TITLE: TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets 

ABSTRACT: The best neural architecture for a given machine learning problem depends on
many factors: not only the complexity and structure of the dataset, but also on
resource constraints including latency, compute, energy consumption, etc.
Neural architecture search (NAS) for tabular datasets is an important but
under-explored problem. Previous NAS algorithms designed for image search
spaces incorporate resource constraints directly into the reinforcement
learning (RL) rewards. However, for NAS on tabular datasets, this protocol
often discovers suboptimal architectures. This paper develops TabNAS, a new and
more effective approach to handle resource constraints in tabular NAS using an
RL controller motivated by the idea of rejection sampling. TabNAS immediately
discards any architecture that violates the resource constraints without
training or learning from that architecture. TabNAS uses a Monte-Carlo-based
correction to the RL policy gradient update to account for this extra filtering
step. Results on several tabular datasets demonstrate the superiority of TabNAS
over previous reward-shaping methods: it finds better models that obey the
constraints.
The best neural architecture for a given machine learning problem depends on
many factors: not only the complexity and structure of the dataset, but also on
resource constraints including latency, compute, energy consumption, etc.
Neural architecture search (NAS) for tabular datasets is an important but
under-explored problem. Previous NAS algorithms designed for image search
spaces incorporate resource constraints directly into the reinforcement
learning (RL) rewards. However, for NAS on tabular datasets, this protocol
often discovers suboptimal architectures. This paper develops TabNAS, a new and
more effective approach to handle resource constraints in tabular NAS using an
RL controller motivated by the idea of rejection sampling. TabNAS immediately
discards any architecture that violates the resource constraints without
training or learning from that architecture. TabNAS uses a Monte-Carlo-based
correction to the RL policy gradient update to account for this extra filtering
step. Results on several tabular datasets demonstrate the superiority of TabNAS
over previous reward-shaping methods: it finds better models that obey the
constraints.
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
http://arxiv.org/abs/2204.07615v4