Machine Learning最新文献

Cross-network node classification aims to leverage the labeled nodes from a source network to assist the learning in a target network. Existing approaches work mainly in homogeneous settings, i.e., the nodes of the source and target networks are characterized by the same features. However, in many practical applications, nodes from different networks usually have heterogeneous features. To handle this issue, in this paper, we study the cross-network node classification under heterogeneous settings, i.e., cross-network heterogeneous node classification. Specifically, we propose a new model called High-order Proximity and Relation Analysis, which studies the high-order proximity in each network and the high-order relation between nodes across the networks to obtain two kinds of features. Subsequently, these features are exploited to learn the final effective representations by introducing a feature matching mechanism and an adversarial domain adaptation. We perform extensive experiments on several real-world datasets and make comparisons with existing baseline methods. Experimental results demonstrate the effectiveness of the proposed model.

Label propagation can rapidly predict the labels of unlabeled objects as the correct answers from a small amount of given label information, which can enhance the performance of subsequent machine learning tasks. Most existing label propagation methods are proposed for static data. However, in many applications, real datasets including multiple feature value types and massive unlabeled objects vary dynamically over time, whereas applying these label propagation methods for dynamic partially labeled hybrid data will be a huge drain due to recalculating from scratch when the data changes every time. To improve efficiency, a novel incremental label propagation algorithm based on neighborhood relation (ILPN) is developed in this paper. Specifically, we first construct graph structures by utilizing neighborhood relations to eliminate unnecessary label information. Then, a new label propagation strategy is designed in consideration of the weights assigned to each class so that it does not rely on a probabilistic transition matrix to fix the structure for propagation. On this basis, a new label propagation algorithm called neighborhood relation-based label propagation (LPN) is developed. For the dynamic partially labeled hybrid data, we integrate incremental learning into LPN and develop an updating mechanism that allows incremental label propagation over previous label propagation results and graph structures, rather than recalculating from scratch. Finally, extensive experiments on UCI datasets validate that our proposed algorithm LPN can outperform other label propagation algorithms in speed on the premise of ensuring accuracy. Especially for simulated dynamic data, the incremental algorithm ILPN is more efficient than other non-incremental methods with the variation of the partially labeled hybrid data.

Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks. Existing methods for detecting adversarial attacks on object detectors have had difficulty detecting new real-life attacks. We present X-Detect, a novel adversarial patch detector that can: (1) detect adversarial samples in real time, allowing the defender to take preventive action; (2) provide explanations for the alerts raised to support the defender’s decision-making process, and (3) handle unfamiliar threats in the form of new attacks. Given a new scene, X-Detect uses an ensemble of explainable-by-design detectors that utilize object extraction, scene manipulation, and feature transformation techniques to determine whether an alert needs to be raised. X-Detect was evaluated in both the physical and digital space using five different attack scenarios (including adaptive attacks) and the benchmark COCO dataset and our new Superstore dataset. The physical evaluation was performed using a smart shopping cart setup in real-world settings and included 17 adversarial patch attacks recorded in 1700 adversarial videos. The results showed that X-Detect outperforms the state-of-the-art methods in distinguishing between benign and adversarial scenes for all attack scenarios while maintaining a 0% FPR (no false alarms) and providing actionable explanations for the alerts raised. A demo is available.

Maximum Variance Unfolding (MVU) is among the first methods in nonlinear dimensionality reduction for data visualization and classification. It aims to preserve local data structure and in the meantime push the variance among data as big as possible. However, MVU in general remains a computationally challenging problem and this may explain why it is less popular than other leading methods such as Isomap and t-SNE. In this paper, based on a key observation that the structure-preserving term in MVU is actually the squared stress in Multi-Dimensional Scaling (MDS), we replace the term with the stress function from MDS, resulting in a model that is usable. The property of the usability guarantees the “crowding phenomenon” will not happen in the dimension reduced results. The new model also allows us to combine label information and hence we call it the supervised MVU (SMVU). We then develop a fast algorithm that is based on Euclidean distance matrix optimization. By making use of the majorization-mininmization technique, the algorithm at each iteration solves a number of one-dimensional optimization problems, each having a closed-form solution. This strategy significantly speeds up the computation. We demonstrate the advantage of SMVU on some standard data sets against a few leading algorithms including Isomap and t-SNE.

We study the interplay between the data distribution and Q-learning-based algorithms with function approximation. We provide a unified theoretical and empirical analysis as to how different properties of the data distribution influence the performance of Q-learning-based algorithms. We connect different lines of research, as well as validate and extend previous results, being primarily focused on offline settings. First, we analyze the impact of the data distribution by using optimization as a tool to better understand which data distributions yield low concentrability coefficients. We motivate high-entropy distributions from a game-theoretical point of view and propose an algorithm to find the optimal data distribution from the point of view of concentrability. Second, from an empirical perspective, we introduce a novel four-state MDP specifically tailored to highlight the impact of the data distribution in the performance of Q-learning-based algorithms with function approximation. Finally, we experimentally assess the impact of the data distribution properties on the performance of two offline Q-learning-based algorithms under different environments. Our results attest to the importance of different properties of the data distribution such as entropy, coverage, and data quality (closeness to optimal policy).

Partially Observable Markov Decision Processes (POMDPs) can model complex sequential decision-making problems under stochastic and uncertain environments. A main reason hindering their broad adoption in real-world applications is the unavailability of a suitable POMDP model or a simulator thereof. Available solution algorithms, such as Reinforcement Learning (RL), typically benefit from the knowledge of the transition dynamics and the observation generating process, which are often unknown and non-trivial to infer. In this work, we propose a combined framework for inference and robust solution of POMDPs via deep RL. First, all transition and observation model parameters are jointly inferred via Markov Chain Monte Carlo sampling of a hidden Markov model, which is conditioned on actions, in order to recover full posterior distributions from the available data. The POMDP with uncertain parameters is then solved via deep RL techniques with the parameter distributions incorporated into the solution via domain randomization, in order to develop solutions that are robust to model uncertainty. As a further contribution, we compare the use of Transformers and long short-term memory networks, which constitute model-free RL solutions and work directly on the observation space, with an approach termed the belief-input method, which works on the belief space by exploiting the learned POMDP model for belief inference. We apply these methods to the real-world problem of optimal maintenance planning for railway assets and compare the results with the current real-life policy. We show that the RL policy learned by the belief-input method is able to outperform the real-life policy by yielding significantly reduced life-cycle costs.

We introduce a novel online (or sequential) nonlinear prediction approach that incorporates the residuals, i.e., prediction errors in the past observations, as additional features for the current data. Including the past error terms in an online prediction algorithm naturally improves prediction performance significantly since this information is essential for an algorithm to adjust itself based on its past errors. These terms are well exploited in many linear statistical models such as ARMA, SES, and Holts-Winters models. However, the past error terms are rarely or in a certain sense not optimally exploited in nonlinear prediction models since training them requires complex nonlinear state-space modeling. To this end, for the first time in the literature, we introduce a nonlinear prediction framework that utilizes not only the current features but also the past error terms as additional features, thereby exploiting the residual state information in the error terms, i.e., the model’s performance on the past samples. Since the new feature vectors contain error terms that change with every update, our algorithm jointly optimizes the model parameters and the feature vectors simultaneously. We achieve this by introducing new update equations that handle the effects resulting from the changes in the feature vectors in an online manner. We use soft decision trees and neural networks as the nonlinear prediction algorithms since these are the most widely used methods in highly publicized competitions. However, as we show, our methods are generic and any algorithm supporting gradient calculations can be straightforwardly used. We show through our experiments on the well-known real-life competition datasets that our method significantly outperforms the state-of-the-art. We also provide the implementation of our approach including the source code to facilitate reproducibility (https://github.com/ahmetberkerkoc/SDT-ARMA).

This article proposes a meta-learning method for estimating the conditional average treatment effect (CATE) from a few observational data. The proposed method learns how to estimate CATEs from multiple tasks and uses the knowledge for unseen tasks. In the proposed method, based on the meta-learner framework, we decompose the CATE estimation problem into sub-problems. For each sub-problem, we formulate our estimation models using neural networks with task-shared and task-specific parameters. With our formulation, we can obtain optimal task-specific parameters in a closed form that are differentiable with respect to task-shared parameters, making it possible to perform effective meta-learning. The task-shared parameters are trained such that the expected CATE estimation performance in few-shot settings is improved by minimizing the difference between a CATE estimated with a large amount of data and one estimated with just a few data. Our experimental results demonstrate that our method outperforms the existing meta-learning approaches and CATE estimation methods.

Ordinary differential equations (ODEs) are a widely used formalism for the mathematical modeling of dynamical systems, a task omnipresent in scientific domains. The paper introduces a novel method for inferring ODEs from data, which extends ProGED, a method for equation discovery that allows users to formalize domain-specific knowledge as probabilistic context-free grammars and use it for constraining the space of candidate equations. The extended method can discover ODEs from partial observations of dynamical systems, where only a subset of state variables can be observed. To evaluate the performance of the newly proposed method, we perform a systematic empirical comparison with alternative state-of-the-art methods for equation discovery and system identification from complete and partial observations. The comparison uses Dynobench, a set of ten dynamical systems that extends the standard Strogatz benchmark. We compare the ability of the considered methods to reconstruct the known ODEs from synthetic data simulated at different temporal resolutions. We also consider data with different levels of noise, i.e., signal-to-noise ratios. The improved ProGED compares favourably to state-of-the-art methods for inferring ODEs from data regarding reconstruction abilities and robustness to data coarseness, noise, and completeness.

Feature attribution maps are a popular approach to highlight the most important pixels in an image for a given prediction of a model. Despite a recent growth in popularity and available methods, the objective evaluation of such attribution maps remains an open problem. Building on previous work in this domain, we investigate existing quality metrics and propose new variants of metrics for the evaluation of attribution maps. We confirm a recent finding that different quality metrics seem to measure different underlying properties of attribution maps, and extend this finding to a larger selection of attribution methods, quality metrics, and datasets. We also find that metric results on one dataset do not necessarily generalize to other datasets, and methods with desirable theoretical properties do not necessarily outperform computationally cheaper alternatives in practice. Based on these findings, we propose a general benchmarking approach to help guide the selection of attribution methods for a given use case. Implementations of attribution metrics and our experiments are available online (https://github.com/arnegevaert/benchmark-general-imaging).

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