Federated Averaging (FedAvg) is known to experience convergence issues when encountering significant clients system heterogeneity and data heterogeneity. Server momentum has been proposed as an effective mitigation. However, existing server momentum works are restrictive in the momentum formulation, do not properly schedule hyperparameters and focus only on system homogeneous settings, which leaves the role of server momentum still an under-explored problem. In this paper, we propose a general framework for server momentum, that (a) covers a large class of momentum schemes that are unexplored in federated learning (FL), (b) enables a popular stagewise hyperparameter scheduler, (c) allows heterogeneous and asynchronous local computing. We provide rigorous convergence analysis for the proposed framework. To our best knowledge, this is the first work that thoroughly analyzes the performances of server momentum with a hyperparameter scheduler and system heterogeneity. Extensive experiments validate the effectiveness of our proposed framework. Due to page limit, we leave all proofs to the full version https://arxiv.org/abs/2312.12670.

Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Our daily choices, especially in domains like fashion and retail, are substantially shaped by multi-modal data, such as pictures and textual descriptions. These modalities not only offer intuitive guidance but also cater to personalized user preferences. However, the predominant personalization approaches mainly focus on ID or text-based recommendation problems, failing to comprehend the information spanning various tasks or modalities. In this paper, our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP), which effectively leverages multi-modal data while eliminating the complexities associated with task- and modality-specific customization. We argue that the advancements in foundational generative modeling have provided the flexibility and effectiveness necessary to achieve the objective. In light of this, we develop a generic and extensible personalization generative framework, that can handle a wide range of personalized needs including item recommendation, product search, preference prediction, explanation generation, and further user-guided image generation. Our methodology enhances the capabilities of foundational language models for personalized tasks by seamlessly ingesting interleaved cross-modal user history information, ensuring a more precise and customized experience for users. To train and evaluate the proposed multi-modal personalized tasks, we also introduce a novel and comprehensive benchmark covering a variety of user requirements. Our experiments on the real-world benchmark showcase the model's potential, outperforming competitive methods specialized for each task.

Non-IID transfer learning on graphs is crucial in many high-stakes domains. The majority of existing works assume stationary distribution for both source and target domains. However, real-world graphs are intrinsically dynamic, presenting challenges in terms of domain evolution and dynamic discrepancy between source and target domains. To bridge the gap, we shift the problem to the dynamic setting and pose the question: given the *label-rich* source graphs and the *label-scarce* target graphs both observed in previous $T$ timestamps, how can we effectively characterize the evolving domain discrepancy and optimize the generalization performance of the target domain at the incoming $T+1$ timestamp? To answer it, we propose a generalization bound for *dynamic non-IID transfer learning on graphs*, which implies the generalization performance is dominated by domain evolution and domain discrepancy between source and target graphs. Inspired by the theoretical results, we introduce a novel generic framework named EvoluNet. It leverages a transformer-based temporal encoding module to model temporal information of the evolving domains and then uses a dynamic domain unification module to efficiently learn domain-invariant representations across the source and target domains. Finally, EvoluNet outperforms the state-of-the-art models by up to 12.1%, demonstrating its effectiveness in transferring knowledge from dynamic source graphs to dynamic target graphs.

Rising usage of deep neural networks to perform decision making in critical applications like medical diagnosis and fi- nancial analysis have raised concerns regarding their reliability and trustworthiness. As automated systems become more mainstream, it is important their decisions be transparent, reliable and understandable by humans for better trust and confidence. To this effect, concept-based models such as Concept Bottleneck Models (CBMs) and Self-Explaining Neural Networks (SENN) have been proposed which constrain the latent space of a model to represent high level concepts easily understood by domain experts in the field. Although concept-based models promise a good approach to both increasing explainability and reliability, it is yet to be shown if they demonstrate robustness and output consistent concepts under systematic perturbations to their inputs. To better understand performance of concept-based models on curated malicious samples, in this paper, we aim to study their robustness to adversarial perturbations, which are also known as the imperceptible changes to the input data that are crafted by an attacker to fool a well-learned concept-based model. Specifically, we first propose and analyze different malicious attacks to evaluate the security vulnerability of concept based models. Subsequently, we propose a potential general adversarial training-based defense mechanism to increase robustness of these systems to the proposed malicious attacks. Extensive experiments on one synthetic and two real-world datasets demonstrate the effectiveness of the proposed attacks and the defense approach. An appendix of the paper with more comprehensive results can also be viewed at https://arxiv.org/abs/2211.16080.

Deep neural networks are susceptible to human imperceptible adversarial perturbations. One of the strongest defense mechanisms is Adversarial Training (AT). In this paper, we aim to address two predominant problems in AT. First, there is still little consensus on how to set hyperparameters with a performance guarantee for AT research, and customized settings impede a fair comparison between different model designs in AT research. Second, the robustly trained neural networks struggle to generalize well and suffer from tremendous overfitting. This paper focuses on the primary AT framework - Projected Gradient Descent Adversarial Training (PGD-AT). We approximate the dynamic of PGD-AT by a continuous-time Stochastic Differential Equation (SDE), and show that the diffusion term of this SDE determines the robust generalization. An immediate implication of this theoretical finding is that robust generalization is positively correlated with the ratio between learning rate and batch size. We further propose a novel approach, Diffusion Enhanced Adversarial Training (DEAT), to manipulate the diffusion term to improve robust generalization with virtually no extra computational burden. We theoretically show that DEAT obtains a tighter generalization bound than PGD-AT. Our empirical investigation is extensive and firmly attests that DEAT universally outperforms PGD-AT by a significant margin.

Conditional stochastic optimization has found applications in a wide range of machine learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the demand for training models with large-scale distributed data grows in these applications, there is an increasing need for communication-efficient distributed optimization algorithms, such as federated learning algorithms. This paper considers the nonconvex conditional stochastic optimization in federated learning and proposes the first federated conditional stochastic optimization algorithm (FCSG) with a conditional stochastic gradient estimator and a momentum-based algorithm (\emph{i.e.}, FCSG-M). To match the lower bound complexity in the single-machine setting, we design an accelerated algorithm (Acc-FCSG-M) via the variance reduction to achieve the best sample and communication complexity. Compared with the existing optimization analysis for Meta-Learning in FL, federated conditional stochastic optimization considers the sample of tasks. Extensive experimental results on various tasks validate the efficiency of these algorithms.

The minimax problems arise throughout machine learning applications, ranging from adversarial training and policy evaluation in reinforcement learning to AUROC maximization. To address the large-scale distributed data challenges across multiple clients with communication-efficient distributed training, federated learning (FL) is gaining popularity. Many optimization algorithms for minimax problems have been developed in the centralized setting (\emph{i.e.}, single-machine). Nonetheless, the algorithm for minimax problems under FL is still underexplored. In this paper, we study a class of federated nonconvex minimax optimization problems. We propose FL algorithms (FedSGDA+ and FedSGDA-M) and reduce existing complexity results for the most common minimax problems. For nonconvex-concave problems, we propose FedSGDA+ and reduce the communication complexity to $O(\varepsilon^{-6})$. Under nonconvex-strongly-concave and nonconvex-PL minimax settings, we prove that FedSGDA-M has the best-known sample complexity of $O(\kappa^{3} N^{-1}\varepsilon^{-3})$ and the best-known communication complexity of $O(\kappa^{2}\varepsilon^{-2})$. FedSGDA-M is the first algorithm to match the best sample complexity $O(\varepsilon^{-3})$ achieved by the single-machine method under the nonconvex-strongly-concave setting. Extensive experimental results on fair classification and AUROC maximization show the efficiency of our algorithms.

With the discovery of causality between synonymous mutations and diseases, it has become increasingly important to identify deleterious synonymous mutations for better understanding of their functional mechanisms. Although several machine learning methods have been proposed to solve the task, an effective feature representation method that can make use of the inner difference and relevance between deleterious and benign synonymous mutations is still challenging considering the vast number of synonymous mutations in human genome. In this work, we developed a robust and accurate predictor called frDSM for deleterious synonymous mutation prediction using logistic regression. More specifically, we introduced an effective feature representation learning method which exploits multiple feature descriptors from different perspectives including functional scores obtained from previously computational methods, evolutionary conservation, splicing and sequence feature descriptors, and these features descriptors were input into the 76 XGBoost classifiers to obtain the predictive probabilities values. These probabilities were concatenated to generate the 76-dimension new feature vector, and feature selection method was used to remove redundant and irrelevant features. Experimental results show that frDSM enables robust and accurate prediction than the competing prediction methods with 31 optimal features, which demonstrated the effectiveness of the feature representation learning method. frDSM is freely available at http://frdsm.xialab.info.

This paper 1 studies how to schedule hyperparameters to improve generalization of both centralized single-machine stochastic gradient descent (SGD) and distributed asynchronous SGD (ASGD). SGD augmented with momentum variants (e.g., heavy ball momentum (SHB) and Nesterov’s accelerated gradient (NAG)) has been the default optimizer for many tasks, in both centralized and distributed environments. However, many advanced momentum variants, despite empirical advantage over classical SHB/NAG, introduce extra hyperparameters to tune. The error-prone tuning is the main barrier for AutoML. Centralized SGD : We first focus on centralized single-machine SGD and show how to efficiently schedule the hyperparameters of a large class of momentum variants to improve generalization. We propose a unified framework called multistage quasi-hyperbolic momentum (Multistage QHM), which covers a large family of momentum variants as its special cases (e.g. vanilla SGD/SHB/NAG). Existing works mainly focus on only scheduling learning rate α ’s decay, while multistage QHM allows additional varying hyperparameters (e.g., momentum factor), and demonstrates better generalization than only tuning α . We show the convergence of multistage QHM for general nonconvex objectives. Distributed SGD : We then extend our theory to distributed asynchronous SGD (ASGD), in which a parameter server distributes data batches to several worker machines and updates parameters via aggregating batch gradients from workers. We quantify the asynchrony between different workers (i.e., gradient staleness), model the dynamics of asynchronous iterations via a stochastic differential equation (SDE), and then derive a PAC-Bayesian generalization bound for ASGD. As a byproduct, we show how a moderately large learning rate helps ASGD to generalize better. Our tuning strategies have rigorous justifications rather than a blind trial-and-error as we theoretically prove why our tuning strategies could decrease our derived generalization errors in both cases. Our strategies simplify the tuning process and beat competitive optimizers in test accuracy empirically. Our codes are publicly available https://github.com/jsycsjh/centralized-asynchronous-tuning.

Given a resource-rich source graph and a resource-scarce target graph, how can we effectively transfer knowledge across graphs and ensure a good generalization performance? In many high-impact domains (e.g., brain networks and molecular graphs), collecting and annotating data is prohibitively expensive and time-consuming, which makes domain adaptation an attractive option to alleviate the label scarcity issue. In light of this, the state-of-the-art methods focus on deriving domain-invariant graph representation that minimizes the domain discrepancy. However, it has recently been shown that a small domain discrepancy loss may not always guarantee a good generalization performance, especially in the presence of disparate graph structures and label distribution shifts. In this paper, we present TRANSNET, a generic learning framework for augmenting knowledge transfer across graphs. In particular, we introduce a novel notion named trinity signal that can naturally formulate various graph signals at different granularity (e.g., node attributes, edges, and subgraphs). With that, we further propose a domain unification module together with a trinity-signal mixup scheme to jointly minimize the domain discrepancy and augment the knowledge transfer across graphs. Finally, comprehensive empirical results show that TRANSNET outperforms all existing approaches on seven benchmark datasets by a significant margin.

A 71-76GHz traveling wave tube amplifier is successfully developed to meet the demand of future high-data-rate wireless communication, breaking through a series of key core technologies including beam-wave interaction circuit, two-stage depressed collector, electronic power conditioner and module integration. The test results show that the amplifier is capable of providing more than 63.1W CW output power,37.8dB saturated gain,23.6% total efficiency in 71-76GHz band. The 1dB compression point is over 21W. The third-order intermodulation is$379\boldsymbol{mm}^{*}200\boldsymbol{mm}^{*}63\boldsymbol{mm}$and 6.5kg respectively with -48VDC electronic power conditioner embedded.

This paper studies the convergence and generalization of a large class of Stochastic Gradient Descent (SGD) momentum schemes, in both learning from scratch and transferring representations with fine-tuning. Momentum-based acceleration of SGD is the default optimizer for many deep learning models. However, there is a lack of general convergence guarantees for many existing momentum variants in conjunction withstochastic gradient. It is also unclear how the momentum methods may affect thegeneralization error. In this paper, we give a unified analysis of several popular optimizers, e.g., Polyak's heavy ball momentum and Nesterov's accelerated gradient. Our contribution is threefold. First, we give a unified convergence guarantee for a large class of momentum variants in thestochastic setting. Notably, our results cover both convex and nonconvex objectives. Second, we prove a generalization bound for neural networks trained by momentum variants. We analyze how hyperparameters affect the generalization bound and consequently propose guidelines on how to tune these hyperparameters in various momentum schemes to generalize well. We provide extensive empirical evidence to our proposed guidelines. Third, this study fills the vacancy of a formal analysis of fine-tuning in literature. To our best knowledge, our work is the first systematic generalizability analysis on momentum methods that cover both learning from scratch and fine-tuning. Our codes are available https://github.com/jsycsjh/Demystify-Hyperparameters-for-Stochastic-Optimization-with-Transferable-Representations .

Stochastic gradient descent (SGD) augmented with various momentum variants (e.g. heavy ball momentum (SHB) and Nesterov's accelerated gradient (NAG)) has been the default optimizer for many learning tasks. Tuning the optimizer's hyperparameters is arguably the most time-consuming part of model training. Many new momentum variants, despite their empirical advantage over classical SHB/NAG, introduce even more hyperparameters to tune. Automating the tedious and error-prone tuning is essential for AutoML. This paper focuses on how to efficiently tune a large class of multistage momentum variants to improve generalization. We use the general formulation of quasi-hyperbolic momentum (QHM) and extend "constant and drop'', the widespread learning rate α scheduler where α is set large initially and then dropped every few epochs, to other hyperparameters (e.g. batch size b, momentum parameter β, instant discount factor ν). Multistage QHM is a unified framework which covers a large family of momentum variants as its special cases (e.g. vanilla SGD/SHB/NAG). Existing works mainly focus on scheduling α's decay, while multistage QHM allows additional varying hyperparameters such as b, β, and ν, and demonstrates better generalization ability than only tuning α. Our tuning strategies have rigorous justifications rather than a blind trial-and-error. We theoretically prove why our tuning strategies could improve generalization. We also show the convergence of multistage QHM for general nonconvex objective functions. Our strategies simplify the tuning process and beat competitive optimizers in test accuracy empirically.

Missing data, which commonly appears in multivariate time series, has been widely recognized as a key challenge in time series analysis. Many commonly used imputation methods either ignore the temporal dependencies of time series data, or do not adequately utilize the relationships among variables. State-ofthe-art methods on time series imputation are built on Recurrent Neural Networks (RNNs), which utilize the historical information to estimate current values sequentially. However, RNNs rely heavily on the output of nearby timestamps, which may lead to important information lost for long sequences. Moreover, individual variables typically present different dynamics and missingness patterns, which is neglected by the global RNN hidden states. In this paper, we propose an imputation framework to learn both global and local dependencies of multivariate time series, as well as a multi-dimensional self-attention to learn capture distant correlations across both time and feature. Extensive experiments show that the proposed framework outperforms the state-of-the-art methods in the imputation task, and benefits the downstream task.

The past years have witnessed the rapid development of deep reinforcement learning (DRL), which is a combination of deep learning and reinforcement learning (RL). However, the adoption of deep neural networks makes the decision-making process of DRL opaque and lacking transparency. Motivated by this, various interpretation methods for DRL have been proposed. However, those interpretation methods make an implicit assumption that they are performed in a reliable and secure environment. In practice, sequential agent-environment interactions expose the DRL algorithms and their corresponding downstream interpretations to extra adversarial risk. In spite of the prevalence of malicious attacks, there is no existing work studying the possibility and feasibility of malicious attacks against DRL interpretations. To bridge this gap, in this paper, we investigate the vulnerability of DRL interpretation methods. Specifically, we introduce the first study of the adversarial attacks against DRL interpretations, and propose an optimization framework based on which the optimal adversarial attack strategy can be derived. In addition, we study the vulnerability of DRL interpretation methods to the model poisoning attacks, and present an algorithmic framework to rigorously formulate the proposed model poisoning attack. Finally, we conduct both theoretical analysis and extensive experiments to validate the effectiveness of the proposed malicious attacks against DRL interpretations.

This paper develops the Correlation Networks (CorNet) architecture for the extreme multi-label text classification (XMTC) task, where the objective is to tag an input text sequence with the most relevant subset of labels from an extremely large label set. XMTC can be found in many real-world applications, such as document tagging and product annotation. Recently, deep learning models have achieved outstanding performances in XMTC tasks. However, these deep XMTC models ignore the useful correlation information among different labels. CorNet addresses this limitation by adding an extra CorNet module at the prediction layer of a deep model, which is able to learn label correlations, enhance raw label predictions with correlation knowledge and output augmented label predictions. We show that CorNet can be easily integrated with deep XMTC models and generalize effectively across different datasets. We further demonstrate that CorNet can bring significant improvements over the existing deep XMTC models in terms of both performance and convergence rate. The models and datasets are available at: https://github.com/XunGuangxu/CorNet.

While many methods have been proposed for generating temporal proposals, the performance of existing temporal detection pipelines is still limited by the quality of proposals. In this paper, we introduce a new refining model for temporal action detection, which incorporates the evaluation of Intersection-over-Union (IoU) value into the action classification, and then regresses a better segment using the neighboring contexts of one candidate proposal. To refine one candidate proposal, we augment it with two neighboring proposals of equal length, which capture the contextual information from the past and future segments. After extracting regional features for the augmented proposals, we utilize the dilated convolutions for contextual modeling to regress the offsets between the candidate proposal and the target segment within this augmented area. Extensive experiments on THUMOS14 demonstrate that our method successfully refines the generated proposals and achieves superior detection performance over other methods.