# Adaptive Classification for Prediction Under a Budget

arXiv: https://arxiv.org/abs/1705.10194 · HTML: https://mlsanity.com/paper/1705.10194/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2017.

## Built on by (most cited first, 15 of 15)

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- [DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks](https://mlsanity.com/paper/2210.05370/) (2022)
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- [Online Selective Classification with Limited Feedback](https://mlsanity.com/paper/2110.14243/) (2021)
- [DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization](https://mlsanity.com/paper/2307.04963/) (2023)
- [Predicting on the Edge: Identifying Where a Larger Model Does Better](https://mlsanity.com/paper/2202.07652/) (2022)
- [How Important is Importance Sampling for Deep Budgeted Training?](https://mlsanity.com/paper/2110.14283/) (2021)
- [EREBA: Black-box Energy Testing of Adaptive Neural Networks](https://mlsanity.com/paper/2202.06084/) (2022)
- [Costly Features Classification using Monte Carlo Tree Search](https://mlsanity.com/paper/2102.07073/) (2021)
- [Learning to Abstain From Uninformative Data](https://mlsanity.com/paper/2309.14240/) (2023)
- [Fast Classification with Sequential Feature Selection in Test Phase](https://mlsanity.com/paper/2306.14347/) (2023)
- [Dynamic feature selection in medical predictive monitoring by reinforcement learning](https://mlsanity.com/paper/2405.19729/) (2024)
- [Efficiency Robustness of Dynamic Deep Learning Systems](https://mlsanity.com/paper/2506.10831/) (2025)
- [Learning Performance Maximizing Ensembles with Explainability Guarantees](https://mlsanity.com/paper/2312.12715/) (2023)
- [Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution](https://mlsanity.com/paper/2609.04820/) (2026)

## Abstract (arXiv)

We propose a novel adaptive approximation approach for test-time resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the input among a collection of models. Our objective is to minimize overall average cost without sacrificing accuracy. We learn gating and prediction models on fully labeled training data by means of a bottom-up strategy. Our novel bottom-up method first trains a high-accuracy complex model. Then a low-complexity gating and prediction model are subsequently learned to adaptively approximate the high-accuracy model in regions where low-cost models are capable of making highly accurate predictions. We pose an empirical loss minimization problem with cost constraints to jointly train gating and prediction models. On a number of benchmark datasets our method outperforms state-of-the-art achieving higher accuracy for the same cost.