Key Contributions

1. Advancements in Uncertainty Representation and Exploration

To enhance the reliability and robustness of AI agents, I advanced methods for representing and handling uncertainty:

• Ensemble Sampling Theory:

  • Developed a rigorous analysis establishing ensemble sampling as a scalable approximation of Thompson sampling.
  • Addressed critical challenges in exploration efficiency and scalability in reinforcement learning.
  • Impact on Trustworthiness: Improves agents’ ability to make reliable decisions under uncertainty, enhancing their robustness.

• Neural Ensemble++ Architecture:

  • Designed to overcome challenges in ensemble scalability and mitigate coupling in shared-layer architectures.
  • Enhanced uncertainty quantification, facilitating more effective exploration in deep RL models.
  • Impact on Trustworthiness: Provides accurate uncertainty estimates, leading to more trustworthy decision-making.

2. Data-Efficient Reinforcement Learning and Multi-Agent Strategic Learning

Efficient learning under data scarcity is crucial for trustworthy AI agents operating in real-world environments:

• HyperAgent Framework:

  • Created HyperAgent to quantify and resolve epistemic uncertainty in optimal value estimation ($Q^\star$) based on Ensemble++.
  • Achieved 10x improvement in data and computational efficiency on deep RL benchmarks such as Atari.
  • Impact on Trustworthiness: Ensures consistent performance even with limited data, enhancing reliability.

• Memoire Framework and Divergence-Augmented Policy Optimization:

  • Developed a distributed deep RL system utilizing prioritized sampling, achieving 10x throughput.
  • Introduced methods to stabilize policy optimization with off-policy data reuse.
  • Impact on Trustworthiness: Enhances stability and robustness in learning, crucial for dependable AI agents.

• Frameworks for Unknown Repeated Games:

  • Integrated structure-aware modeling with no-regret learning.
  • Enabled efficient collaboration and competition in domains such as traffic routing and radar sensing.
  • Impact on Trustworthiness: Facilitates predictable and fair agent behavior in multi-agent settings.

3. Enhancing Language-Based Reasoning, Decision-Making, and Human-AI Interaction

Transparency and explainability are key aspects of trustworthiness, addressed through advancements in language-based reasoning:

• Uncertainty-Guided Search Strategies in LLMs:

  • Applied Ensemble++ & Thompson sampling to improve complex multi-step reasoning in LLMs.
  • Achieved significant advancements in solving intricate mathematical problems.
  • Impact on Trustworthiness: Improves explainability and confidence in AI reasoning processes.

• Hospital Referral Agent:

  • Implemented a conversational agent serving 16 hospitals nationwide.
  • Designed a multi-agent role-play framework to generate synthetic data, addressing privacy concerns.
  • Impact on Trustworthiness: Enhances reliability and efficiency in critical medical decision-making.

• Human-AI Collaboration Frameworks:

  • Explored methods to enhance the interplay between humans and AI systems.
  • Effectively handled uncertainty and prioritized human oversight where necessary.
  • Impact on Trustworthiness: Promotes ethical operation and respect for human judgment, essential for user trust.

This research collectively enhances the trustworthiness of AI agents by addressing fundamental challenges and providing scalable, reliable solutions applicable to real-world scenarios.