Martin Vechev

zkay v0.2: Practical Data Privacy for Smart Contracts

2020 · arXiv (Cornell University)

Recent work introduces zkay, a system for specifying and enforcing data privacy in smart contracts. While the original prototype implementation of zkay (v0.1) demonstrates the feasibility of the approach, its proof-of-concept implementation suffers from severe …

Scaling Polyhedral Neural Network Verification on GPUs

2020 · arXiv (Cornell University)

Certifying the robustness of neural networks against adversarial attacks is essential to their reliable adoption in safety-critical systems such as autonomous driving and medical diagnosis. Unfortunately, state-of-the-art verifiers either do not scale to bigger networks …

Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation

2024 · arXiv (Cornell University)

To ensure that text generated by large language models (LLMs) is in an expected format, constrained decoding proposes to enforce strict formal language constraints during generation. However, as we show in this work, not only …

Mitigating Catastrophic Forgetting in Language Transfer via Model Merging

2024 · arXiv (Cornell University)

As open-weight large language models (LLMs) achieve ever more impressive performances across a wide range of tasks in English, practitioners aim to adapt these models to different languages. However, such language adaptation is often accompanied …

PHOG: probabilistic model for code

2016 · International Conference on Machine Learning

We introduce a new generative model for code called probabilistic higher order grammar (PHOG). PHOG generalizes probabilistic context free grammars (PCFGs) by allowing conditioning of a production rule beyond the parent non-terminal, thus capturing rich …