Research map / Bandits and online learning / Online convex optimization and games
Distributed Linear Bandits under Communication Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Progressively learn and share reward-vector information one bit per coordinate at a time | Distributed linear bandits with PLS · Distributed linear bandits | cumulative regret: \mathcal{O}(d\sqrt{MT}) | Helped |
| Progressively learn and share reward-vector information one bit per coordinate at a time | Distributed linear bandits with PLS · Distributed linear bandits | uplink communication: \mathcal{O}(d \log T) | Helped |
| Progressively learn and share reward-vector information one bit per coordinate at a time | Distributed linear bandits with PLS · Distributed linear bandits | downlink communication: \mathcal{O}(d(\log M + \log T)) | Helped |
| Replace orthonormal-basis exploration with sparse random projections and use server-side LASSO (ablation) | PLS · Sparse distributed linear bandits | cumulative regret: \mathcal{O}(d\sqrt{MT}) -> \sqrt{sdMT} | Helped |
| Replace orthonormal-basis exploration with sparse random projections and use server-side LASSO (ablation) | PLS · Sparse distributed linear bandits | uplink communication: \mathcal{O}(d \log T) -> \mathcal{O}(s \log T) | Helped |
Closest papers by meaning
- Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication (ICLR 2020 · similarity 0.95)
- Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost (ICML 2023 · similarity 0.93)
- Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits (AAAI 2024 · similarity 0.91)
- Individual Regret in Cooperative Stochastic Multi-Armed Bandits (NeurIPS 2025 · similarity 0.91)
- Distributed Multi-Agent Bandits Over Erdős-Rényi Random Networks (NeurIPS 2025 · similarity 0.90)
- Batched Stochastic Linear Bandits with 1-Bit Communication Constraints (NeurIPS 2026 · similarity 0.90)
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