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Xiang Jiang

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AAAI Conference 2026 Conference Paper

Time-Frequency Augmented Multi-level Contrastive Clustering for Time Series

Congyu Wang
Mingjing Du
Xiang Jiang

Current unsupervised time series clustering methods often struggle to fully exploit the inherent characteristics of time series data and commonly adopt a two-stage training strategy that separates feature learning from the clustering process. To address these limitations, this paper proposes a novel deep clustering framework, Time-Frequency augmented Multi-level Contrastive Clustering (TFMCC). TFMCC employs a multi-scale time-frequency augmentation strategy, where each training iteration stochastically selects time and frequency scales to generate diverse augmented views, enhancing the model’s ability to learn robust and generalizable representations. In addition, a multi-level contrastive learning mechanism is introduced to jointly capture temporal dependencies, inter-sample similarities, and cluster structures. By jointly optimizing these components, TFMCC enables the learning of temporally-aware and clustering-friendly representations. Experimental results on 40 benchmark datasets demonstrate that TFMCC outperforms six existing methods in clustering accuracy.

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AAAI Conference 2021 Conference Paper

Hypothesis Disparity Regularized Mutual Information Maximization

Qicheng Lao
Xiang Jiang
Mohammad Havaei

We propose a hypothesis disparity regularized mutual information maximization (HDMI) approach to tackle unsupervised hypothesis transfer—as an effort towards unifying hypothesis transfer learning (HTL) and unsupervised domain adaptation (UDA)—where the knowledge from a source domain is transferred solely through hypotheses and adapted to the target domain in an unsupervised manner. In contrast to the prevalent HTL and UDA approaches that typically use a single hypothesis, HDMI employs multiple hypotheses to leverage the underlying distributions of the source and target hypotheses. To better utilize the crucial relationship among different hypotheses— as opposed to unconstrained optimization of each hypothesis independently—while adapting to the unlabeled target domain through mutual information maximization, HDMI incorporates a hypothesis disparity regularization that coordinates the target hypotheses jointly learn better target representations while preserving more transferable source knowledge with better-calibrated prediction uncertainty. HDMI achieves stateof-the-art adaptation performance on benchmark datasets for UDA in the context of HTL, without the need to access the source data during the adaptation.

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