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Found 13 papers, 6 papers with code
Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification
We present Rango, a fully automated proof synthesis tool for Coq that automatically identifies relevant premises and also similar proofs from the current project and uses them during synthesis.
Cobblestone: Iterative Automation for Formal Verification
We introduce Cobblestone, a new proof-synthesis approach that improves on the state of the art by taking advantage of partial progress in proof synthesis attempts.
QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learning
To address this problem, we create QEDCartographer, an automated proof-synthesis tool that combines supervised and reinforcement learning to more effectively explore the proof space.
Thinking Forward: Memory-Efficient Federated Finetuning of Language Models
We also derive Spry's convergence rate, showing that the gradients decrease inversely proportional to the number of FL rounds, indicating the convergence up to the limits of heterogeneity.
Attack-Resilient Image Watermarking Using Stable Diffusion
To address this problem, we present ZoDiac, which uses a pre-trained stable diffusion model to inject a watermark into the trainable latent space, resulting in watermarks that can be reliably detected in the latent vector even when attacked.
Baldur: Whole-Proof Generation and Repair with Large Language Models
Recent work has developed methods to automate formal verification using proof assistants, such as Coq and Isabelle/HOL, e. g., by training a model to predict one proof step at a time, and using that model to search through the space of possible proofs.
Enforcing Delayed-Impact Fairness Guarantees
Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on peoples' lives or well-being (e. g., applications involving education, employment, and lending), can inadvertently increase social inequality in the long term.
Fairness Guarantees under Demographic Shift
Recent studies have demonstrated that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes.
Blindspots in Python and Java APIs Result in Vulnerable Code
We find that using APIs with blindspots statistically significantly reduces the developers' ability to correctly reason about the APIs in both languages, but that the effect is more pronounced for Python.
Software Engineering Cryptography and Security
Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models
To address bias in machine learning, data scientists need tools that help them understand the trade-offs between model quality and fairness in their specific data domains.
Offline Contextual Bandits with High Probability Fairness Guarantees
We present RobinHood, an offline contextual bandit algorithm designed to satisfy a broad family of fairness constraints.
Fairness Testing: Testing Software for Discrimination
This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior.
Effectiveness of Anonymization in Double-Blind Review
Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions.
Digital Libraries General Literature Software Engineering
