467 research outputs found
Second-Order Stochastic Optimization for Machine Learning in Linear Time
First-order stochastic methods are the state-of-the-art in large-scale
machine learning optimization owing to efficient per-iteration complexity.
Second-order methods, while able to provide faster convergence, have been much
less explored due to the high cost of computing the second-order information.
In this paper we develop second-order stochastic methods for optimization
problems in machine learning that match the per-iteration cost of gradient
based methods, and in certain settings improve upon the overall running time
over popular first-order methods. Furthermore, our algorithm has the desirable
property of being implementable in time linear in the sparsity of the input
data
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
We analyze the convergence rate of the randomized Newton-like method
introduced by Qu et. al. (2016) for smooth and convex objectives, which uses
random coordinate blocks of a Hessian-over-approximation matrix \bM instead
of the true Hessian. The convergence analysis of the algorithm is challenging
because of its complex dependence on the structure of \bM. However, we show
that when the coordinate blocks are sampled with probability proportional to
their determinant, the convergence rate depends solely on the eigenvalue
distribution of matrix \bM, and has an analytically tractable form. To do so,
we derive a fundamental new expectation formula for determinantal point
processes. We show that determinantal sampling allows us to reason about the
optimal subset size of blocks in terms of the spectrum of \bM. Additionally,
we provide a numerical evaluation of our analysis, demonstrating cases where
determinantal sampling is superior or on par with uniform sampling
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design
Aircraft industry is constantly striving for more efficient design
optimization methods in terms of human efforts, computation time, and resource
consumption. Hybrid surrogate optimization maintains high results quality while
providing rapid design assessments when both the surrogate model and the switch
mechanism for eventually transitioning to the HF model are calibrated properly.
Feedforward neural networks (FNNs) can capture highly nonlinear input-output
mappings, yielding efficient surrogates for aircraft performance factors.
However, FNNs often fail to generalize over the out-of-distribution (OOD)
samples, which hinders their adoption in critical aircraft design optimization.
Through SmOOD, our smoothness-based out-of-distribution detection approach, we
propose to codesign a model-dependent OOD indicator with the optimized FNN
surrogate, to produce a trustworthy surrogate model with selective but credible
predictions. Unlike conventional uncertainty-grounded methods, SmOOD exploits
inherent smoothness properties of the HF simulations to effectively expose OODs
through revealing their suspicious sensitivities, thereby avoiding
over-confident uncertainty estimates on OOD samples. By using SmOOD, only
high-risk OOD inputs are forwarded to the HF model for re-evaluation, leading
to more accurate results at a low overhead cost. Three aircraft performance
models are investigated. Results show that FNN-based surrogates outperform
their Gaussian Process counterparts in terms of predictive performance.
Moreover, SmOOD does cover averagely 85% of actual OODs on all the study cases.
When SmOOD plus FNN surrogates are deployed in hybrid surrogate optimization
settings, they result in a decrease error rate of 34.65% and a computational
speed up rate of 58.36 times, respectively
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