16 research outputs found
Federated Multi-Level Optimization over Decentralized Networks
Multi-level optimization has gained increasing attention in recent years, as
it provides a powerful framework for solving complex optimization problems that
arise in many fields, such as meta-learning, multi-player games, reinforcement
learning, and nested composition optimization. In this paper, we study the
problem of distributed multi-level optimization over a network, where agents
can only communicate with their immediate neighbors. This setting is motivated
by the need for distributed optimization in large-scale systems, where
centralized optimization may not be practical or feasible. To address this
problem, we propose a novel gossip-based distributed multi-level optimization
algorithm that enables networked agents to solve optimization problems at
different levels in a single timescale and share information through network
propagation. Our algorithm achieves optimal sample complexity, scaling linearly
with the network size, and demonstrates state-of-the-art performance on various
applications, including hyper-parameter tuning, decentralized reinforcement
learning, and risk-averse optimization.Comment: arXiv admin note: substantial text overlap with arXiv:2206.1087
Multi-Level Stochastic Gradient Methods for Nested Composition Optimization
Stochastic gradient methods are scalable for solving large-scale optimization
problems that involve empirical expectations of loss functions. Existing
results mainly apply to optimization problems where the objectives are one- or
two-level expectations. In this paper, we consider the multi-level
compositional optimization problem that involves compositions of multi-level
component functions and nested expectations over a random path. It finds
applications in risk-averse optimization and sequential planning. We propose a
class of multi-level stochastic gradient methods that are motivated from the
method of multi-timescale stochastic approximation. First we propose a basic
-level stochastic compositional gradient algorithm, establish its almost
sure convergence and obtain an -iteration error bound . Then
we develop accelerated multi-level stochastic gradient methods by using an
extrapolation-interpolation scheme to take advantage of the smoothness of
individual component functions. When all component functions are smooth, we
show that the convergence rate improves to for general
objectives and for strongly convex objectives. We also
provide almost sure convergence and rate of convergence results for nonconvex
problems. The proposed methods and theoretical results are validated using
numerical experiments
Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection
Millimeter-wave (MMW) imaging is emerging as a promising technique for safe
security inspection. It achieves a delicate balance between imaging resolution,
penetrability and human safety, resulting in higher resolution compared to
low-frequency microwave, stronger penetrability compared to visible light, and
stronger safety compared to X ray. Despite of recent advance in the last
decades, the high cost of requisite large-scale antenna array hinders
widespread adoption of MMW imaging in practice. To tackle this challenge, we
report a large-scale single-shot MMW imaging framework using sparse antenna
array, achieving low-cost but high-fidelity security inspection under an
interpretable learning scheme. We first collected extensive full-sampled MMW
echoes to study the statistical ranking of each element in the large-scale
array. These elements are then sampled based on the ranking, building the
experimentally optimal sparse sampling strategy that reduces the cost of
antenna array by up to one order of magnitude. Additionally, we derived an
untrained interpretable learning scheme, which realizes robust and accurate
image reconstruction from sparsely sampled echoes. Last, we developed a neural
network for automatic object detection, and experimentally demonstrated
successful detection of concealed centimeter-sized targets using 10% sparse
array, whereas all the other contemporary approaches failed at the same sample
sampling ratio. The performance of the reported technique presents higher than
50% superiority over the existing MMW imaging schemes on various metrics
including precision, recall, and mAP50. With such strong detection ability and
order-of-magnitude cost reduction, we anticipate that this technique provides a
practical way for large-scale single-shot MMW imaging, and could advocate its
further practical applications
Optimality Conditions and Algorithms for Top-K Arm Identification
We consider the top-k arm identification problem for multi-armed bandits with
rewards belonging to a one-parameter canonical exponential family. The
objective is to select the set of k arms with the highest mean rewards by
sequential allocation of sampling efforts. We propose a unified optimal
allocation problem that identifies the complexity measures of this problem
under the fixed-confidence, fixed-budget settings, and the posterior
convergence rate from the Bayesian perspective. We provide the first
characterization of its optimality. We provide the first provably optimal
algorithm in the fixed-confidence setting for k>1. We also propose an efficient
heuristic algorithm for the top-k arm identification problem. Extensive
numerical experiments demonstrate superior performance compare to existing
methods in all three settings
Through-the-Wall Micro-Doppler De-Wiring Technique via Cycle-Consistent Adversarial Network
The radar penetrating technique has aroused a keen interest in the research community, due to its superior abilities for through-the-wall indoor human motion monitoring. Micro-Doppler signatures in this situation play a significant role in recognition and classification for human activities. However, the live wire buried in the wall introduces additive clutters to the spectrograms. Such degraded spectrograms drastically affect the performance of behind-the-wall human activity detection. In this paper, an ultra-wideband (UWB) radar system is utilized in the through-the-wall scenario to get the feature enhanced micro-Doppler signature called range-max time-frequency representation (R-max TFR). Then, a recently introduced Cycle-Consistent Generative Adversarial Network (Cycle GAN) is employed to realize the end-to-end de-wiring task. Cycle GAN can learn the mapping between spectrograms with and without the live wire effect. To minimize the wiring clutters, a loss function called identity loss is introduced in this work. Finally, the proposed de-wiring approach is evaluated through classification. The results show that the proposed Cycle GAN architecture outperforms other state-of-art de-wiring methods
Through-the-Wall Micro-Doppler De-Wiring Technique via Cycle-Consistent Adversarial Network
The radar penetrating technique has aroused a keen interest in the research community, due to its superior abilities for through-the-wall indoor human motion monitoring. Micro-Doppler signatures in this situation play a significant role in recognition and classification for human activities. However, the live wire buried in the wall introduces additive clutters to the spectrograms. Such degraded spectrograms drastically affect the performance of behind-the-wall human activity detection. In this paper, an ultra-wideband (UWB) radar system is utilized in the through-the-wall scenario to get the feature enhanced micro-Doppler signature called range-max time-frequency representation (R-max TFR). Then, a recently introduced Cycle-Consistent Generative Adversarial Network (Cycle GAN) is employed to realize the end-to-end de-wiring task. Cycle GAN can learn the mapping between spectrograms with and without the live wire effect. To minimize the wiring clutters, a loss function called identity loss is introduced in this work. Finally, the proposed de-wiring approach is evaluated through classification. The results show that the proposed Cycle GAN architecture outperforms other state-of-art de-wiring methods