Author name cluster

Costas Spanos

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

NeurIPS Conference 2024 Conference Paper

Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation

Shangding Gu
Laixi Shi
Yuhao Ding
Alois Knoll
Costas Spanos
Adam Wierman
Ming Jin

Safe reinforcement learning (RL) is crucial for deploying RL agents in real-world applications, as it aims to maximize long-term rewards while satisfying safety constraints. However, safe RL often suffers from sample inefficiency, requiring extensive interactions with the environment to learn a safe policy. We propose Efficient Safe Policy Optimization (ESPO), a novel approach that enhances the efficiency of safe RL through sample manipulation. ESPO employs an optimization framework with three modes: maximizing rewards, minimizing costs, and balancing the trade-off between the two. By dynamically adjusting the sampling process based on the observed conflict between reward and safety gradients, ESPO theoretically guarantees convergence, optimization stability, and improved sample complexity bounds. Experiments on the Safety-MuJoCo and Omnisafe benchmarks demonstrate that ESPO significantly outperforms existing primal-based and primal-dual-based baselines in terms of reward maximization and constraint satisfaction. Moreover, ESPO achieves substantial gains in sample efficiency, requiring 25--29\% fewer samples than baselines, and reduces training time by 21--38\%.

PDF Details DOI

AAAI Conference 2018 Conference Paper

Non-Parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis

Yuxun Zhou
Han Zou
Reza Arghandeh
Weixi Gu
Costas Spanos

In this study we consider the problem of outlier detection with multiple co-evolving time series data. To capture both the temporal dependence and the inter-series relatedness, a multi-task non-parametric model is proposed, which can be extended to data with a broader exponential family distribution by adopting the notion of Bregman divergence. Albeit convex, the learning problem can be hard as the time series accumulate. In this regards, an efﬁcient randomized block coordinate descent (RBCD) algorithm is proposed. The model and the algorithm is tested with a real-world application, involving outlier detection and event analysis in power distribution networks with high resolution multi-stream measurements. It is shown that the incorporation of inter-series relatedness enables the detection of system level events which would otherwise be unobservable with traditional methods.

PDF Details

AAAI Conference 2018 Conference Paper

WiFi-Based Human Identification via Convex Tensor Shapelet Learning

Han Zou
Yuxun Zhou
Jianfei Yang
Weixi Gu
Lihua Xie
Costas Spanos

We propose AutoID, a human identiﬁcation system that leverages the measurements from existing WiFi-enabled Internet of Things (IoT) devices and produces the identity estimation via a novel sparse representation learning technique. The key idea is to use the unique ﬁne-grained gait patterns of each person revealed from the WiFi Channel State Information (CSI) measurements, technically referred to as shapelet signatures, as the ”ﬁngerprint” for human identiﬁcation. For this purpose, a novel OpenWrt-based IoT platform is designed to collect CSI data from commercial IoT devices. More importantly, we propose a new optimization-based shapelet learning framework for tensors, namely Convex Clustered Concurrent Shapelet Learning (C3 SL), which formulates the learning problem as a convex optimization. The global solution of C3 SL can be obtained efﬁciently with a generalized gradient-based algorithm, and the three concurrent regularization terms reveal the inter-dependence and the clustering effect of the CSI tensor data. Extensive experiments are conducted in multiple real-world indoor environments, showing that AutoID achieves an average human identiﬁcation accuracy of 91% from a group of 20 people. As a combination of novel sensing and learning platform, AutoID attains substantial progress towards a more accurate, cost-effective and sustainable human identiﬁcation system for pervasive implementations.

PDF Details

AAAI Conference 2017 Conference Paper

Parametric Dual Maximization for Non-Convex Learning Problems

Yuxun Zhou
Zhaoyi Kang
Costas Spanos

We consider a class of non-convex learning problems that can be formulated as jointly optimizing regularized hinge loss and a set of auxiliary variables. Such problems encompass but are not limited to various versions of semi-supervised learning, learning with hidden structures, robust learning, etc. Existing methods either suffer from local minima or have to invoke a non-scalable combinatorial search. In this paper, we propose a novel learning procedure, namely Parametric Dual Maximization (PDM), that can approach global optimality efﬁciently with user speciﬁed approximation levels. The building blocks of PDM are two new results: (1) The equivalent convex maximization reformulation derived by parametric analysis. (2) The improvement of local solutions based on a necessary and sufﬁcient condition for global optimality. Experimental results on two representative applications demonstrate the effectiveness of PDM compared to other approaches.

PDF Details

NeurIPS Conference 2016 Conference Paper

Causal meets Submodular: Subset Selection with Directed Information

Yuxun Zhou
Costas Spanos

We study causal subset selection with Directed Information as the measure of prediction causality. Two typical tasks, causal sensor placement and covariate selection, are correspondingly formulated into cardinality constrained directed information maximizations. To attack the NP-hard problems, we show that the first problem is submodular while not necessarily monotonic. And the second one is ``nearly'' submodular. To substantiate the idea of approximate submodularity, we introduce a novel quantity, namely submodularity index (SmI), for general set functions. Moreover, we show that based on SmI, greedy algorithm has performance guarantee for the maximization of possibly non-monotonic and non-submodular functions, justifying its usage for a much broader class of problems. We evaluate the theoretical results with several case studies, and also illustrate the application of the subset selection to causal structure learning.

PDF Details

AAAI Conference 2016 Conference Paper

Veto-Consensus Multiple Kernel Learning

Yuxun Zhou
Ninghang Hu
Costas Spanos

We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The proposed conﬁguration is a natural ﬁt for domain description and learning with hidden subgroups. We ﬁrst provide generalization risk bound in terms of the Rademacher complexity of the classiﬁer, then a large margin multi-ν learning objective with tunable training error bound is formulated. Seeing that the corresponding optimization is non-convex and existing methods severely suffer from local minima, we establish a new algorithm, namely Parametric Dual Descent Procedure(PDDP), that can approach global optimum with guarantees. The bases of PDDP are two theorems that reveal the global convexity and local explicitness of the parameterized dual optimum, for which a series of new techniques for parametric program have been developed. The proposed method is evaluated on extensive set of experiments, and the results show signiﬁcant improvement over the state-of-the-art approaches.

PDF Details