Research map / Bandits and online learning / Online convex optimization and games
Risk-Averse No-Regret Learning in Online Convex Games
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Reuse samples from the previous iteration to estimate CVaR (ablation) | Risk-averse learning · Online convex games | CVaR estimation error: strictly less than that achieved without reusing previous samples | Helped |
| Reuse samples from the previous iteration (ablation) | Risk-averse learning · Cournot game | convergence speed | Helped |
| Use residual feedback in the CVaR gradient estimate (ablation) | Risk-averse learning · Cournot game | gradient-estimate variance | Helped |
| Use residual feedback in the CVaR gradient estimate (ablation) | Risk-averse learning · Cournot game | CVaR | No clear effect |
Closest papers by meaning
- Adaptive Sampling for Stochastic Risk-Averse Learning (NeurIPS 2020 · similarity 0.91)
- Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR (ICML 2023 · similarity 0.90)
- Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games (NeurIPS 2016 · similarity 0.89)
- Best of Both Worlds: Regret Minimization versus Minimax Play (ICML 2025 · similarity 0.89)
- No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation (NeurIPS 2022 · similarity 0.89)
- Adaptive Regret for Bandits Made Possible: Two Queries Suffice (ICLR 2024 · 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.