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Escaping Saddle-Point Faster under Interpolation-like Conditions
Abhishek Roy · Krishnakumar Balasubramanian · Saeed Ghadimi · Prasant Mohapatra

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1882
In this paper, we show that under over-parametrization several standard stochastic optimization algorithms escape saddle-points and converge to local-minimizers much faster. One of the fundamental aspects of over-parametrized models is that they are capable of interpolating the training data. We show that, under interpolation-like assumptions satisfied by the stochastic gradients in an over-parametrization setting, the first-order oracle complexity of Perturbed Stochastic Gradient Descent (PSGD) algorithm to reach an $\epsilon$-local-minimizer, matches the corresponding deterministic rate of $O(1/\epsilon^{2})$. We next analyze Stochastic Cubic-Regularized Newton (SCRN) algorithm under interpolation-like conditions, and show that the oracle complexity to reach an $\epsilon$-local-minimizer under interpolation-like conditions, is $O(1/\epsilon^{2.5})$. While this obtained complexity is better than the corresponding complexity of either PSGD, or SCRN without interpolation-like assumptions, it does not match the rate of $O(1/\epsilon^{1.5})$ corresponding to deterministic Cubic-Regularized Newton method. It seems further Hessian-based interpolation-like assumptions are necessary to bridge this gap. We also discuss the corresponding improved complexities in the zeroth-order settings.

Author Information

Abhishek Roy (University of California, Davis)
Krishnakumar Balasubramanian (University of California, Davis)
Saeed Ghadimi (University of Waterloo)
Prasant Mohapatra (University of California, Davis)

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