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Found 6 papers, 2 papers with code
CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation
Our experiments show that CFL achieves such a detoxification without much impact on the model perplexity.
Learning Good Interventions in Causal Graphs via Covering
We study the causal bandit problem that entails identifying a near-optimal intervention from a specified set $A$ of (possibly non-atomic) interventions over a given causal graph.
Intervention Efficient Algorithm for Two-Stage Causal MDPs
We study Markov Decision Processes (MDP) wherein states correspond to causal graphs that stochastically generate rewards.
Scale Invariant Monte Carlo under Linear Function Approximation with Curvature based step-size
To speed up convergence in our algorithm, we introduce an adaptive step-size based on the curvature of the iterate convergence path -- a novelty that may be useful in more general optimization contexts as well.
A generalized Bayesian framework for the analysis of subscription based businesses
We have created a framework for analyzing subscription based businesses in terms of a unified metric which we call SCV (single customer value).
