56 citations · 72 across the 6 of their papers we have counts for
11 papers
Navigating Ensemble Configurations for Algorithmic Fairness
Michael Feffer, Martin Hirzel, Samuel C. Hoffman +3
Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits. A popular approach to train more s…
Translating Canonical SQL to Imperative Code in Coq
Véronique Benzaken, Évelyne Contejean, Mohammed Houssem Hachmaoui +4
SQL is by far the most widely used and implemented query language. Yet, on some key features, such as correlated queries and NULL value semantics, many implementations diverge or c…
An Empirical Study of Modular Bias Mitigators and Ensembles
Michael Feffer, Martin Hirzel, Samuel C. Hoffman +3
There are several bias mitigators that can reduce algorithmic bias in machine learning models but, unfortunately, the effect of mitigators on fairness is often not stable when meas…
CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in Coq
Koundinya Vajjha, Avraham Shinnar, Vasily Pestun +2
Reinforcement learning algorithms solve sequential decision-making problems in probabilistic environments by optimizing for long-term reward. The desire to use reinforcement learni…
Lale: Consistent Automated Machine Learning
Guillaume Baudart, Martin Hirzel, Kiran Kate +2
Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies…
Type Safety with JSON Subschema
Andrew Habib, Avraham Shinnar, Martin Hirzel +1
JSON is a popular data format used pervasively in web APIs, cloud computing, NoSQL databases, and increasingly also machine learning. JSON Schema is a language for declaring the st…