Toward a general, scaleable framework for Bayesian teaching with applications to topic models
This addresses the bottleneck of knowledge transfer from machines to humans, with incremental improvements in scalability for real-world applications like topic models.
The authors tackled the problem of conveying machine-accumulated knowledge to humans by proposing a scalable Bayesian teaching framework, which uses pseudo-marginal sampling to select data subsets that speed learning, with simulation results showing effectiveness in approximating probabilities and differences from random sampling.
Machines, not humans, are the world's dominant knowledge accumulators but humans remain the dominant decision makers. Interpreting and disseminating the knowledge accumulated by machines requires expertise, time, and is prone to failure. The problem of how best to convey accumulated knowledge from computers to humans is a critical bottleneck in the broader application of machine learning. We propose an approach based on human teaching where the problem is formalized as selecting a small subset of the data that will, with high probability, lead the human user to the correct inference. This approach, though successful for modeling human learning in simple laboratory experiments, has failed to achieve broader relevance due to challenges in formulating general and scalable algorithms. We propose general-purpose teaching via pseudo-marginal sampling and demonstrate the algorithm by teaching topic models. Simulation results show our sampling-based approach: effectively approximates the probability where ground-truth is possible via enumeration, results in data that are markedly different from those expected by random sampling, and speeds learning especially for small amounts of data. Application to movie synopsis data illustrates differences between teaching and random sampling for teaching distributions and specific topics, and demonstrates gains in scalability and applicability to real-world problems.