Parkinson's disease (PD) is one of the most common neurodegenerative disorder. PD telemonitoring emerges as a novel assessment modality enabling self-administered at-home tests of Unified Parkinson's Disease Rating Scale (UPDRS) scores, enhancing accessibility for PD patients. However, three types of noise would occur during measurements: (1) patient-induced measurement inaccuracies, (2) environmental noise, and (3) data packet loss during transmission, resulting in higher prediction errors. To address these challenges, NoRo, a noise-robust UPDRS prediction framework is proposed. First, the original speech features are grouped into ordered bins, based on the continuous values of a selected feature, to construct contrastive pairs. Second, the contrastive pairs are employed to train a multilayer perceptron encoder for generating noise-robust features. Finally, these features are concatenated with the original features as the augmented features, which are then fed into the UPDRS prediction models. Notably, we further introduces a novel evaluation approach with customizable noise injection module, and extensive experiments show that NoRo can successfully enhance the noise robustness of UPDRS prediction across various downstream prediction models under different noisy environments.

We use the SGD optimizer in the three training stage.

A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emerged as a promising approach for solving ILPs. However, a significant challenge arises when applying GNNs to ILPs with symmetry: classic GNN architectures struggle to differentiate between symmetric variables, which limits their predictive accuracy. In this work, we investigate the properties of permutation equivariance and invariance in GNNs, particularly in relation to the inherent symmetry of ILP formulations. We reveal that the interaction between these two factors contributes to the difficulty of distinguishing between symmetric variables. To address this challenge, we explore the potential of feature augmentation and propose several guiding principles for constructing augmented features. Building on these principles, we develop an orbit-based augmentation scheme that first groups symmetric variables and then samples augmented features for each group from a discrete uniform distribution. Empirical results demonstrate that our proposed approach significantly enhances both training efficiency and predictive performance. Integer Linear Programs (ILPs) are fundamental optimization problems characterized by a linear objective function and linear constraints, where the decision variables are restricted to integer values. These problems play a critical role in various fields, including operations research, computer science, and engineering (Pochet & Wolsey, 2006; Liu & Fan, 2018; Watson & Woodruff, 2011; Luathep et al., 2011; Schöbel, 2001).

In the past few years, Graph Neural Networks (GNNs) [14, 43] have been emerging as one of the most powerful and successful techniques for graph representation learning. Message passing neural networks constitute a prevalent category of GNN models, which learn node features and graph structure information through recursively aggregating current representations of node and its neighbors. Diverse aggregation strategies have been introduced, giving rise to various GNN backbones, such as GCN, GIN, and among others [14, 15, 16, 17, 18]. However, the expressive power of these message passing GNNs is upper bounded by 1-dimensional Weisfeiler-Leman (1-WL) tests [18, 19] that encode a node's color via recursively expanding the neighbors of the node to construct a rooted subtree for the node. As shown in Figure 1, such rooted subtrees are with limited expressiveness and might be the same for graphs with different structures, leading to failure in distinguishing these graphs. This presents a bottleneck for applying WL tests or message passing neural networks to many real-world graph application domains. The failure of WL test is mainly due to the rooted subtree's limited capabilities in capturing different substructures that can appear in the graph. Since the message passing scheme of GNNs mimics the 1-WL algorithm, one intuition to enhance the expressive power of GNNs is to enrich the passing information, es-2 Figure 1: 1-and 2-WL tests fail to distinguish the two graphs as they obtain the same rooted subtree (node coloring).

In the feature space, the collapse between features invokes critical problems in representation learning by remaining the features undistinguished. Interpolation-based augmentation methods such as mixup have shown their effectiveness in relieving the collapse problem between different classes, called inter-class collapse. However, intra-class collapse raised in coarse-to-fine transfer learning has not been discussed in the augmentation approach. To address them, we propose a better feature augmentation method, asymptotic midpoint mixup. The method generates augmented features by interpolation but gradually moves them toward the midpoint of inter-class feature pairs. As a result, the method induces two effects: 1) balancing the margin for all classes and 2) only moderately broadening the margin until it holds maximal confidence. We empirically analyze the collapse effects by measuring alignment and uniformity with visualizing representations. Then, we validate the intra-class collapse effects in coarse-to-fine transfer learning and the inter-class collapse effects in imbalanced learning on long-tailed datasets. In both tasks, our method shows better performance than other augmentation methods.

By leveraging the representations learned in the past, we aim to augment the features Recent deep learning techniques have shown remarkable at each incremental stage to address data deficiency in progress in various computer vision tasks including image the classes belonging to old tasks. To this end, inspired by classification (He et al. 2016; Hu, Shen, and Sun 2018), object adversarial attacks, we adjust the feature representations of detection (Liu et al. 2016; Redmon et al. 2016; Zhu et al. training examples to resemble representations from specific 2021c), semantic segmentation (Chen et al. 2017; Long, target classes that are different from their original classes. Shelhamer, and Darrell 2015; Noh, Hong, and Han 2015), These perturbed features allow a new classifier to maintain and many others. Behind this success is an implicit assumption the decision boundaries for the classes learned up to the that the whole dataset with a predefined set of classes previous stages. Note that this is a novel perspective different should be given in a batch. However, this assumption is from conventional adversarial attack methods (Carlini unlikely to hold in the real-world scenarios which change and Wagner 2017; Goodfellow, Shlens, and Szegedy 2017; dynamically over time. This limits the applicability to realworld Madry et al. 2018; Moosavi-Dezfooli, Fawzi, and Frossard problems because deep neural networks trained under 2016; Zhao, Dua, and Singh 2018), which focus on deceiving changing data distribution often suffer from catastrophic forgetting, models. One may consider generating additional features meaning that the models lose the ability to maintain for each class using the exemplars with the same class labels.

In this work, we propose {\it Fair-CDA}, a fine-grained data augmentation strategy for imposing fairness constraints. We use a feature disentanglement method to extract the features highly related to the sensitive attributes. Then we show that group fairness can be achieved by regularizing the models on transition paths of sensitive features between groups. By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable. To alleviate the accuracy degradation caused by fairness constraints, we further introduce a calibrated model to impute labels for the augmented data. Our proposed method does not assume any data generative model and ensures good generalization for both accuracy and fairness. Experimental results show that Fair-CDA consistently outperforms state-of-the-art methods on widely-used benchmarks, e.g., Adult, CelebA and MovieLens. Especially, Fair-CDA obtains an 86.3\% relative improvement for fairness while maintaining the accuracy on the Adult dataset. Moreover, we evaluate Fair-CDA in an online recommendation system to demonstrate the effectiveness of our method in terms of accuracy and fairness.

Researching bragging behavior on social media arouses interest of computational (socio) linguists. However, existing bragging classification datasets suffer from a serious data imbalance issue. Because labeling a data-balance dataset is expensive, most methods introduce external knowledge to improve model learning. Nevertheless, such methods inevitably introduce noise and non-relevance information from external knowledge. To overcome the drawback, we propose a novel bragging classification method with disentangle-based representation augmentation and domain-aware adversarial strategy. Specifically, model learns to disentangle and reconstruct representation and generate augmented features via disentangle-based representation augmentation. Moreover, domain-aware adversarial strategy aims to constrain domain of augmented features to improve their robustness. Experimental results demonstrate that our method achieves state-of-the-art performance compared to other methods.

In multi-dimensional classification (MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented features. In this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented features. Specifically, by assuming that only part of the augmented features is pertinent and useful for each dimension's model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features.

Online metric learning has been widely exploited for large-scale data classification due to the low computational cost. However, amongst online practical scenarios where the features are evolving (e.g., some features are vanished and some new features are augmented), most metric learning models cannot be successfully applied into these scenarios although they can tackle the evolving instances efficiently. To address the challenge, we propose a new online Evolving Metric Learning (EML) model for incremental and decremental features, which can handle the instance and feature evolutions simultaneously by incorporating with a smoothed Wasserstein metric distance. Specifically, our model contains two essential stages: the Transforming stage (T-stage) and the Inheriting stage (I-stage). For the T-stage, we propose to extract important information from vanished features while neglecting non-informative knowledge, and forward it into survived features by transforming them into a low-rank discriminative metric space. It further explores the intrinsic low-rank structure of heterogeneous samples to reduce the computation and memory burden especially for highly-dimensional large-scale data. For the I-stage, we inherit the metric performance of survived features from the T-stage and then expand to include the augmented new features. Moreover, the smoothed Wasserstein distance is utilized to characterize the similarity relations among the complex and heterogeneous data, since the evolving features in the different stages are not strictly aligned. In addition to tackling the challenges in one-shot case, we also extend our model into multi-shot scenario. After deriving an efficient optimization method for both T-stage and I-stage, extensive experiments on several benchmark datasets verify the superiority of our model.