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Researcher profile

George J. Pappas

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Verisig: verifying safety properties of hybrid systems with neural network controllers

    2018 · arXiv (Cornell University)

    This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. Although techniques exist for verifying input/output properties of the neural network itself, these methods cannot …

  2. Minimum Number of Probes for Brain Dynamics Observability

    2015 · arXiv (Cornell University)

    In this paper, we address the problem of placing sensor probes in the brain such that the system dynamics' are generically observable. The system dynamics whose states can encode for instance the fire-rating of the …

  3. Actor-only Deterministic Policy Gradient via Zeroth-order Gradient Oracles in Action Space

    2021

    Deterministic policies demonstrate substantial empirical success over their stochastic counterparts as they remove a level of randomness in Policy Gradient (PG) methods when applied to stochastic search problems involving Markov decision processes. However, current implementations …

  4. Conformal Prediction Regions for Time Series using Linear Complementarity Programming

    2023 · arXiv (Cornell University)

    Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction to time series data leads to conservative prediction regions. In fact, …

  5. Symmetries-enhanced Multi-Agent Reinforcement Learning

    2025 · arXiv (Cornell University)

    Multi-agent reinforcement learning has emerged as a powerful framework for enabling agents to learn complex, coordinated behaviors but faces persistent challenges regarding its generalization, scalability and sample efficiency. Recent advancements have sought to alleviate those …

  6. Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models

    2025

    Uncertainty quantification (UQ) is essential for safe deployment of generative AI models such as large language models (LLMs), especially in high stakes applications. Conformal prediction (CP) offers a principled uncertainty quantification framework, but classical methods …