# Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions

arXiv: https://arxiv.org/abs/1703.02567 · HTML: https://mlsanity.com/paper/1703.02567/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

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

- [No-regret Learning in Repeated First-Price Auctions with Budget Constraints](https://mlsanity.com/paper/2205.14572/) (2022)
- [Censored Semi-Bandits for Resource Allocation](https://mlsanity.com/paper/2104.05781/) (2021)
- [Large Language Models as Bidding Agents in Repeated HetNet Auction](https://mlsanity.com/paper/2603.04455/) (2026)
- [Instance-dependent Stochastic Lipschitz bandit](https://mlsanity.com/paper/2605.29748/) (2026)

## Abstract (arXiv)

We study the online learning problem of a bidder who participates in repeated auctions. With the goal of maximizing his T-period payoff, the bidder determines the optimal allocation of his budget among his bids for K goods at each period. As a bidding strategy, we propose a polynomial-time algorithm, inspired by the dynamic programming approach to the knapsack problem. The proposed algorithm, referred to as dynamic programming on discrete set (DPDS), achieves a regret order of O(√TlogT). By showing that the regret is lower bounded by Omega(√T) for any strategy, we conclude that DPDS is order optimal up to a √logT term. We evaluate the performance of DPDS empirically in the context of virtual trading in wholesale electricity markets by using historical data from the New York market. Empirical results show that DPDS consistently outperforms benchmark heuristic methods that are derived from machine learning and online learning approaches.