Recent learning-to-imitation methods have shown promise in planning by imitating within the observation-action space, yet they remain constrained in open environments, especially for long-horizon tasks. In contrast, while traditional symbolic planning excels in such tasks through logical reasoning over human-defined symbolic spaces, it struggles with high-dimensional visual inputs encountered in real-world scenarios. In this work, we draw inspiration from abductive learning and introduce a novel framework ABductive Imitation Learning (ABIL) that integrates the benefits of data-driven learning and symbolic-based reasoning, enabling long-horizon planning. Specifically, we employ abductive reasoning to understand the demonstrations in symbolic space and design the principles of sequential consistency to resolve the conflicts between perception and reasoning. ABIL generates predicate candidates to facilitate the perception from raw observations to symbolic space without laborious predicate annotations, providing a groundwork for symbolic planning. With the symbolic understanding, we develop a policy ensemble with base policies designed around different logical objectives, managed through symbolic reasoning. Experiments demonstrate that our method successfully comprehends observations with task-relevant symbolics to aid imitation learning. Importantly, ABIL demonstrates improved data efficiency and generalization across various long-horizon tasks, highlighting it as a promising solution for long-horizon planning. Project website: https://www.lamda.nju.edu.cn/shaojj/KDD25_ABIL/.

In offline Imitation Learning (IL), one of the main challenges is the covariate shift between the expert observations and the actual distribution encountered by the agent, because it is difficult to determine what action an agent should take when outside the state distribution of the expert demonstrations. Recently, the model-free solutions introduced supplementary data and identified the latent expert-similar samples to augment the reliable samples during learning. Model-based solutions build forward dynamic models with conservatism quantification and then generate additional trajectories in the neighborhood of expert demonstrations. However, without reward supervision, these methods are often over-conservative in the out-of-expert-support regions, because only in states close to expert-observed states can there be a preferred action enabling policy optimization. To encourage more exploration on expert-unobserved states, we propose a novel model-based framework, called offline Imitation Learning with Self-paced Reverse Augmentation (SRA). Specifically, we build a reverse dynamic model from the offline demonstrations, which can efficiently generate trajectories leading to the expert-observed states in a self-paced style. Then, we use the subsequent reinforcement learning method to learn from the augmented trajectories and transit from expert-unobserved states to expert-observed states. This framework not only explores the expert-unobserved states but also guides maximizing long-term returns on these states, ultimately enabling generalization beyond the expert data. Empirical results show that our proposal could effectively mitigate the covariate shift and achieve the state-of-the-art performance on the offline imitation learning benchmarks. Project website: https://www.lamda.nju.edu.cn/shaojj/KDD24_SRA/.

Successful machine learning typically relies on fixed data distribution. However, due to unforeseen situations in the open world, distribution shift often occurs in applications. For instance, in the image recognition task, an unpredictable distributional shift may occur due to changes in background or lighting. Furthermore, to alleviate the harm of distribution shift, the resource budget is not infinite and often constrained. To cope with such a novel problem Resource Constrained Adaptation under Unknown Shift, in this paper we study active model adaptation both theoretically and empirically. First, we present a generalization analysis of active model adaptation for distribution shift. In theory, we show that active model adaptation could improve the generalization error from O(1/N) to O(1/N), with only a few queried samples. Second, based on the theoretical analysis, we present a systemic solution Auto, consisting of three sub-steps, that is, distribution tracking, sample selection and model adaptation. Specifically, we design a shifted distribution detection module to locate the distributional shifted samples. To fit the labeling budget, we employ a core-set algorithm to enhance the informativeness of the selected samples. Finally, we update the model through the newly queried labeled data. We conduct empirical studies of nine existing active strategies on diverse real world data sets and the results show that Auto could remarkably outperform all the baselines.

In multi-label tasks, sufficient and class-balanced label is usually hard to obtain, which makes it challenging to train a good classifier. In this paper, we consider the problem of learning from imbalanced and incomplete supervision, where only a small subset of labeled data is available and the label distribution is highly imbalanced. This setting is of importance and commonly appears in a variety of real applications. For instance, considering the ride-sharing liability judgment task, liability disputes usually due to a variety of reasons, however, it is expensive to manually annotate the reasons, meanwhile, the distribution of reason is often seriously imbalanced. In this paper, we present a systemic framework Limi consisting of three sub-steps, that is, Label Separating, Correlation Mining and Label Completion. Specifically, we propose an effective two-classifier strategy to separately tackle head and tail labels so as to alleviate the performance degradation on tail labels while maintaining high performance on head labels. Then, a novel label correlation network is adopted to explore the label relation knowledge with flexible aggregators. Moreover, the Limi framework completes the label on unlabeled instances in a semi-supervised fashion. The framework is general, flexible, and effective. Extensive experiments on diverse applications, such as the ride-sharing liability judgment task from Didi and various benchmark tasks, demonstrate that our solution is clearly better than many competitive methods.