Research map / Bandits and online learning
Online convex optimization and games: research map
952 accepted papers on Online convex optimization and games in Bandits and online learning, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 3 clusters and 13 approaches. The busiest year so far is 2026.
Within Bandits and online learning, its share grew from 41.1% in 2023–24 to 46.5% in 2025–26 (229 → 276 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
price · pricing · auctions · 648 papers
Approaches in this cluster:
- Online learning with feedback graphs (155 papers)
Design best-of-both-worlds experts and bandit algorithms with structured feedback and limited information. - Best-of-both-worlds bandit algorithms (125 papers)
Obtain optimal regret in both stochastic and adversarial regimes for linear and semi-bandits. - Learning for pricing and auctions (101 papers)
Develop regret-minimizing pricing and bidding algorithms for repeated auctions with strategic buyers. - Online allocation with constraints (77 papers)
Solve online allocation and knapsack problems using dual mirror descent and primal-dual methods. - Online decision-making with feedback structure (81 papers)
Analyze online prediction under delayed, bandit, strategic, or dynamical feedback. - Online linear-quadratic control regret (36 papers)
Learn linear-quadratic regulators online with regret guarantees. - Learning-augmented online algorithms (73 papers)
Use machine-learned predictions to improve online algorithms while preserving worst-case robustness.
Most cited and most cited since 2024:
- Online Meta-Learning (ICML 2019 · 93 citations)
- Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions (NeurIPS 2019 · 71 citations)
- Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound (AAAI 2024 · 4 citations)
- Dynamic Budget Throttling in Repeated Second-Price Auctions (AAAI 2024 · 4 citations)
convex optimization · online convex · functions · 187 papers
Approaches in this cluster:
- Constrained and unconstrained OCO algorithms (85 papers)
Design online convex optimization algorithms handling constraints, unbounded domains, and switching costs. - Dynamic and adaptive regret (57 papers)
Reduce and bound dynamic and adaptive regret for convex and smooth losses. - Parameter-free non-stationary online learning (45 papers)
Achieve universal, parameter-free guarantees in changing environments through meta-algorithm ensembles.
Most cited and most cited since 2024:
- Adaptive Algorithms for Online Convex Optimization with Long-term Constraints (ICML 2016 · 82 citations)
- Online convex optimization for cumulative constraints (NeurIPS 2018 · 72 citations)
- Optimal Algorithms for Online Convex Optimization with Adversarial Constraints (NeurIPS 2024 · 7 citations)
- Online Submodular Maximization via Online Convex Optimization (AAAI 2024 · 5 citations)
game · equilibrium · convergence · 117 papers
Approaches in this cluster:
- Last-iterate convergence in games (65 papers)
Prove convergence of uncoupled no-regret dynamics in zero-sum, monotone, and potential games. - Fast no-regret dynamics in games (37 papers)
Obtain near-constant regret and fast convergence via optimism and swap-regret analysis. - Counterfactual regret minimization (15 papers)
Improve CFR for extensive-form games with neural approximation and learned algorithm design.
Most cited and most cited since 2024:
- Learning with Bandit Feedback in Potential Games (NeurIPS 2017 · 53 citations)
- Learning in Games: Robustness of Fast Convergence (NeurIPS 2016 · 44 citations)
- Learning Not to Regret (AAAI 2024 · 2 citations)
- Optimism Without Regularization: Constant Regret in Zero-Sum Games (NeurIPS 2025 · 1 citations)