The existing Click-Through Rate (CTR) algorithms have their own advantages and are sensitive to hyperparameters. Quickly obtaining a high-performance CTR model for the new task can bring good application effects. However, ordinary users fail to do so due to the lack of domain knowledge. In this paper, we remedy this deficiency by proposing AutoCTR, an efficient meta-learning based Combined Algorithm Selection and Hyperparameter Optimization (CASH) algorithm, to help non-expert users quickly find the best CTR model. In AutoCTR, we introduce the meta-learning technique to make full use of the meta-information w.r.t. CTR to guide for the new CTR task. Specifically, we utilize the meta-information to learn characteristics and representations of CTR algorithms with different settings. We use these meta experiences combined with few evaluation information on the target CTR dataset to efficiently exploring the huge CTR CASH search space for the new task. The CTR model representation method has significant influence on the quality of the learned meta experiences. To further enhance the experiences quality, we also design a Graph Neural Network (GNN) based embedding learning method. This method can link different CTR models through their components, and thus quickly learning higher-quality model representations. Extensive experimental results show that AutoCTR can quickly select suitable CTR models for different CTR tasks. Compared with the existing CASH algorithms, which ignore meta-information or rely on a huge amount of meta-information, AutoCTR is more reasonable and efficient.

With the explosion of artificial intelligence in various fields, providing causal explanations for predictive models becomes urgent. In this study, we investigate causal counterfactual explanation generation and propose CUBE, a causal intervention-based counterfactual explanation method. This work models the counterfactual generation process as a causal intervention model for the first time and employs a causal director to integrate causal relationships. Furthermore, this work proposes a model-based framework to improve counterfactual generation efficiency. The experimental results validate that CUBE outperforms baselines in terms of both lower time costs and higher explanation quality.

Recent several years have witnessed the rapid explosion of artificial intelligence applied in various domains with the surpassing human-level performance. Despite the success, these models' underlying mechanisms remain a mystery, as their complicated representations make human understanding impossible. This mystery may cause discrimination and non-robustness in prediction. Making deep learning models more transparent and understandable is gaining popularity, but most of interpretation approaches provide spurious correlations leading to suboptimal, incorrect or even biased interpretations, which could be reduced by causal explanations. Motivated by this, we attempt to study the generation of causal explanations and proposeCUBE, a causal intervention-based counterfactual interpretation method. To ensure that the generation process of counterfactual explanation conforms to causality, we model the counterfactual generation process as a causal graph and construct a counterfactual generation model based on the causal intervention; to generate counterfactuals that adhere to the causality, we introduce a causal director to capture the causal relationships in the distribution and guide the generation of counterfactuals; to improve the efficiency of the counterfactual generation when facing a large number of explanation queries, we model it as a sample generation problem and propose an explainable framework based on adversarial generation. The experimental results validate thatCUBEoutperforms other approaches in terms of both lower time costs and higher explanation quality.

Although the Trajectory Prediction (TP) model has achieved great success in computer vision and robotics fields, its architecture and training scheme design rely on heavy manual work and domain knowledge, which is not friendly to common users. Besides, the existing works ignore Federated Learning (FL) scenarios, failing to make full use of distributed multi-source datasets with rich actual scenes to learn more a powerful TP model. In this paper, we make up for the above defects and propose ATPFL to help users federate multi-source trajectory datasets to automatically design and train a powerful TP model. In ATPFL, we build an effective TP search space by analyzing and summarizing the existing works. Then, based on the characters of this search space, we design a relation-sequence-aware search strategy, realizing the automatic design of the TP model. Finally, we find appropriate federated training methods to respectively support the TP model search and final model training under the FL framework, ensuring both the search efficiency and the final model performance. Extensive experimental results show that ATPFL can help users gain well-performed TP models, achieving better results than the existing TP models trained on the single-source dataset.