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Found 6 papers, 2 papers with code
Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias
Common methods for interpreting neural models in natural language processing typically examine either their structure or their behavior, but not both.
Fast Parallel Algorithms for Statistical Subset Selection Problems
Recently, there has been a surge of interest in a parallel optimization technique called adaptive sampling which produces solutions with desirable approximation guarantees for submodular maximization in exponentially faster parallel runtime.
Robustness from Simple Classifiers
Despite the vast success of Deep Neural Networks in numerous application domains, it has been shown that such models are not robust i. e., they are vulnerable to small adversarial perturbations of the input.
Investigating Gender Bias in Language Models Using Causal Mediation Analysis
As a case study, we apply this methodology to analyzing gender bias in pre-trained Transformer language models.
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation
Both the upper and lower bounds are under the assumption that queries are only on feasible sets (i. e., of size at most k).
Instance Specific Approximations for Submodular Maximization
A major question is therefore how to measure the performance of an algorithm in comparison to an optimal solution on instances we encounter in practice.
