5 accepted papers
CodeQL is a powerful static analysis engine that represents programs’ abstract syntax trees as databases that can be queried to detect security vulnerabilities. While CodeQL supports expressive interprocedural dataflow queries, the coverage and precision of its existing security queries remain limit…
Neurosymbolic learning enables the integration of symbolic reasoning with deep learning but faces significant challenges in scaling to complex symbolic programs, large datasets, or both. We introduce DOLPHIN, a framework that tackles these challenges by supporting neurosymbolic programs in Python, e…
Software is prone to security vulnerabilities. Program analysis tools to detect them have limited effectiveness in practice due to their reliance on human labeled specifications. Large language models (or LLMs) have shown impressive code generation capabilities but they cannot do complex reasoning o…
Generalized Class Discovery (GCD) clusters base and novel classes in a target domain, using supervision from a source domain with only base classes. Current methods often falter with distribution shifts and typically require access to target data during training, which can sometimes be impractical.…
We present the first systematic study of effectiveness of robustness transformations on a diverse set of 24 probabilistic programs representing generalized linear models, mixture models, and time-series models. We evaluate five robustness transformations from literature on each model. We quantify an…