Proposes QTERM for quantum process learning, but the proof rests on an incorrect equality E[e^{γY}] = e^{γE[Y]} for measurement bits, invalidating the sample complexity and PAC claims.
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
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abstract
We analyze a family of supervised learning algorithms based on sample compression schemes that are stable, in the sense that removing points from the training set which were not selected for the compression set does not alter the resulting classifier. We use this technique to derive a variety of novel or improved data-dependent generalization bounds for several learning algorithms. In particular, we prove a new margin bound for SVM, removing a log factor. The new bound is provably optimal. This resolves a long-standing open question about the PAC margin bounds achievable by SVM.
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Quantum Learning with Tunable Loss Functions
Proposes QTERM for quantum process learning, but the proof rests on an incorrect equality E[e^{γY}] = e^{γE[Y]} for measurement bits, invalidating the sample complexity and PAC claims.