2024, 00:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (72)
Fetched from OpenReview (v2) on 2026-06-10.
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A Behavioral Economics Approach to Principled Multi-Agent Reinforcement Learning
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A New Approach to Generate Individual Level Data of Walled Garden Platforms: Linear Programming Reconstruction
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A Spatio-Temporal Flow Matching Framework for Pedestrian Trajectory Prediction
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Accuracy Isn’t Everything: Understanding the Desiderata of AI Tools in Legal-Financial Settings
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An Experimental Study of Competitive Market Behavior Through LLMs
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Analyzing Reward Functions via Trajectory Alignment
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Are LLMs good pragmatic speakers?
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Assessing Behavioral Alignment of Personality-Driven Generative Agents in Social Dilemma Games
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Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?
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Attention Redistribution During Event Segmentation In Large Language Model
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Behavioral Sequence Modeling with Ensemble Learning
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Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks
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Cognitive Bias for Human-AI ad hoc Teamwork
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Comparing Human and LLM Ratings of Music-Recommendation Quality with User Context
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Computational discovery of human reinforcement learning dynamics from choice behavior
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CoS: Enhancing Personalization with Context Steering
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Debiasing Global Workspace: A Cognitive Neural Framework for Learning Debiased and Interpretable Representations
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Deep and shallow thinking in a single forward pass
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Designing Algorithmic Delegates
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Do Language Models Have Bayesian Brains? Distinguishing Stochastic and Deterministic Decision Patterns within Large Language Models
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Does GPT Really Get It? A Hierarchical Scale to Quantify Human and AI's Understanding of Algorithms
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Empowering Neural Networks with Control and Planning Abilities
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Evidence from the Synthetic Laboratory: Language Models as Auction Participants
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ExpressivityArena: Can LLMs Express Information Implicitly?
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From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs
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Generating and Validating Agent and Environment Code for Simulating Realistic Personality Profiles with Large Language Models
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Helping People Predict Agent Behaviors by Operationalizing the Variation Theory of Learning
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HuLE-Nav: Human-Like Exploration for Zero-Shot Object Navigation via Vision-Language Models
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Impact of a biomimetic training regimen based on early visual experience on neural network organization and behavior
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Improving optimal control and estimation for realistic noise models of the sensorimotor system
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Integrating Preference-Aware Modeling of Human Spatial Behavior in Cyber-Physical-Human Systems
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Investigating Same-Different Concept Understanding in Generative Multimodal Models
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Learning to Cooperate with Humans using Generative Agents
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Limitations in Planning Ability in AlphaZero
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LLM to Bridge Human Instructions with a Dynamic Symbolic Representation in Hierarchical Reinforcement Learning
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LLMs and Personalities: Inconsistencies Across Scales
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Meaning Through Motion: DUET – A Multimodal Dataset for Kinesics Analysis in Dyadic Activities
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Measuring Implicit Bias in Explicitly Unbiased Large Language Models
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Mitigating Overconfidence in Large Language Models: A Behavioral Lens on Confidence Estimation and Calibration
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Modulating Language Model Experiences through Frictions
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Monitoring Behavioral Changes Using Spatiotemporal Graphs: A Case Study on the StudentLife Dataset
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Multimodal Integration in Audio-Visual Speech Recognition --- How Far Are We From Human-Level Robustness?
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Multivariate Prediction of Human Behavior in Task fMRI
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MuMA-ToM: Multi-modal Multi-Agent Theory of Mind
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Non-local Exchange: Introduce Non-locality via Graph Re-wiring to Graph Neural Networks
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Optimizing Reward Models with Proximal Policy Exploration in Preference-Based Reinforcement Learning
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Outcome-Irrelevant and State-Independent Learning Mechanisms in Human Reinforcement Learning
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PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences
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Predicting human decisions with behavioral theories and machine learning
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Principled probing of foundation models in the auditory modality
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Principles of Animal Cognition for LLM Evaluations: A Case Study on Transitive Inference
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Probing LLM World Models: Enhancing Guesstimation with Wisdom of Crowds Decoding
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pSAE-chiatry: Utilizing Sparse Autoencoders to Uncover Mental-Health-Related Features in Language Models
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Rational Metareasoning for Large Language Models
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Reassessing Number-Detector Units in Convolutional Neural Networks
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Rediscovering the Latent Dimensions of Personality with Large Language Models as Trait Descriptors
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Selective Preference Aggregation
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Self-Attention Limits Working Memory Capacity of Transformer-Based Models
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StepCountJITAI: simulation environment for RL with application to physical activity adaptive intervention
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Superficial Alignment, Subtle Divergence, and Nudge Sensitivity in LLM Decision-Making
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The Double-Edged Sword of Behavioral Responses in Strategic Classification
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Towards Deliberating Agents: Evaluating the Ability of Large Language Models to Deliberate
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Towards Robust Estimation of Human Intention Hierarchy in Robot Teleoperation
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Ultimatum Bargaining: Algorithms vs. Humans
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Understanding Graphical Perception in Data Visualization through Zero-shot Prompting of Vision-Language Models
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Unexploited Information Value in Human-AI Collaboration
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Using LLMs to Model the Beliefs and Preferences of Targeted Populations
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Virtual Personas for Language Models via an Anthology of Backstories
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Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models
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What you say or how you say it? Predicting Conflict Outcomes in Real and LLM-Generated Conversations
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WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
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Words that work: Using language to generate hypotheses