counterfactual
952 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find counterfactual models and implementationsMost implemented papers
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.
Counterfactual Multi-Agent Policy Gradients
COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies.
Unbiased Scene Graph Generation from Biased Training
Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims.
Solving Imperfect-Information Games via Discounted Regret Minimization
Counterfactual regret minimization (CFR) is a family of iterative algorithms that are the most popular and, in practice, fastest approach to approximately solving large imperfect-information games.
Deep Counterfactual Regret Minimization
This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game.
Single Deep Counterfactual Regret Minimization
Counterfactual Regret Minimization (CFR) is the most successful algorithm for finding approximate Nash equilibria in imperfect information games.
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods.
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on.
Locating and Editing Factual Associations in GPT
To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME).