State-of-the-art federated learning adopts the centralized network architecture where a centralized node collects the gradients sent from child agents to update the global model. Despite its simplicity, the centralized method suffers from communication and computational bottlenecks in the central node, especially for federated learning, where a large number of clients are usually involved. Moreover, to prevent reverse engineering of the user's identity, a certain amount of noise must be added to the gradient to protect user privacy, which partially sacrifices the efficiency and the accuracy (Shokri and Shmatikov, 2015). To further protect the data privacy and avoid the communication bottleneck, the decentralized architecture has been recently proposed (Vanhaesebrouck et al., 2017; Bellet et al., 2018), where the centralized node has been removed, and each node only communicates with its neighbors (with mutual trust) by exchanging their local models. Exchanging local models is usually favored with respect to the data privacy protection over sending private gradients because the local model is the aggregation or mixture of quite a large amount of data while the local gradient directly reflects only one or a batch of private data samples. Although advantages of decentralized architecture have been well recognized over the state-of-the-art method (its centralized counterpart), it usually can only be run on the network with mutual trusts . That is, two nodes (or users) can exchange their local models only if they trust each other reciprocally (e.g.

A fundamental problem in computer vision is image segmentation, where the goal is to delineate the boundary of the object in the image. The focus of this work is on the segmentation of grayscale images and its purpose is two-fold. First, we conduct an in-depth study comparing active contour and topologically-based methods, two popular approaches for boundary detection of 2-dimensional images. Certain properties of the image dataset may favor one method over the other, both from an interpretability perspective as well as through evaluation of performance measures. Second, we propose the use of topological knowledge to assist an active contour method, which can potentially incorporate prior shape information. The latter is known to be extremely sensitive to algorithm initialization, and thus, we use a topological model to provide an automatic initialization. In addition, our proposed model can handle objects in images with more complex topological structures. We demonstrate this on artificially-constructed image datasets from computer vision, as well as real medical image data.

Textual network embedding aims to learn low-dimensional representations of text-annotated nodes in a graph. Prior works have typically focused on fixed graph structures. However, real-world networks are often dynamic. We address this challenge with a novel end-to-end node-embedding model, called Dynamic Embedding for Textual Networks with a Gaussian Process (DetGP). Because the structure is allowed to be dynamic, our method uses the Gaussian process to take advantage of its non-parametric properties. After training, DetGP can be applied efficiently to dynamic graphs without re-training or backpropagation. To use both local and global graph structures, diffusion is used to model multiple hops between neighbors. The relative importance of global versus local structure for the embeddings is learned automatically. With the non-parametric nature of the Gaussian process, updating the embeddings for a changed graph structure requires only a forward pass through the learned model. Experiments demonstrate the empirical effectiveness of our method compared to baseline approaches, on link prediction and node classification. We further show that DetGP can be straightforwardly and efficiently applied to dynamic textual networks.

Neural backdoor attack is emerging as a severe security threat to deep learning, while the capability of existing defense methods is limited, especially for complex backdoor triggers. In the work, we explore the space formed by the pixel values of all possible backdoor triggers. An original trigger used by an attacker to build the backdoored model represents only a point in the space. It then will be generalized into a distribution of valid triggers, all of which can influence the backdoored model. Thus, previous methods that model only one point of the trigger distribution is not sufficient. Getting the entire trigger distribution, e.g., via generative modeling, is a key to effective defense. However, existing generative modeling techniques for image generation are not applicable to the backdoor scenario as the trigger distribution is completely unknown. In this work, we propose max-entropy staircase approximator (MESA), an algorithm for high-dimensional sampling-free generative modeling and use it to recover the trigger distribution. We also develop a defense technique to remove the triggers from the backdoored model. Our experiments on Cifar10 dataset demonstrate the effectiveness of MESA in modeling the trigger distribution and the robustness of the proposed defense method.

Ensemble methods (Breiman, 1996; Amit and Geman, 1997; Josse and Wager, 2016) are an oft-used strategy used successfully in a broad range of problems in machine learning and statistics, in which one combines a number of weak predictors together to obtain one powerful predictor. This is accomplished by giving each weak learner a different view of the training data. Various strategies for changing this training data view exist, among which many are simple sampling-based techniques in which each predictor is (independently) given access to a subsampling the rows (examples) and columns (features) of the training data matrix, such as bagging (Breiman, 1996; B uhlmann and Yu, 2002). Another noteworthy technique is boosting (Freund and Schapire, 1997; Breiman, 1998), in which the training data examples are reweighted adaptively according to how badly they have been misclassified while buliding the ensemble. In this work, we consider the former class of techniques--those that train each weak predictor using an independent subsampling of the training data. Ensemble methods based on independent example and feature subsampling are attractive for two reasons. First, they are computationally appealing in that they are massively parallelizable, and since each member of the ensemble uses only part of the data, they are able to overcome memory limitations faced by other methods (Louppe and Geurts, 2012). Second, ensemble methods are known to achieve lower risk due to the fact that combining several different predictors reduces variance (B uhlmann and Yu, 2002; Wager et al., 2014; Scornet et al., 2015), and empirically they have been found to perform very well. Random forests (Breiman, 2001; Athey et al., 2019; Friedberg et al., 2018), for example, ensemble methods that combine example and feature subsampling with shallow decision tress, remain among the best-performing off-the-shelf machine learning methods available (Cutler and Zhao, 2001; Fern andez-Delgado et al., 2014; Wyner et al., 2017).

We propose a graph spectral representation of time series data that 1) is parsimoniously encoded to user-demanded resolution; 2) is unsupervised and performant in data-constrained scenarios; 3) captures event and event-transition structure within the time series; and 4) has near-linear computational complexity in both signal length and ambient dimension. This representation, which we call Laplacian Events Signal Segmentation (LESS), can be computed on time series of arbitrary dimension and originating from sensors of arbitrary type. Hence, time series originating from sensors of heterogeneous type can be compressed to levels demanded by constrained-communication environments, before being fused at a common center. Temporal dynamics of the data is summarized without explicit partitioning or probabilistic modeling. As a proof-of-principle, we apply this technique on high dimensional wavelet coefficients computed from the Free Spoken Digit Dataset to generate a memory efficient representation that is interpretable. Due to its unsupervised and non-parametric nature, LESS representations remain performant in the digit classification task despite the absence of labels and limited data.

Operator-Valued Kernels (OVKs) and Vector-Valued Reproducing Kernel Hilbert Spaces (vv-RKHSs) provide an elegant way to extend scalar kernel methods when the output space is a Hilbert space. First used in multi-task regression, this theoretical framework opens the door to various applications, ranging from structured output prediction to functional regression, thanks to its ability to deal with infinite dimensional output spaces. This work investigates how to use the duality principle to handle different families of loss functions, yet unexplored within vv-RKHSs. The difficulty of having infinite dimensional dual variables is overcome, either by means of a Double Representer Theorem when the loss depends on inner products solely, or by an in-depth analysis of the Fenchel-Legendre transform of integral losses. Experiments on structured prediction, function-to-function regression and structured representation learning with $\epsilon$-insensitive and Huber losses illustrate the benefits of this framework.

Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to improve scalability of GPs, approximate posterior inference is frequently employed, where a prominent class of approximation techniques is based on local GP experts. However, the local-expert techniques proposed so far are either not well-principled, come with limited approximation guarantees, or lead to intractable models. In this paper, we introduce deep structured mixtures of GP experts, a stochastic process model which i) allows exact posterior inference, ii) has attractive computational and memory costs, and iii), when used as GP approximation, captures predictive uncertainties consistently better than previous approximations. In a variety of experiments, we show that deep structured mixtures have a low approximation error and outperform existing expert-based approaches.

We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based on training data with variable-length learning curves. We study instantiations of this framework based on random forests and Bayesian recurrent neural networks. Our experiments show that these models yield better predictions than state-of-the-art models from the hyperparameter optimization literature when extrapolating the performance of neural networks trained with different hyperparameter settings.

Online retail, eCommerce, frequently falls victim to fraud conducted by malicious customers (fraudsters) who obtain goods or services through deception. Fraud coordinated by groups of professional fraudsters that place several fraudulent orders to maximize their gain is referred to as organized fraud. Existing approaches to fraud detection typically analyze orders in isolation and they are not effective at identifying groups of fraudulent orders linked to organized fraud. These also wrongly identify many legitimate orders as fraud, which hinders their usage for automated fraud cancellation. We introduce a novel solution to detect organized fraud by analyzing orders in bulk. Our approach is based on clustering and aims to group together fraudulent orders placed by the same group of fraudsters. It selectively uses two existing techniques, agglomerative clustering and sampling to recursively group orders into small clusters in a reasonable amount of time. We assess our clustering technique on real-world orders placed on the Zalando website, the largest online apparel retailer in Europe1. Our clustering processes 100,000s of orders in a few hours and groups 35-45% of fraudulent orders together. We propose a simple technique built on top of our clustering that detects 26.2% of fraud while raising false alarms for only 0.1% of legitimate orders.