Background Network clustering is the task of assigning nodes to groups via user-defined (statistical) "similarities" among nodal time series (signals), and is ubiquitous across a plethora of disciplines such as computer vision [1], wireless-sensor [2], social [3] and brain networks [4]. In brain networks, the choice of scale and type of data determine how networks are built. At the microscopic level, network nodes might be neurons, and edges could represent anatomical connections such as synapses (structural connectivity), or statistical relationships between firing patterns of neurons (functional connectivity). Similarly, at the macroscopic level, nodes can represent brain regions. At this scale, in structural networks, edges might represent long range anatomical connections between brain regions or, in functional networks, statistical relationships between regional brain dynamics recorded via functional Magnetic Resonance Imaging (fMRI) or encephalopathy (EEG). Here, we are interested in functional brain networks in which network nodes represent brain regions whose activity can be represented by a time series describing the dynamic evolution of brain activity.[5];

This paradigm provides a rigorous mathematical framework for the study and design of privacy-preserving algorithms. This setting assumes that there is a trusted curator that holds data containing some possibly sensitive records of n individuals. The goal of differential privacy is to simultaneously protect every individual record while releasing global characteristics of the database [14]. This is achieved by constructing randomized algorithms that release noisy versions of the desired outputs, where the noise level is calibrated to prevent any individual level data to be identifiable by querying the database. Even though the machine learning community has been very prolific in developing differentially private algorithms for complex settings including multiarmed bandit problems [23, 26, 30], high-dimensional regression [18, 29] and deep learning [1, 19], some fundamental statistical questions are only starting to be understood. For example, the first statistical minimax rates of convergence under differential privacy were recently established in [7,11]. Some earlier work framing differential privacy in traditional statistics terms include [9, 17, 20, 28, 31].

Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.

Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks. The evolving nature of temporal dynamic graphs requires handling new nodes as well as capturing temporal patterns. The node embeddings, which are now functions of time, should represent both the static node features and the evolving topological structures. Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture. We propose the temporal graph attention (TGAT) layer to efficiently aggregate temporal-topological neighborhood features as well as to learn the time-feature interactions. For TGAT, we use the self-attention mechanism as building block and develop a novel functional time encoding technique based on the classical Bochner's theorem from harmonic analysis. By stacking TGAT layers, the network recognizes the node embeddings as functions of time and is able to inductively infer embeddings for both new and observed nodes as the graph evolves. The proposed approach handles both node classification and link prediction task, and can be naturally extended to include the temporal edge features. We evaluate our method with transductive and inductive tasks under temporal settings with two benchmark and one industrial dataset. Our TGAT model compares favorably to state-of-the-art baselines as well as the previous temporal graph embedding approaches.

The goal of federated learning is to design algorithms in which several agents communicate with a central node, in a privacy-protecting manner, to minimize the average of their loss functions. In this approach, each node not only shares the required computational budget but also has access to a larger data set, which improves the quality of the resulting model. However, this method only develops a common output for all the agents, and therefore, does not adapt the model to each user data. This is an important missing feature especially given the heterogeneity of the underlying data distribution for various agents. In this paper, we study a personalized variant of the federated learning in which our goal is to find a shared initial model in a distributed manner that can be slightly updated by either a current or a new user by performing one or a few steps of gradient descent with respect to its own loss function. This approach keeps all the benefits of the federated learning architecture while leading to a more personalized model for each user. We show this problem can be studied within the Model-Agnostic Meta-Learning (MAML) framework. Inspired by this connection, we propose a personalized variant of the well-known Federated Averaging algorithm and evaluate its performance in terms of gradient norm for non-convex loss functions. Further, we characterize how this performance is affected by the closeness of underlying distributions of user data, measured in terms of distribution distances such as Total Variation and 1-Wasserstein metric.

Despite substantial progress in signal source separation, results for richly structured data continue to contain perceptible artifacts. In contrast, recent deep generative models can produce authentic samples in a variety of domains that are indistinguishable from samples of the data distribution. This paper introduces a Bayesian approach to source separation that uses generative models as priors over the components of a mixture of sources, and Langevin dynamics to sample from the posterior distribution of sources given a mixture. This decouples the source separation problem from generative modeling, enabling us to directly use cutting-edge generative models as priors. The method achieves state-of-the-art performance for MNIST digit separation. We introduce new methodology for evaluating separation quality on richer datasets, providing quantitative evaluation of separation results on CIFAR-10. We also provide qualitative results on LSUN.

We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences. Existing work suffers from either limited model flexibility or poor model explainability and thus fails to uncover Granger causality across a wide variety of event sequences with diverse event interdependency. To address these weaknesses, we propose CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework for the studied task. The key idea of CAUSE is to first implicitly capture the underlying event interdependency by fitting a neural point process, and then extract from the process a Granger causality statistic using an axiomatic attribution method. Across multiple datasets riddled with diverse event interdependency, we demonstrate that CAUSE achieves superior performance on correctly inferring the inter-type Granger causality over a range of state-of-the-art methods.

Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains requiring high reliability. Various adversarial attacks are proposed to sabotage the learning performance of DNN models. Among those, the black-box adversarial attack methods have received special attentions owing to their practicality and simplicity. Black-box attacks usually prefer less queries in order to maintain stealthy and low costs. However, most of the current black-box attack methods adopt the first-order gradient descent method, which may come with certain deficiencies such as relatively slow convergence and high sensitivity to hyper-parameter settings. In this paper, we propose a zeroth-order natural gradient descent (ZO-NGD) method to design the adversarial attacks, which incorporates the zeroth-order gradient estimation technique catering to the black-box attack scenario and the second-order natural gradient descent to achieve higher query efficiency. The empirical evaluations on image classification datasets demonstrate that ZO-NGD can obtain significantly lower model query complexities compared with state-of-the-art attack methods.

A recent line of research has provided convergence guarantees for gradient descent algorithms in the excessive over-parameterization regime where the widths of all the hidden layers are required to be polynomially large in the number of training samples. However, the widths of practical deep networks are often only large in the first layer(s) and then start to decrease towards the output layer. This raises an interesting open question whether similar results also hold under this empirically relevant setting. Existing theoretical insights suggest that the loss surface of this class of networks is well-behaved, but these results usually do not provide direct algorithmic guarantees for optimization. In this paper, we close the gap by showing that one wide layer followed by pyramidal deep network topology suffices for gradient descent to find a global minimum with a geometric rate. Our proof is based on a weak form of Polyak-Lojasiewicz inequality which holds for deep pyramidal networks in the manifold of full-rank weight matrices.

It is often important to leverage parallelism in order to tackle large scale stochastic optimization problems. A prime example is the task of minimizing the loss of machine learning models with millions or billions of parameters over enormous training sets. One popular distributed approach is local stochastic gradient descent (SGD) (Zinkevich et al., 2010; Coppola, 2015; Zhou and Cong, 2018; Stich, 2018), also known as "parallel SGD" or "Federated Averaging" 1 (McMahan et al., 2016), which is commonly applied to large scale convex and non-convex stochastic optimization problems, including in data center and "Federated Learning" settings (Kairouz et al., 2019). Local SGD uses M parallel workers which, in each of R rounds, independently execute K steps of SGD starting from a common iterate, and then communicate and average their iterates to obtain the common iterate from which the next round begins. Overall, each machine computes T KR stochastic gradients and executes KR SGD steps locally, for a total of N KRM overall stochastic gradients computed (and so N KRM samples used), with R rounds of communication (every K steps of computation).