# How Does Data Freshness Affect Real-time Supervised Learning?

arXiv: https://arxiv.org/abs/2208.06948 · HTML: https://mlsanity.com/paper/2208.06948/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 56 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Stochastic Adversarial Video Prediction](https://mlsanity.com/paper/1804.01523/) (2018)
- [Self-Supervised Visual Planning with Temporal Skip Connections](https://mlsanity.com/paper/1710.05268/) (2017)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [A simple measure of conditional dependence](https://mlsanity.com/paper/1910.12327/) (2019)
- [A Minimax Approach to Supervised Learning](https://mlsanity.com/paper/1606.02206/) (2016)
- [Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review](https://mlsanity.com/paper/1912.11676/) (2019)
- [Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks](https://mlsanity.com/paper/1801.01803/) (2018)
- [A Whittle Index Approach to Minimizing Functions of Age of Information](https://mlsanity.com/paper/1908.10438/) (2019)
- [On Universal Features for High-Dimensional Learning and Inference](https://mlsanity.com/paper/1911.09105/) (2019)
- [Sampling for Data Freshness Optimization: Non-linear Age Functions](https://mlsanity.com/paper/1812.07241/) (2018)
- [Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning](https://mlsanity.com/paper/1805.09470/) (2018)
- [The Age of Correlated Features in Supervised Learning based Forecasting](https://mlsanity.com/paper/2103.00092/) (2021)
- [A Local Geometric Interpretation of Feature Extraction in Deep Feedforward Neural Networks](https://mlsanity.com/paper/2202.04632/) (2022)
- [On the Credibility of Information Flows in Real-time Wireless Networks](https://mlsanity.com/paper/1901.10599/) (2019)

## Built on by (most cited first, 3 of 3)

- [Timely Communications for Remote Inference](https://mlsanity.com/paper/2404.16281/) (2024)
- [Multimodal Remote Inference](https://mlsanity.com/paper/2508.07555/) (2025)
- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)

## Abstract (arXiv)

In this paper, we analyze the impact of data freshness on real-time supervised learning, where a neural network is trained to infer a time-varying target (e.g., the position of the vehicle in front) based on features (e.g., video frames) observed at a sensing node (e.g., camera or lidar). One might expect that the performance of real-time supervised learning degrades monotonically as the feature becomes stale. Using an information-theoretic analysis, we show that this is true if the feature and target data sequence can be closely approximated as a Markov chain; it is not true if the data sequence is far from Markovian. Hence, the prediction error of real-time supervised learning is a function of the Age of Information (AoI), where the function could be non-monotonic. Several experiments are conducted to illustrate the monotonic and non-monotonic behaviors of the prediction error. To minimize the inference error in real-time, we propose a new "selection-from-buffer" model for sending the features, which is more general than the "generate-at-will" model used in earlier studies. By using Gittins and Whittle indices, low-complexity scheduling strategies are developed to minimize the inference error, where a new connection between the Gittins index theory and Age of Information (AoI) minimization is discovered. These scheduling results hold (i) for minimizing general AoI functions (monotonic or non-monotonic) and (ii) for general feature transmission time distributions. Data-driven evaluations are presented to illustrate the benefits of the proposed scheduling algorithms.