GenAI4ELab/papercli-papers huggingface dataset:
Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting
Authors: Tiange Zhang; Qing Cai; Feng Gao; Lin Qi; Junyu Dong
Venue: IJCAI 2024 (main)

Abstract: Cross-Domain Few-Shot Learning has witnessed great stride with the development of meta-learning. However, most existing methods pay more attention to learning domain-adaptive inductive bias (meta-knowledge) through feature-wise manipulation or task diversity improvement while neglecting the phenomenon that deep networks tend to rely more on high-frequency cues to make the classification decision, which thus degenerates the robustness of learned inductive bias since high-frequency information is vulnerable and easy to be disturbed by noisy information. Hence in this paper, we make one of the first attempts to propose a Frequency-Aware Prompting method with mutual attention for Cross-Domain Few-Shot classification, which can let networks simulate the human visual perception of selecting different frequency cues when facing new recognition tasks. Specifically, a frequency-aware prompting mechanism is first proposed, in which high-frequency components of the decomposed source image are switched either with normal distribution sampling or zeroing to get frequency-aware augment samples. Then, a mutual attention module is designed to learn generalizable inductive bias under CD-FSL settings. More importantly, the proposed method is a plug-and-play module that can be directly applied to most off-the-shelf CD-FLS methods. Experimental results on CD-FSL benchmarks demonstrate the effectiveness of our proposed method as well as robustly improve the performance of existing CD-FLS methods. Resources at https://github.com/tinkez/FAP_CDFSC.
Skip-Timeformer: Skip-Time Interaction Transformer for Long Sequence Time-Series Forecasting
Authors: Wenchang Zhang; Hua Wang; Fan Zhang
Venue: IJCAI 2024 (main)

Abstract: Recent studies have raised questions about the suitability of the Transformer architecture for long sequence time-series forecasting. These forecasting models leverage Transformers to capture dependencies between multiple time steps in a time series, with embedding tokens composed of data from individual time steps. However, challenges arise when applying Transformers to predict long sequences with strong periodicity, leading to performance degradation and increased computational burden. Furthermore, embedding tokens formed one time step at a time may struggle to reveal meaningful information in long sequences, failing to capture correlations between different time steps. In this study, we propose Skip-Timeformer, a Transformer-based model that utilizes a skip-time interaction for long sequence time-series forecasting. Specifically, we decompose the time series into multiple subsequences based on different time intervals, embedding various time steps into variable tokens across multiple sequences. The skip-time interaction mechanism utilizes these variable tokens to capture dependencies in the skip-time dimension. Additionally, skip-time interaction is employed to learn dependencies between sequences missed by multiple skip time steps. The Skip-Timeformer model demonstrates state-of-the-art performance on various real-world datasets, further enhancing the long sequence forecasting capabilities of the Transformer variations and better adapting to arbitrary lookback windows.
Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization
Authors: Xuelin Zhang; Hong Chen; Bin Gu; Tieliang Gong; Feng Zheng
Venue: IJCAI 2024 (main)

Abstract: Stochastic bilevel optimization (SBO) has been integrated into many machine learning paradigms recently including hyperparameter optimization, meta learning, reinforcement learning, etc. Along with the wide range of applications, there have been abundant studies on concerning the computing behaviors of SBO. However, the generalization guarantees of SBO methods are far less understood from the lens of statistical learning theory. In this paper, we provide a systematical generalization analysis of the first-order gradient-based bilevel optimization methods. Firstly, we establish the quantitative connections between the on-average argument stability and the generalization gap of SBO methods. Then, we derive the upper bounds of on-average argument stability for single timescale stochastic gradient descent (SGD) and two timescale SGD, where three settings (nonconvex-nonconvex (NC-NC), convex-convex (C-C) and strongly-convex-strongly-convex (SC-SC)) are considered respectively. Experimental analysis validates our theoretical findings. Compared with the previous algorithmic stability analysis, our results do not require the re-initialization of the inner-level parameters before each iteration and are suit for more general objective functions.
Visual Attention Prompted Prediction and Learning
Authors: Yifei Zhang; Bo Pan; Siyi Gu; Guangji Bai; Meikang Qiu; Xiaofeng Yang; Liang Zhao
Venue: IJCAI 2024 (main)

Abstract: Visual explanation (attention)-guided learning uses not only labels but also explanations to guide the model reasoning process. While visual attention-guided learning has shown promising results, it requires a large number of explanation annotations that are time-consuming to prepare. However, in many real-world situations, it is usually desired to prompt the model with visual attention without model retraining. For example, when doing AI-assisted cancer classification on a medical image, users (e.g., clinicians) can provide the AI model with visual attention prompts on which areas are indispensable and which are precluded. Despite its promising objectives, achieving visual attention-prompted prediction presents several major challenges: 1) How can the visual prompt be effectively integrated into the model's reasoning process? 2) How should the model handle samples that lack visual prompts? 3) What is the impact on the model's performance when a visual prompt is imperfect? This paper introduces a novel framework for visual attention prompted prediction and learning, utilizing visual prompts to steer the model's reasoning process. To improve performance in non-prompted situations and align it with prompted scenarios, we propose a co-training approach for both non-prompted and prompted models, ensuring they share similar parameters and activation. Additionally, for instances where the visual prompt does not encompass the entire input image, we have developed innovative attention prompt refinement methods. These methods interpolate the incomplete prompts while maintaining alignment with the model's explanations. Extensive experiments on four datasets demonstrate the effectiveness of our proposed framework in enhancing predictions for samples both with and without prompt.
Personalized Federated Learning for Cross-City Traffic Prediction
Authors: Yu Zhang; Hua Lu; Ning Liu; Yonghui Xu; Qingzhong Li; Lizhen Cui
Venue: IJCAI 2024 (main)

Abstract: Traffic prediction plays an important role in urban computing. However, many cities face data scarcity due to low levels of urban development. Although many approaches transfer knowledge from data-rich cities to data-scarce cities, the centralized training paradigm cannot uphold data privacy. For the sake of inter-city data privacy, Federated Learning has been used, which follows a decentralized training paradigm to enhance traffic knowledge of data-scarce cities. However, spatio-temporal data heterogeneity causes client drift, leading to unsatisfactory traffic prediction performance. In this work, we propose a novel personalized Federated learning method for Cross-city Traffic Prediction (pFedCTP). It learns traffic knowledge from multiple data-rich source cities and transfers the knowledge to a data-scarce target city while preserving inter-city data privacy. In the core of pFedCTP lies a Spatio-Temporal Neural Network (ST-Net) for clients to learn traffic representation. We decouple the ST-Net to learn space-independent traffic patterns to overcome cross-city spatial heterogeneity. Besides, pFedCTP adaptively interpolates the layer-wise global and local parameters to deal with temporal heterogeneity across cities. Extensive experiments on four real-world traffic datasets demonstrate significant advantages of pFedCTP over representative state-of-the-art methods.
Pre-training General User Representation with Multi-type APP Behaviors
Authors: Yuren Zhang; Min Hou; Kai Zhang; Yuqing Yuan; Chao Song; Zhihao Ye; Enhong Chen; Yang Yu
Venue: IJCAI 2024 (main)

Abstract: In numerous user-centric services on mobile applications (apps), accurately mining user interests and generating effective user representations are paramount. Traditional approaches, which often involve training task-specific user representations, are becoming increasingly impractical due to their high computational costs and limited adaptability. This paper introduces a novel solution to this challenge: the Multi-type App-usage Fusion Network (MAFN). MAFN innovatively pre-trains universal user representations, leveraging multi-type app behaviors to overcome key limitations in existing methods. We address two primary challenges: 1) the varying frequency of user behaviors (ranging from low-frequency actions like (un)installations to high-frequency yet insightful app launches); and 2) the integration of multi-type behaviors to form a cohesive representation. Our approach involves the creation of novel pre-training tasks that harness self-supervised signals from diverse app behaviors, capturing both long-term and short-term user interests. MAFN's unique fusion approach effectively amalgamates these interests into a unified vector space, facilitating the development of a versatile, general-purpose user representation. With a practical workflow, extensive experiments with three typical downstream tasks on real-world datasets verify the effectiveness of our approach.
Reconfigurability-Aware Selection for Contrastive Active Domain Adaptation
Authors: Zeyu Zhang; Chun Shen; Shuai Lü; Shaojie Zhang
Venue: IJCAI 2024 (main)

Abstract: Active domain adaptation (ADA) aims to label a small portion of target samples to drastically improve the adaptation performance. The existing ADA methods mostly rely on the output of domain discriminator or the original prediction probability to design sample selection strategies and do not fully explore the semantic information of source and target domain features, which may lead to selecting the valueless target samples. Moreover, most of them require complex network structures (such as introducing additional domain discriminator, multiple classifiers, or loss predictors) and multiple query functions. In this work, we propose a concise but effective ADA method called Reconfigurability-Aware Selection for Contrastive active domain adaptation (RASC). With the reconfigurability-aware sample selection strategy, RASC can select the most valuable target samples for annotation in the presence of domain shift. To better utilize the selected target samples, we further design a contrastive learning-based gradual active domain adaptation framework. In addition, we propose a variant of RASC called RASC-Ob, which uses a simpler sample annotation method and supplements the learning of misclassified samples. Extensive experimental results on multiple benchmarks demonstrate the superiority of RASC.
Active Deep Multi-view Clustering
Authors: Helin Zhao; Wei Chen; Peng Zhou
Venue: IJCAI 2024 (main)

Abstract: Deep multi-view clustering has been widely studied. However, since it is an unsupervised task, where no labels are used to guide the training, it is still unreliable especially when handling complicated data. Although deep semi-supervised multi-view clustering can alleviate this problem by using some supervised information, the supervised information is often pregiven or randomly selected. Unfortunately, as we know, the clustering performance highly depends on the quality of the supervised information and most of the semi-supervised methods ignore the supervised information selection. To tackle this problem, in this paper, we propose a novel active deep multi-view clustering method, which can actively select important data for querying human annotations. In this method, we carefully design a fusion module, an active selection module, a supervised module, and an unsupervised module, and integrate them into a unified framework seamlessly. In this framework, we can obtain a more reliable clustering result with as few annotations as possible. The extensive experiments on benchmark data sets show that our method can outperform state-of-the-art unsupervised and semi-supervised methods, demonstrating the effectiveness and superiority of the proposed method. The code is available at https://github.com/wodedazhuozi/ADMC .
ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
Authors: Kai Zhao; Jianye Hao; Yi Ma; Jinyi Liu; Yan Zheng; Zhaopeng Meng
Venue: IJCAI 2024 (main)

Abstract: Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience. However, learning solely from a static dataset can limit the performance due to the lack of exploration. To overcome it, offline-to-online RL combines offline pre-training with online fine-tuning, which enables the agent to further refine its policy by interacting with the environment in real-time. Despite its benefits, existing offline-to-online RL methods suffer from performance degradation and slow improvement during the online phase. To tackle these challenges, we propose a novel framework called ENsemble-based Offline-To-Online (ENOTO) RL. By increasing the number of Q-networks, we seamlessly bridge offline pre-training and online fine-tuning without degrading performance. Moreover, to expedite online performance enhancement, we appropriately loosen the pessimism of Q-value estimation and incorporate ensemble-based exploration mechanisms into our framework. Experimental results demonstrate that ENOTO can substantially improve the training stability, learning efficiency, and final performance of existing offline RL methods during online fine-tuning on a range of locomotion and navigation tasks, significantly outperforming existing offline-to-online RL methods.
NanoAdapt: Mitigating Negative Transfer in Test Time Adaptation with Extremely Small Batch Sizes
Authors: Shiji Zhao; Shao-Yuan Li; Sheng-Jun Huang
Venue: IJCAI 2024 (main)

Abstract: Test Time Adaptation (TTA) has garnered significant attention in recent years, with the research focus on addressing distribution shifts during test time. As one fundamental component of many TTA methods, the Batch Normalization (BN) layer plays a crucial role in enabling the model adaptability. However, existing BN strategies can prove detrimental when the batch size is (extremely) small. In numerous real-world scenarios, limited hardware resources or just-in-time demand often necessitates adjusting models with very small batch sizes, making existing methods less practical. In this paper, we first showcase and thoroughly analyze the negative transfer phenomenon in previous TTA methods encountering extremely small batch sizes. Subsequently, we propose a novel batch size-agnostic method called NanoAdapt to effectively mitigate the negative transfer even with batch size 1. NanoAdapt is composed of three key components: a dynamic BN calibration strategy that leverages historical information and the Taylor series to refine the statistics estimations, an entropy-weighted gradient accumulation strategy that uses the entropy of each sample's label prediction to weigh and accumulate the loss for backpropagation, and a novel proxy computation graph to capture the sample interactions. Extensive experiments are conducted to validate the superiority of NanoAdapt, showing its consistent efficacy in improving existing TTA methods.
Towards Robust Multi-Label Learning against Dirty Label Noise
Authors: Yuhai Zhao; Yejiang Wang; Zhengkui Wang; Wen Shan; Miaomiao Huang; Meixia Wang; Min Huang; Xingwei Wang
Venue: IJCAI 2024 (main)

Abstract: In multi-label learning, one of the major challenges is that the data are associated with label noise including the random noisy labels (e.g., data encoding errors) and noisy labels created by annotators (e.g., missing, extra, or error label), where noise is promoted by different structures (e.g., gaussian, sparse or subjective). Existing methods are tailored to handle noise with one specific structure. However, they lack of consideration of the fact that the data are always with dirty noisy labels, simutaneously gaussian, sparse and subjective, in real applications. In this paper, we formalize the multi-label learning with dirty noise as a new learning problem, namely Noisy Multi-label Learning (NML). To solve the NML problem, we decompose a corrupted label matrix as the noise matrix plus a true label matrix (maybe high-rank). For the noise matrix, a mixed norm penalty is developed as regularizer for dirty noise distribution. Under this norm, the conditions required for exact noise recovery are provided theoretically. For the true label matrix that is not necessarily low-rank, we apply a non-linear mapping to ensure its low-rankness such that the high-order label correlation can be utilized. Experimental results show that the proposed method outperforms the state-of-the-art methods significantly.
Protecting Split Learning by Potential Energy Loss
Authors: Fei Zheng; Chaochao Chen; Lingjuan Lyu; Xinyi Fu; Xing Fu; Weiqiang Wang; Xiaolin Zheng; Jianwei Yin
Venue: IJCAI 2024 (main)

Abstract: As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry. However, its security is constantly being questioned since the intermediate results are shared during training and inference. In this paper, we focus on the privacy leakage from the forward embeddings of split learning. Specifically, since the forward embeddings contain too much information about the label, the attacker can either use a few labeled samples to fine-tune the top model or perform unsupervised attacks such as clustering to infer the true labels from the forward embeddings. To prevent such kind of privacy leakage, we propose the potential energy loss to make the forward embeddings more 'complicated', by pushing embeddings of the same class towards the decision boundary. Therefore, it is hard for the attacker to learn from the forward embeddings. Experiment results show that our method significantly lowers the performance of both fine-tuning attacks and clustering attacks.
Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation
Authors: Guangtao Zheng; Wenqian Ye; Aidong Zhang
Venue: IJCAI 2024 (main)

Abstract: Deep neural classifiers tend to rely on spurious correlations between spurious attributes of inputs and targets to make predictions, which could jeopardize their generalization capability. Training classifiers robust to spurious correlations typically relies on annotations of spurious correlations in data, which are often expensive to get. In this paper, we tackle an annotation-free setting and propose a self-guided spurious correlation mitigation framework. Our framework automatically constructs fine-grained training labels tailored for a classifier obtained with empirical risk minimization to improve its robustness against spurious correlations. The fine-grained training labels are formulated with different prediction behaviors of the classifier identified in a novel spuriousness embedding space. We construct the space with automatically detected conceptual attributes and a novel spuriousness metric which measures how likely a class-attribute correlation is exploited for predictions. We demonstrate that training the classifier to distinguish different prediction behaviors reduces its reliance on spurious correlations without knowing them a priori and outperforms prior methods on five real-world datasets.
Constrained Intrinsic Motivation for Reinforcement Learning
Authors: Xiang Zheng; Xingjun Ma; Chao Shen; Cong Wang
Venue: IJCAI 2024 (main)

Abstract: This paper investigates two fundamental problems that arise when utilizing Intrinsic Motivation (IM) for reinforcement learning in Reward-Free Pre-Training (RFPT) tasks and Exploration with Intrinsic Motivation (EIM) tasks: 1) how to design an effective intrinsic objective in RFPT tasks, and 2) how to reduce the bias introduced by the intrinsic objective in EIM tasks. Existing IM methods suffer from static skills, limited state coverage, sample inefficiency in RFPT tasks, and suboptimality in EIM tasks. To tackle these problems, we propose Constrained Intrinsic Motivation (CIM) for RFPT and EIM tasks, respectively: 1) CIM for RFPT maximizes the lower bound of the conditional state entropy subject to an alignment constraint on the state encoder network for efficient dynamic and diverse skill discovery and state coverage maximization; 2) CIM for EIM leverages constrained policy optimization to adaptively adjust the coefficient of the intrinsic objective to mitigate the distraction from the intrinsic objective. In various MuJoCo robotics environments, we empirically show that CIM for RFPT greatly surpasses fifteen IM methods for unsupervised skill discovery in terms of skill diversity, state coverage, and fine-tuning performance. Additionally, we showcase the effectiveness of CIM for EIM in redeeming intrinsic rewards when task rewards are exposed from the beginning. Our code is available at https://github.com/x-zheng16/CIM.
Cross-View Contrastive Fusion for Enhanced Molecular Property Prediction
Authors: Yan Zheng; Song Wu; Junyu Lin; Yazhou Ren; Jing He; Xiaorong Pu; Lifang He
Venue: IJCAI 2024 (main)

Abstract: Machine learning based molecular property prediction has been a hot topic in the field of computer aided drug discovery (CADD). However, current MPP methods face two prominent challenges: 1) single-view MPP methods do not sufficiently exploit the complementary information of molecular data across multiple views, generally producing suboptimal performance, and 2) most existing multi-view MPP methods ignore the disparities in data quality among different views, inadvertently introducing the risk of models being overshadowed by inferior views. To address the above challenges, we introduce a novel cross-view contrastive fusion for enhanced molecular property prediction method (MolFuse). First, we extract intricate molecular semantics and structures from both sequence and graph views to leverage the complementarity of multi-view data. Then, MolFuse employs two distinct graphs, the atomic graph and chemical bond graph, to enhance the representation of the molecular graph, allow us to integrate both the fundamental backbone attributes and the nuanced shape characteristics. Notably, we incorporate a dual learning mechanism to refine the initial feature representations, and global features are obtained by maximizing the coherence among diverse view-specific molecular representations for the downstream task. The overall learning processes are combined into a unified optimization problem for iterative training. Experiments on multiple benchmark datasets demonstrate the superiority of our MolFuse.