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Sparsity regret bounds for individual sequences in online linear regression
Published on Aug 02, 20113366 Views
We consider the problem of online linear regression on arbitrary deterministic sequences when the ambient dimension d can be much larger than the number of time rounds T. We introduce the notion of sp
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Chapter list
Sparsity regret bounds for individual sequences in online linear regression00:00
Introduction: sparsity in the stochastic setting00:08
Outline02:25
Introduction of the notion of sparsity regret bound03:02
Setting: online linear regression on individual sequences03:06
Prediction goal04:32
What about the high dimensions?05:47
Sparsity regret bounds06:43
Related works in online convex optimization08:02
Main results with individual sequences09:39
Algorithm: Sequential Sparse Exponential Weighting (SeqSEW)10:02
Known bound By on the observations12:02
Unknown bound By on the observations13:29
Adaptivity results with i.i.d. data15:06
Application to i.i.d. data15:15
Adaptivity to the unknown variance - 116:32
Adaptivity to the unknown variance - 217:06
Conclusion and ongoing work - 118:25
Conclusion and ongoing work - 219:14