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Enze Shi

7 accepted papers

2026

Decoding 3D Perception via BrainSSD: Synergistic Fusion of EEG Representations from Static and Dynamic Visual Streams

CVPR 2026

Understanding how the brain constructs coherent 3D visual percepts from multifaceted experiences remains a pivotal yet underexplored challenge. To investigate this, we introduce BrainSSD, a novel framework for decoding 3D representations from electroencephalography (EEG) signals. The core of BrainSS

Cited by 0SourceScholar
2026

Structured Diversity Control: A Dual-Level Framework for Group-Aware Multi-Agent Coordination

ICRA 2026poster

Controlling the behavioral diversity is a pivotal challenge in multi-agent reinforcement learning (MARL), particularly in complex collaborative scenarios. While existing methods attempt to regulate behavioral diversity by directly differentiating across all agents, they lack deep characterization an…

2025

HARP: Human-Assisted Regrouping With Permutation Invariant Critic for Multi-Agent Reinforcement Learning

ICRA 2025

Human-in-the-loop reinforcement learning integrates human expertise to accelerate agent learning and provide critical guidance and feedback in complex fields. However, many existing approaches focus on single-agent tasks and require continuous human involvement during the training process, significa

Cited by 1SourcecodeScholar
2025

Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning

NeurIPS 2025poster

The remarkable empirical performance of distributional reinforcement learning~(RL) has garnered increasing attention to understanding its theoretical advantages over classical RL. By decomposing the categorical distributional loss commonly employed in distributional RL, we find that the potential su…

Cited by 0SourceScholar
2025

Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis

NeurIPS 2025poster

Machine learning models have achieved widespread success but often inherit and amplify historical biases, resulting in unfair outcomes. Traditional fairness methods typically impose constraints at the prediction level, without addressing underlying biases in data representations. In this work, we pr…

Cited by 0SourceScholar
2024

Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations

NAACL 2024long

Pre-trained vector representations in natural language processing often inadvertently encode undesirable social biases. Identifying and removing unwanted biased information from vector representation is an evolving and significant challenge. Our study uniquely addresses this issue from the perspecti…

2024

Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach

NeurIPS 2024poster

As generative large language models (LLMs) such as ChatGPT gain widespread adoption in various domains, their potential to propagate and amplify social biases, particularly in high-stakes areas such as the labor market, has become a pressing concern. AI algorithms are not only widely used in the sel…

Cited by 1SourcePDFScholar