18 citations · 32 across the 18 of their papers we have counts for
21 papers
Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran
(Flat) Reinforcement Learning (RL) agents face significant challenges in environments with sparse rewards that require long-horizon reasoning. A compelling approach to improve samp…
Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines
Eduardo de Conto, Blaise Genest, Arvind Easwaran +2
Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instan…
Approximation-Free Differentiable Oblique Decision Trees
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran
Decision Trees (DTs) are widely used in safety-critical domains such as medical diagnosis, valued for their interpretability and effectiveness on tabular data. However, training ac…
Formal Reasoning About Confidence and Automated Verification of Neural Networks
Mohammad Afzal, S. Akshay, Blaise Genest +1
In the last decade, a large body of work has emerged on robustness of neural networks, i.e., checking if the decision remains unchanged when the input is slightly perturbed. Howeve…
DesCartes Builder: A Tool to Develop Machine-Learning Based Digital Twins
Eduardo de Conto, Blaise Genest, Arvind Easwaran +2
Digital twins (DTs) are increasingly utilized to monitor, manage, and optimize complex systems across various domains, including civil engineering. A core requirement for an effect…
Solution-aware vs global ReLU selection: partial MILP strikes back for DNN verification
Yuke Liao, Blaise Genest, Kuldeep Meel +1
To handle complex instances, we revisit a divide-and-conquer approach to break down the complexity: instead of few complex BaB calls, we rely on many small {\em partial} MILP calls…