# Context-Aware Doubly-Robust Semi-Supervised Learning

arXiv: https://arxiv.org/abs/2502.15577 · HTML: https://mlsanity.com/paper/2502.15577/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 21 Feb 2025.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Meta Pseudo Labels](https://mlsanity.com/paper/2003.10580/) (2020)
- [Prediction-Powered Inference](https://mlsanity.com/paper/2301.09633/) (2023)
- [PPI++: Efficient Prediction-Powered Inference](https://mlsanity.com/paper/2311.01453/) (2023)
- [Self-Training: A Survey](https://mlsanity.com/paper/2202.12040/) (2022)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation](https://mlsanity.com/paper/2406.04291/) (2024)
- [Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling](https://mlsanity.com/paper/2409.15156/) (2024)
- [Doubly Robust Self-Training](https://mlsanity.com/paper/2306.00265/) (2023)
- [Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates](https://mlsanity.com/paper/2312.12625/) (2023)
- [Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?](https://mlsanity.com/paper/2301.07682/) (2023)
- [Semi-Supervised Learning via Cross-Prediction-Powered Inference for Wireless Systems](https://mlsanity.com/paper/2405.15415/) (2024)
- [Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning](https://mlsanity.com/paper/2404.01815/) (2024)
- [Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization](https://mlsanity.com/paper/1406.3665/) (2014)
- [Environment-Aware and Training-Free Beam Alignment for mmWave Massive MIMO via Channel Knowledge Map](https://mlsanity.com/paper/2102.09871/) (2021)
- [Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning](https://mlsanity.com/paper/2406.15819/) (2024)
- [Driving Innovation in 6G Wireless Technologies: The OpenAirInterface Approach](https://mlsanity.com/paper/2412.13295/) (2024)

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

- [How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks](https://mlsanity.com/paper/2507.07067/) (2025)
- [Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data](https://mlsanity.com/paper/2608.27996/) (2026)

## Abstract (arXiv)

The widespread adoption of artificial intelligence (AI) in next-generation communication systems is challenged by the heterogeneity of traffic and network conditions, which call for the use of highly contextual, site-specific, data. A promising solution is to rely not only on real-world data, but also on synthetic pseudo-data generated by a network digital twin (NDT). However, the effectiveness of this approach hinges on the accuracy of the NDT, which can vary widely across different contexts. To address this problem, this paper introduces context-aware doubly-robust (CDR) learning, a novel semi-supervised scheme that adapts its reliance on the pseudo-data to the different levels of fidelity of the NDT across contexts. CDR is evaluated on the task of downlink beamforming where it outperforms previous state-of-the-art approaches, providing a 24% loss decrease when compared to doubly-robust (DR) semi-supervised learning in regimes with low labeled data availability.