Research map / Bandits and online learning / Online convex optimization and games
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Assume the feature matrix satisfies RIP and use randomized feature sampling with a bias-corrected Dantzig selector | Online sparse linear regression in the realizable model · POSLR, realizable | regret: \mathcal{O} \left( k^2 \log(d/\delta) (d/{k_0})^3 \log(T) \right) | Helped |
| Assume bounded block restricted condition number and use online greedy feature selection with BEXP and VAW | Online sparse linear regression in the agnostic setting · POSLR, agnostic | expected regret: \tilde{O}((\frac{\kappa^8 dk^4}{{k_0}})^{1/3}T^{2/3}) | Helped |
Closest papers by meaning
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