# Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis

arXiv: https://arxiv.org/abs/2404.04903 · HTML: https://mlsanity.com/paper/2404.04903/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 95 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Set Functions for Time Series](https://mlsanity.com/paper/1909.12064/) (2019)
- [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://mlsanity.com/paper/1711.03705/) (2017)
- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition](https://mlsanity.com/paper/1811.00270/) (2018)
- [Graph Neural Network for Cell Tracking in Microscopy Videos](https://mlsanity.com/paper/2202.04731/) (2022)
- [Learning with Feature Evolvable Streams](https://mlsanity.com/paper/1706.05259/) (2017)
- [One-Pass Learning with Incremental and Decremental Features](https://mlsanity.com/paper/1605.09082/) (2016)
- [Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction](https://mlsanity.com/paper/1708.04757/) (2017)
- [Prediction with Unpredictable Feature Evolution](https://mlsanity.com/paper/1904.12171/) (2019)
- [Normalized Online Learning](https://mlsanity.com/paper/1408.2065/) (2014)
- [Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs](https://mlsanity.com/paper/2008.11828/) (2020)
- [Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts](https://mlsanity.com/paper/2303.05155/) (2023)
- [Adaptive Normalization in Streaming Data](https://mlsanity.com/paper/1910.07696/) (2019)
- [Evolving Metric Learning for Incremental and Decremental Features](https://mlsanity.com/paper/2006.15334/) (2020)
- [Simulation-supervised deep learning for analysing organelles states and behaviour in living cells](https://mlsanity.com/paper/2008.12617/) (2020)
- [No Imputation Needed: A Switch Approach to Irregularly Sampled Time Series](https://mlsanity.com/paper/2309.08698/) (2023)

## Built on by (most cited first, 2 of 2)

- [packetLSTM: Dynamic LSTM Framework for Streaming Data with Varying Feature Space](https://mlsanity.com/paper/2410.17394/) (2024)
- [Haphazard Inputs as Images in Online Learning](https://mlsanity.com/paper/2504.02912/) (2025)

## Abstract (arXiv)

The domain of online learning has experienced multifaceted expansion owing to its prevalence in real-life applications. Nonetheless, this progression operates under the assumption that the input feature space of the streaming data remains constant. In this survey paper, we address the topic of online learning in the context of haphazard inputs, explicitly foregoing such an assumption. We discuss, classify, evaluate, and compare the methodologies that are adept at modeling haphazard inputs, additionally providing the corresponding code implementations and their carbon footprint. Moreover, we classify the datasets related to the field of haphazard inputs and introduce evaluation metrics specifically designed for datasets exhibiting imbalance. The code of each methodology can be found at https://github.com/Rohit102497/HaphazardInputsReview