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Found 5 papers, 2 papers with code
Stream-Based Monitoring of Algorithmic Fairness
Concretely, we present a principled way to formalize algorithmic fairness over temporal data streams in the specification language RTLola and demonstrate the efficacy of this approach on a number of benchmarks.
Learning Better Representations From Less Data For Propositional Satisfiability
By combining certificate-driven training and expert iteration, our model learns better representations than models trained for classification only, with a much higher data efficiency -- requiring orders of magnitude less training data.
Counterfactuals Modulo Temporal Logics
Lewis' theory of counterfactuals is the foundation of many contemporary notions of causality.
Formal Specifications from Natural Language
We study the generalization abilities of language models when translating natural language into formal specifications with complex semantics.
Attention Flows for General Transformers
In this paper, we study the computation of how much an input token in a Transformer model influences its prediction.
