# Contextual Procurement Auctions with Bandit Learning

arXiv: https://arxiv.org/abs/2607.05813 · HTML: https://mlsanity.com/paper/2607.05813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Causal Bandits: Learning Good Interventions via Causal Inference](https://mlsanity.com/paper/1606.03203/) (2016)
- [Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions](https://mlsanity.com/paper/2002.11349/) (2020)
- [Combinatorial Causal Bandits without Graph Skeleton](https://mlsanity.com/paper/2301.13392/) (2023)
- [Causal Inference for Influence Propagation -- Identifiability of the Independent Cascade Model](https://mlsanity.com/paper/2107.04224/) (2021)
- [Carrot and Stick: Eliciting Comparison Data and Beyond](https://mlsanity.com/paper/2410.23243/) (2024)
- [A Correction of Pseudo Log-Likelihood Method](https://mlsanity.com/paper/2403.18127/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning the Cost of Reliable Inference](https://mlsanity.com/paper/2609.28322/) (2026)

## Abstract (arXiv)

We study repeated procurement auctions in which producers have private costs and the platform must learn the context-dependent value of selecting each producer. We evaluate performance by welfare regret: the cumulative loss in total surplus relative to the full-information efficient rule that knows the context-dependent values and true producer costs. The natural UCB allocation rule achieves widetilde O(√ngT) welfare regret under truthful bids, but its adaptive, bid-dependent learning path does not by itself ensure truthfulness. To obtain exact incentives, we first design a bid-independent explore-then-commit mechanism with empirical threshold payments; it is dominant-strategy truthful and has widetilde O((ng)1/3T2/3) regret. We then introduce frozen-payment UCB, which estimates payments from initial bid-independent exploration but continues allocation learning by UCB. Under a truthful-path margin condition, the frozen-payment UCB is approximately truthful with an average per-round deviation gain widetilde O(T-1/4) for fixed n, g. Under truthful bidding, it achieves widetilde O(√ngT) welfare regret, matching the UCB rate. A lower bound shows that this regret-incentive tradeoff is tight within the frozen critical-payment class.