In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series. Most existing methods in the literature focus on manually building specific Contrastive Learning Strategies (CLS) by human heuristics for certain datasets and tasks. However, manually developing CLS usually require excessive prior knowledge about the datasets and tasks, e.g., professional cognition of the medical time series in healthcare, as well as huge human labor and massive experiments to determine the detailed learning configurations. In this paper, we present an Automated Machine Learning (AutoML) practice at Microsoft, which automatically learns to contrastively learn representations for various time series datasets and tasks, namely Automated Contrastive Learning (AutoCL). We first construct a principled universal search space of size over 3x1012, covering data augmentation, embedding transformation, contrastive pair construction and contrastive losses. Further, we introduce an efficient reinforcement learning algorithm, which optimizes CLS from the performance on the validation tasks, to obtain more effective CLS within the space. Experimental results on various real-world tasks and datasets demonstrate that AutoCL could automatically find the suitable CLS for a given dataset and task. From the candidate CLS found by AutoCL on several public datasets/tasks, we compose a transferable Generally Good Strategy (GGS), which has a strong performance for other datasets. We also provide empirical analysis as a guidance for future design of CLS.
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 the ID or text-based recommendation problem, 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.
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems. Previous data-driven RCA methods, particularly those employing causal discovery techniques, have primarily focused on constructing dependency or causal graphs for backtracking the root causes. However, these methods often fall short as they rely solely on data from a single modality, thereby resulting in suboptimal solutions. In this work, we propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. To explore intricate relationships across different modalities, we propose a contrastive learning-based approach to extract modality-invariant and modality-specific representations within a shared latent space. Additionally, we introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph. Finally, we employ random walk with restart to simulate system fault propagation and identify potential root causes. Extensive experiments on three real-world datasets validate the effectiveness of our proposed framework.
In the dynamic landscape of online businesses, recommender systems are pivotal in enhancing user experiences. While traditional approaches have relied on static supervised learning, the quest for adaptive, user-centric recommendations has led to the emergence of the formulation of contextual bandits. This tutorial investigates the contextual bandits as a powerful framework for personalized recommendations. We delve into the challenges, advanced algorithms and theories, collaborative strategies, and open challenges and future prospects within this field. Different from existing related tutorials, (1) we focus on the exploration perspective of contextual bandits to alleviate the ``Matthew Effect'' in the recommender systems, i.e., the rich get richer and the poor get poorer, concerning the popularity of items; (2) in addition to the conventional linear contextual bandits, we will also dedicated to neural contextual bandits which have emerged as an important branch in recent years, to investigate how neural networks benefit contextual bandits for personalized recommendation both empirically and theoretically; (3) we will cover the latest topic, collaborative neural contextual bandits, to incorporate both user heterogeneity and user correlations customized for recommender system; (4) we will provide and discuss the new emerging challenges and open questions for neural contextual bandits with applications in the personalized recommendation, especially for large neural models.
Recent works have shown a reduction from contextual bandits to online regression under a realizability assumption [Foster and Rakhlin, 2020, Foster and Krishnamurthy, 2021]. In this work, we investigate the use of neural networks for such online regression and associated Neural Contextual Bandits (NeuCBs). Using existing results for wide networks, one can readily show a ${\mathcal{O}}(\sqrt{T})$ regret for online regression with square loss, which via the reduction implies a ${\mathcal{O}}(\sqrt{K} T^{3/4})$ regret for NeuCBs. Departing from this standard approach, we first show a $\mathcal{O}(\log T)$ regret for online regression with almost convex losses that satisfy QG (Quadratic Growth) condition, a generalization of the PL (Polyak-\L ojasiewicz) condition, and that have a unique minima. Although not directly applicable to wide networks since they do not have unique minima, we show that adding a suitable small random perturbation to the network predictions surprisingly makes the loss satisfy QG with unique minima. Based on such a perturbed prediction, we show a ${\mathcal{O}}(\log T)$ regret for online regression with both squared loss and KL loss, and subsequently convert these respectively to $\tilde{\mathcal{O}}(\sqrt{KT})$ and $\tilde{\mathcal{O}}(\sqrt{KL^*} + K)$ regret for NeuCB, where $L^*$ is the loss of the best policy. Separately, we also show that existing regret bounds for NeuCBs are $\Omega(T)$ or assume i.i.d. contexts, unlike this work. Finally, our experimental results on various datasets demonstrate that our algorithms, especially the one based on KL loss, persistently outperform existing algorithms.
The growth of e-commerce has seen a surge in popularity of platforms like Amazon, eBay, and Taobao. This has given rise to a unique shopping behavior involving baskets - sets of items purchased together. As a less studied interaction mode in the community, the question of how should shopping basket complement personalized recommendation systems remains under-explored. While previous attempts focused on jointly modeling user purchases and baskets, the distinct semantic nature of these elements can introduce noise when directly integrated. This noise negatively impacts the model's performance, further exacerbated by significant noise (e.g., a user is misled to click an item or recognizes it as uninteresting after consuming it) within both user and basket behaviors. In order to cope with the above difficulties, we propose a novel Basket recommendation framework via Noise-tolerated Contrastive Learning, named BNCL, to handle the noise existing in the cross-behavior integration and within-behavior modeling. First, we represent the basket-item interactions as the hypergraph to model the complex basket behavior, where all items appearing in the same basket are treated as a single hyperedge. Second, cross-behavior contrastive learning is designed to suppress the noise during the fusion of diverse behaviors. Next, to further inhibit the within-behavior noise of the user and basket interactions, we propose to exploit invariant properties of the recommenders w.r.t augmentations through within-behavior contrastive learning. A novel consistency-aware augmentation approach is further designed to better identify noisy interactions with the consideration of the above two types of interactions. Our framework BNCL offers a generic training paradigm that is applicable to different backbones. Extensive experiments on three shopping transaction datasets verify the effectiveness of our proposed method.
Dataset distillation (DD) offers a compelling approach in computer vision, with the goal of condensing extensive datasets into smaller synthetic versions without sacrificing much of the model performance. In this paper, we continue to study the methods for DD, by addressing its conceptually core objective: how to capture the essential representation of extensive datasets in smaller, synthetic forms. We propose a novel approach utilizing the Wasserstein distance, a metric rooted in optimal transport theory, to enhance distribution matching in DD. Our method leverages the Wasserstein barycenter, offering a geometrically meaningful way to quantify distribution differences and effectively capture the centroid of a set of distributions. Our approach retains the computational benefits of distribution matching-based methods while achieving new state-of-the-art performance on several benchmarks. To provide useful prior for learning the images, we embed the synthetic data into the feature space of pretrained classification models to conduct distribution matching. Extensive testing on various high-resolution datasets confirms the effectiveness and adaptability of our method, indicating the promising yet unexplored capabilities of Wasserstein metrics in dataset distillation.
Personalized federated learning algorithms have shown promising results in adapting models to various distribution shifts. However, most of these methods require labeled data on testing clients for personalization, which is usually unavailable in real-world scenarios. In this paper, we introduce a novel setting called test-time personalized federated learning (TTPFL), where clients locally adapt a global model in an unsupervised way without relying on any labeled data during test-time. While traditional test-time adaptation (TTA) can be used in this scenario, most of them inherently assume training data come from a single domain, while they come from multiple clients (source domains) with different distributions. Overlooking these domain interrelationships can result in suboptimal generalization. Moreover, most TTA algorithms are designed for a specific kind of distribution shift and lack the flexibility to handle multiple kinds of distribution shifts in FL. In this paper, we find that this lack of flexibility partially results from their pre-defining which modules to adapt in the model. To tackle this challenge, we propose a novel algorithm called ATP to adaptively learns the adaptation rates for each module in the model from distribution shifts among source domains. Theoretical analysis proves the strong generalization of ATP. Extensive experiments demonstrate its superiority in handling various distribution shifts including label shift, image corruptions, and domain shift, outperforming existing TTA methods across multiple datasets and model architectures. Our code is available at https://github.com/baowenxuan/ATP .
Class imbalance is prevalent in real-world node classification tasks and often biases graph learning models toward majority classes. Most existing studies root from a node-centric perspective and aim to address the class imbalance in training data by node/class-wise reweighting or resampling. In this paper, we approach the source of the class-imbalance bias from an under-explored topology-centric perspective. Our investigation reveals that beyond the inherently skewed training class distribution, the graph topology also plays an important role in the formation of predictive bias: we identify two fundamental challenges, namely ambivalent and distant message-passing, that can exacerbate the bias by aggravating majority-class over-generalization and minority-class misclassification. In light of these findings, we devise a lightweight topological augmentation method ToBA to dynamically rectify the nodes influenced by ambivalent/distant message-passing during graph learning, so as to mitigate the class-imbalance bias. We highlight that ToBA is a model-agnostic, efficient, and versatile solution that can be seamlessly combined with and further boost other imbalance-handling techniques. Systematic experiments validate the superior performance of ToBA in both promoting imbalanced node classification and mitigating the prediction bias between different classes.