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Found 5 papers, 2 papers with code
Uncertainty Quantification for Local Model Explanations Without Model Access
We present a model-agnostic algorithm for generating post-hoc explanations and uncertainty intervals for a machine learning model when only a static sample of inputs and outputs from the model is available, rather than direct access to the model itself.
Content Filtering with Inattentive Information Consumers
We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content.
Policy-focused Agent-based Modeling using RL Behavioral Models
We test the hypothesis that RL agents are effective as utility-maximizing agents in policy ABMs.
Perturbing Inputs to Prevent Model Stealing
We show how perturbing inputs to machine learning services (ML-service) deployed in the cloud can protect against model stealing attacks.
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes
The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such decision problems.
