# Efficient learning of nonlinear prediction models with time-series privileged information

arXiv: https://arxiv.org/abs/2209.07067 · HTML: https://mlsanity.com/paper/2209.07067/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 63 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA](https://mlsanity.com/paper/1605.06336/) (2016)
- [Unifying distillation and privileged information](https://mlsanity.com/paper/1511.03643/) (2015)
- [The Benefit of Multitask Representation Learning](https://mlsanity.com/paper/1505.06279/) (2015)
- [Generalization Properties of Learning with Random Features](https://mlsanity.com/paper/1602.04474/) (2016)
- [A Brief Prehistory of Double Descent](https://mlsanity.com/paper/2004.04328/) (2020)
- [On the role of surrogates in the efficient estimation of treatment effects with limited outcome data](https://mlsanity.com/paper/2003.12408/) (2020)
- [Patterns for Learning with Side Information](https://mlsanity.com/paper/1511.06429/) (2015)
- [Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models](https://mlsanity.com/paper/2110.14993/) (2021)
- [Efficient least squares for estimating total effects under linearity and causal sufficiency](https://mlsanity.com/paper/2008.03481/) (2020)
- [Everything old is new again: A multi-view learning approach to learning using privileged information and distillation](https://mlsanity.com/paper/1903.03694/) (2019)

## Built on by (most cited first, 4 of 4)

- [Unsupervised domain adaptation by learning using privileged information](https://mlsanity.com/paper/2303.09350/) (2023)
- [Learning Using Generated Privileged Information by Text-to-Image Diffusion Models](https://mlsanity.com/paper/2309.15238/) (2023)
- [When are radiology reports useful for training medical image classifiers?](https://mlsanity.com/paper/2510.24385/) (2025)
- [Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information](https://mlsanity.com/paper/2606.22210/) (2026)

## Abstract (arXiv)

In domains where sample sizes are limited, efficient learning algorithms are critical. Learning using privileged information (LuPI) offers increased sample efficiency by allowing prediction models access to auxiliary information at training time which is unavailable when the models are used. In recent work, it was shown that for prediction in linear-Gaussian dynamical systems, a LuPI learner with access to intermediate time series data is never worse and often better in expectation than any unbiased classical learner. We provide new insights into this analysis and generalize it to nonlinear prediction tasks in latent dynamical systems, extending theoretical guarantees to the case where the map connecting latent variables and observations is known up to a linear transform. In addition, we propose algorithms based on random features and representation learning for the case when this map is unknown. A suite of empirical results confirm theoretical findings and show the potential of using privileged time-series information in nonlinear prediction.