# Learning to Bid in Discriminatory Auctions with Budget Constraints

arXiv: https://arxiv.org/abs/2606.29252 · HTML: https://mlsanity.com/paper/2606.29252/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 75 references that are in the map.
- First posted on arXiv 28 Jun 2026.

## Builds on (most cited first, 1 of 1)

- [A New Benchmark for Online Learning with Budget-Balancing Constraints](https://mlsanity.com/paper/2503.14796/) (2025)

## Abstract (arXiv)

We study repeated bidding in multi-unit discriminatory (pay-as-bid) auctions for a single bidder with per-round utility equal to value minus α times payment, where α∈[0,1] is a cost-of-capital parameter. The bidder aims to maximize cumulative utility over T rounds subject to a total budget B. The problem is challenging even without budgets: the action space is exponential in M, the maximum demand of the bidder and the valuation vector (context) varies over time. Exploiting a decomposition of utility across units, we develop polynomial-time learning algorithms based on shortest paths in a directed acyclic graph, obtaining sublinear regret under both full-information and bandit feedback. In the bandit setting, the regret is independent of the number of contexts due to complete cross-learning: observing the utility of the chosen action under the realized context reveals the utility for the same action under all counterfactual contexts. With budget constraints, when the average normalized per-round budget ρ=fracBMT<1, we design a coupled primal-dual algorithm in which the DAG-based procedure uses dual-adjusted edge weights for primal updates, while online gradient descent updates the dual variable, yielding ρ-approximate sublinear regret. Finally, we give implementations whose per-round time and space are independent of the number of contexts, enabling scalability to large or even infinite context spaces.