Search Results for author:
Found 16 papers, 8 papers with code
Conformal Off-Policy Prediction for Multi-Agent Systems
In this work, we introduce MA-COPP, the first conformal prediction method to solve OPP problems involving multi-agent systems, deriving joint prediction regions for all agents' trajectories when one or more "ego" agents change their policies.
Counterfactual Influence in Markov Decision Processes
Our work addresses a fundamental problem in the context of counterfactual inference for Markov Decision Processes (MDPs).
Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees
This tutorial focuses on efficient methods to predictive monitoring (PM), the problem of detecting at runtime future violations of a given requirement from the current state of a system.
Probabilistic Reach-Avoid for Bayesian Neural Networks
Such computed lower bounds provide safety certification for the given policy and BNN model.
Causal Temporal Reasoning for Markov Decision Processes
We introduce $\textit{PCFTL (Probabilistic CounterFactual Temporal Logic)}$, a new probabilistic temporal logic for the verification of Markov Decision Processes (MDP).
Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes
We consider the problem of predictive monitoring (PM), i. e., predicting at runtime the satisfaction of a desired property from the current system's state.
Neural Predictive Monitoring under Partial Observability
We consider the problem of predictive monitoring (PM), i. e., predicting at runtime future violations of a system from the current state.
Certification of Iterative Predictions in Bayesian Neural Networks
We consider the problem of computing reach-avoid probabilities for iterative predictions made with Bayesian neural network (BNN) models.
On Guaranteed Optimal Robust Explanations for NLP Models
We build on abduction-based explanations for ma-chine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP).
Learnable Strategies for Bilateral Agent Negotiation over Multiple Issues
We present a novel bilateral negotiation model that allows a self-interested agent to learn how to negotiate over multiple issues in the presence of user preference uncertainty.
MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas
Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices.
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets.
Neural Simplex Architecture
NSA not only provides safety assurances in the presence of a possibly unsafe neural controller, but can also improve the safety of such a controller in an online setting via retraining, without overly degrading its performance.
Statistical Guarantees for the Robustness of Bayesian Neural Networks
We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set such that the BNN prediction differs between the two.
Neural State Classification for Hybrid Systems
We introduce the State Classification Problem (SCP) for hybrid systems, and present Neural State Classification (NSC) as an efficient solution technique.
How to Learn a Model Checker
We show how machine-learning techniques, particularly neural networks, offer a very effective and highly efficient solution to the approximate model-checking problem for continuous and hybrid systems, a solution where the general-purpose model checker is replaced by a model-specific classifier trained by sampling model trajectories.
