Research map / Bandits and online learning / Online convex optimization and games
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Beyond Monotonicity: On the Convergence of Learning Algorithms in Standard Auction Games (AAAI 2025 · similarity 0.92)
- Learning Bayesian Nash Equilibrium in Auction Games via Approximate Best Response (ICML 2025 · similarity 0.91)
- Randomized Truthful Auctions with Learning Agents (NeurIPS 2024 · similarity 0.90)
- Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization (NeurIPS 2025 · similarity 0.89)
- Computing Perfect Bayesian Equilibria in Sequential Auctions with Verification (AAAI 2025 · similarity 0.89)
- Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions (ICML 2025 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
