# Learning to Price: Interpretable Attribute-Level Models for Dynamic Markets

arXiv: https://arxiv.org/abs/2602.00188 · HTML: https://mlsanity.com/paper/2602.00188/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Contextual Bandits with Latent Confounders: An NMF Approach](https://mlsanity.com/paper/1606.00119/) (2016)
- [Explicit Explore-Exploit Algorithms in Continuous State Spaces](https://mlsanity.com/paper/1911.00617/) (2019)
- [Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing](https://mlsanity.com/paper/1801.10242/) (2018)
- [Meta-Learning for Contextual Bandit Exploration](https://mlsanity.com/paper/1901.08159/) (2019)

## Abstract (arXiv)

Dynamic pricing in high-dimensional markets poses fundamental challenges of scalability, uncertainty, and interpretability. Existing low-rank bandit formulations learn efficiently but rely on latent features that obscure how individual product attributes influence price. We address this by introducing an interpretable emphAdditive Feature Decomposition-based Low-Dimensional Demand (AFDLD) model, where product prices are expressed as the sum of attribute-level contributions and substitution effects are explicitly modeled. Building on this structure, we propose ADEPT (Additive DEcomposition for Pricing with cross-elasticity and Time-adaptive learning)-a projection-free, gradient-free online learning algorithm that operates directly in attribute space and achieves a sublinear regret of tildeO(√dT3/4). Through controlled synthetic studies and real-world datasets, we show that ADEPT (i) learns near-optimal prices under dynamic market conditions, (ii) adapts rapidly to shocks and drifts, and (iii) yields transparent, attribute-level price explanations. The results demonstrate that interpretability and efficiency in autonomous pricing agents can be achieved jointly through structured, attribute-driven representations.