Treatment Effect Estimation with Unmeasured Confounders in Data Fusion

In the presence of unmeasured confounders, we address the problem of treatment effect estimation from data fusion, that is, multiple datasets collected under different treatment assignment mechanisms. For example, marketers may assign different advertising strategies to the same products at different times/places. To handle the bias induced by unmeasured confounders and data fusion, we propose to separate the observational data into multiple groups (each group with an independent treatment assignment mechanism), and then explicitly model the group indicator as a Latent Group Instrumental Variable (LatGIV) to implement IV-based Regression. In this paper, we conceptualize this line of thought and develop a unified framework to (1) estimate the distribution differences of observed variables across groups; (2) model the LatGIVs from the different treatment assignment mechanisms; and (3) plug LatGIVs to estimate the treatment-response function. Empirical results demonstrate the advantages of the LatGIV compared with state-of-the-art methods.

Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling

In an era of information explosion, recommendation systems play an important role in people's daily life by facilitating content exploration. It is known that user activeness, i.e., number of behaviors, tends to follow a long-tail distribution, where the majority of users are with low activeness. In practice, we observe that tail users suffer from significantly lower-quality recommendation than the head users after joint training. We further identify that a model trained on tail users separately still achieve inferior results due to limited data. Though long-tail distributions are ubiquitous in recommendation systems, improving the recommendation performance on the tail users still remains challenge in both research and industry. Directly applying related methods on long-tail distribution might be at risk of hurting the experience of head users, which is less affordable since a small portion of head users with high activeness contribute a considerate portion of platform revenue. In this paper, we propose a novel approach that significantly improves the recommendation performance of the tail users while achieving at least comparable performance for the head users over the base model. The essence of this approach is a novel Gradient Aggregation technique that learns common knowledge shared by all users into a backbone model, followed by separate plugin prediction networks for the head users and the tail users personalization. As for common knowledge learning, we leverage the backward adjustment from the causality theory for deconfounding the gradient estimation and thus shielding off the backbone training from the confounder, i.e., user activeness. We conduct extensive experiments on two public recommendation benchmark datasets and a large-scale industrial datasets collected from the Alipay platform. Empirical studies validate the rationality and effectiveness of our approach.

Label-Efficient Domain Generalization via Collaborative Exploration and Generalization

Considerable progress has been made in domain generalization (DG) which aims to learn a generalizable model from multiple well-annotated source domains to unknown target domains. However, it can be prohibitively expensive to obtain sufficient annotation for source datasets in many real scenarios. To escape from the dilemma between domain generalization and annotation costs, in this paper, we introduce a novel task named label-efficient domain generalization (LEDG) to enable model generalization with label-limited source domains. To address this challenging task, we propose a novel framework called Collaborative Exploration and Generalization (CEG) which jointly optimizes active exploration and semi-supervised generalization. Specifically, in active exploration, to explore class and domain discriminability while avoiding information divergence and redundancy, we query the labels of the samples with the highest overall ranking of class uncertainty, domain representativeness, and information diversity. In semi-supervised generalization, we design MixUp-based intra- and inter-domain knowledge augmentation to expand domain knowledge and generalize domain invariance. We unify active exploration and semi-supervised generalization in a collaborative way and promote mutual enhancement between them, boosting model generalization with limited annotation. Extensive experiments show that CEG yields superior generalization performance. In particular, CEG can even use only 5% data annotation budget to achieve competitive results compared to the previous DG methods with fully labeled data on PACS dataset.

S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?

Collaborative multi-agent reinforcement learning (MARL) has been widely used in many practical applications, where each agent makes a decision based on its own observation. Most mainstream methods treat each local observation as an entirety when modeling the decentralized local utility functions. However, they ignore the fact that local observation information can be further divided into several entities, and only part of the entities is helpful to model inference. Moreover, the importance of different entities may change over time. To improve the performance of decentralized policies, the attention mechanism is used to capture features of local information. Nevertheless, existing attention models rely on dense fully connected graphs and cannot better perceive important states. To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations. The local utility functions are estimated through the self-attention and sparse attention mechanisms separately, then are combined into a standard joint value function and auxiliary joint value function in the central critic. We design the S2RL framework as a plug-and-play module, making it general enough to be applied to various methods. Extensive experiments on StarCraft II show that S2RL can significantly improve the performance of many state-of-the-art methods.

Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning

While annotating decent amounts of data to satisfy sophisticated learning models can be cost-prohibitive for many real-world applications. Active learning (AL) and semi-supervised learning (SSL) are two effective, but often isolated, means to alleviate the data-hungry problem. Some recent studies explored the potential of combining AL and SSL to better probe the unlabeled data. However, almost all these contemporary SSL-AL works use a simple combination strategy, ignoring SSL and AL's inherent relation. Further, other methods suffer from high computational costs when dealing with large-scale, high-dimensional datasets. Motivated by the industry practice of labeling data, we propose an innovative Inconsistency-based virtual aDvErsarial Active Learning (IDEAL) algorithm to further investigate SSL-AL's potential superiority and achieve mutual enhancement of AL and SSL, i.e., SSL propagates label information to unlabeled samples and provides smoothed embeddings for AL, while AL excludes samples with inconsistent predictions and considerable uncertainty for SSL. We estimate unlabeled samples' inconsistency by augmentation strategies of different granularities, including fine-grained continuous perturbation exploration and coarse-grained data transformations. Extensive experiments, in both text and image domains, validate the effectiveness of the proposed algorithm, comparing it against state-of-the-art baselines. Two real-world case studies visualize the practical industrial value of applying and deploying the proposed data sampling algorithm.

Debiased Graph Neural Networks with Agnostic Label Selection Bias

Most existing Graph Neural Networks (GNNs) are proposed without considering the selection bias in data, i.e., the inconsistent distribution between the training set with test set. In reality, the test data is not even available during the training process, making selection bias agnostic. Training GNNs with biased selected nodes leads to significant parameter estimation bias and greatly impacts the generalization ability on test nodes. In this paper, we first present an experimental investigation, which clearly shows that the selection bias drastically hinders the generalization ability of GNNs, and theoretically prove that the selection bias will cause the biased estimation on GNN parameters. Then to remove the bias in GNN estimation, we propose a novel Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation regularizer. The differentiated decorrelation regularizer estimates a sample weight for each labeled node such that the spurious correlation of learned embeddings could be eliminated. We analyze the regularizer in causal view and it motivates us to differentiate the weights of the variables based on their contribution on the confounding bias. Then, these sample weights are used for reweighting GNNs to eliminate the estimation bias, thus help to improve the stability of prediction on unknown test nodes. Comprehensive experiments are conducted on several challenging graph datasets with two kinds of label selection biases. The results well verify that our proposed model outperforms the state-of-the-art methods and DGNN is a flexible framework to enhance existing GNNs.

Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth

Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL). In many real-world scenarios, communication can be expensive and the bandwidth of the multi-agent system is subject to certain constraints. Redundant messages who occupy the communication resources can block the transmission of informative messages and thus jeopardize the performance. In this paper, we aim to learn the minimal sufficient communication messages. First, we initiate the communication between agents by a complete graph. Then we introduce the graph information bottleneck (GIB) principle into this complete graph and derive the optimization over graph structures. Based on the optimization, a novel multi-agent communication module, called CommGIB, is proposed, which effectively compresses the structure information and node information in the communication graph to deal with bandwidth-constrained settings. Extensive experiments in Traffic Control and StanCraft II are conducted. The results indicate that the proposed methods can achieve better performance in bandwidth-restricted settings compared with state-of-the-art algorithms, with especially large margins in large-scale multi-agent tasks.

Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth (a position paper)

Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL). In many real-world scenarios, communication can be expensive and the bandwidth of the multi-agent system is subject to certain constraints. Redundant messages who occupy the communication resources can block the transmission of informative messages and thus jeopardize the performance. In this paper, we aim to learn the minimal sufficient communication messages. First, we initiate the communication between agents by a complete graph. Then we introduce the graph information bottleneck (GIB) principle into this complete graph and derive the optimization over graph structures. Based on the optimization, a novel multi-agent communication module, called CommGIB, is proposed, which effectively compresses the structure information and node information in the communication graph to deal with bandwidth-constrained settings. Extensive experiments in Traffic Control and StanCraft II are conducted. The results indicate that the proposed methods can achieve better performance in bandwidth-restricted settings compared with state-of-the-art algorithms, with especially large margins in large-scale multi-agent tasks.

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.