GenAI4ELab/papercli-papers huggingface dataset:
SketchEdit: Editing Freehand Sketches at the Stroke-Level
Authors: Tengjie Li; Shikui Tu; Lei Xu
Venue: IJCAI 2024 (main)

Abstract: Recent sketch synthesis methods have demonstrated the capability of generating lifelike outcomes. However, these methods directly encode the entire sketches making it challenging to decouple the strokes from the sketches and have difficulty in controlling local sketch synthesis, e.g., stroke editing. Besides, the sketch editing task encounters the issue of accurately positioning the edited strokes, because users may not be able to draw on the exact position and the same stroke may appear in various locations in different sketches. We propose SketchEdit to realize flexible editing of sketches at the stroke-level for the first time. To tackle the challenge of decoupling strokes, SketchEdit divides a drawing sequence of a sketch into a series of strokes based on the pen state, aligns the stroke segments to have the same starting position, and learns the embeddings of every stroke by a proposed stroke encoder. Moreover, we overcome the problem of stroke placement via a diffusion process, which progressively generates the locations for the strokes to be synthesized, using the stroke features as the guiding condition. Experiments demonstrate that SketchEdit is effective for stroke-level sketch editing and sketch reconstruction. The source code is publicly available at https://github.com/CMACH508/SketchEdit/.
No Regularization Is Needed: Efficient and Effective Incomplete Label Distribution Learning
Authors: Xiang Li; Songcan Chen
Venue: IJCAI 2024 (main)

Abstract: In reality, it is laborious to obtain complete label degrees, giving birth to Incomplete Label Distribution Learning (InLDL), where some degrees are missing. Existing InLDL methods often assume that degrees are uniformly random missing. However, it is often not the case in practice, which arises the first issue. Besides, they often adopt explicit regularization to compensate the incompleteness, leading to burdensome parameter tuning and extra computation, causing the second issue. To address the first issue, we adopt a more practical setting, i.e., small degrees are more prone to be missing, since large degrees are likely to catch more attention. To tackle the second issue, we argue that label distribution itself already contains abundant knowledge, such as label correlation and ranking order, thus it may have provided enough prior for learning. It is precisely because existing methods overlook such a prior that leads to the forced adoption of explicit regularization. By directly utilizing the label degrees prior, we design a properly weighted objective function, exempting the need from explicit regularization. Moreover, we provide rigorous theoretical analysis, revealing in principle that the weighting plays an implicit regularization role. To sum up, our method has four advantages, it is 1) model selection free; 2) with closed-form solution (sub-problem) and easy-to-implement (a few lines of codes); 3) with linear computational complexity in the number of samples, thus scalable to large datasets; 4) competitive with state-of-the-arts in both random and non-random missing scenarios.
WPML3CP: Wasserstein Partial Multi-Label Learning with Dual Label Correlation Perspectives
Authors: Ximing Li; Yuanchao Dai; Bing Wang; Changchun Li; Renchu Guan; Fangming Gu; Jihong Ouyang
Venue: IJCAI 2024 (main)

Abstract: Partial multi-label learning (PMLL) refers to a weakly-supervised classification problem, where each instance is associated with a set of candidate labels, covering its ground-truth labels but also with irrelevant ones. The current methodology of PMLL is to estimate the ground-truth confidences of candidate labels, i.e., the likelihood of a candidate label being a ground-truth one, and induce the multi-label predictor with them, rather than the candidate labels. In this paper, we aim to estimate precise ground-truth confidences by leveraging precise label correlations, which are also required to estimate. To this end, we propose to capture label correlations from both measuring and modeling perspectives. Specifically, we measure the loss between ground-truth confidences and predictions by employing the Wasserstein distance involving label correlations; and form a label correlation-aware regularization to constraint predictive parameters. The two techniques are coupled to promote precise estimations of label correlations. Upon these ideas, we propose a novel PMLL method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (WPML3CP). We conduct extensive experiments on several benchmark datasets. Empirical results demonstrate that WPML3CP can outperform the existing PMLL baselines.
Fast Unpaired Multi-view Clustering
Authors: Xingfeng Li; Yuangang Pan; Yinghui Sun; Quansen Sun; Ivor Tsang; Zhenwen Ren
Venue: IJCAI 2024 (main)

Abstract: Anchor based pair-wised multi-view clustering often assumes multi-view data are paired, and has demonstrated significant advancements in recent years. However, this presumption is easily violated, and data is commonly unpaired fully in practical applications due to the influence of data collection and storage processes. Addressing unpaired large-scale multi-view data through anchor learning remains a research gap. The absence of pairing in multi-view data disrupts the consistency and complementarity of multiple views, posing significant challenges in learning powerful and meaningful anchors and bipartite graphs from unpaired multi-view data. To tackle this challenge, this study proposes a novel Fast Unpaired Multi-view Clustering (FUMC) framework for fully unpaired large-scale multi-view data. Specifically, FUMC first designs an inverse local manifold learning paradigm to guide the learned anchors for effective pairing and balancing, ensuring alignment, fairness, and power in unpaired multi-view data. Meanwhile, a novel bipartite graph matching framework is developed to align unpaired bipartite graphs, creating a consistent bipartite graph from unpaired multi-view data. The efficacy, efficiency, and superiority of our FUMC are corroborated through extensive evaluations on numerous benchmark datasets with shallow and deep SOTA methods.
Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations
Authors: Ye Li; Siqi Chen; Bin Shan; Sheng-Jun Huang
Venue: IJCAI 2024 (main)

Abstract: Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning. However, PINNs face training difficulties for evolutionary PDEs, particularly for dynamical systems whose solutions exhibit multi-scale or turbulent behavior over time. The reason is that PINNs may violate the temporal causality property since all the temporal features in the PINNs loss are trained simultaneously. This paper proposes to use implicit time differencing schemes to enforce temporal causality, and use transfer learning to sequentially update the PINNs in space as surrogates for PDE solutions in different time frames. The evolving PINNs are better able to capture the varying complexities of the evolutionary equations, while only requiring minor updates between adjacent time frames. Our method is theoretically proven to be convergent if the time step is small and each PINN in different time frames is well-trained. In addition, we provide state-of-the-art (SOTA) numerical results for a variety of benchmarks for which existing PINNs formulations may fail or be inefficient. We demonstrate that the proposed method improves the accuracy of PINNs approximation for evolutionary PDEs and improves efficiency by a factor of 4–40x. The code is available at https://github.com/SiqiChen9/TL-DPINNs.
Angluin-Style Learning of Deterministic Büchi and Co-Büchi Automata
Authors: Yong Li; Sven Schewe; Qiyi Tang
Venue: IJCAI 2024 (main)

Abstract: While recently developed Angluin-style learning algorithms for omega-automata have much in common with her classic DFA learning algorithm, there is a huge difference in the cost of the equivalence queries about the target automata. For omega-regular languages, the target is to learn nondeterministic Buchi automata (NBAs) through the vehicle of Families of DFAs (FDFAs). While the cost of equivalence queries is usually idealised as constant in learning, it makes a practical difference that the language equivalence checking about the learned NBAs is computationally hard. We develop efficient techniques for the cases, where we learn deterministic Buchi automata (DBAs) or deterministic co-Buchi automata (DCAs). This is based on the observation that some classes of FDFAs can be used to learn DBAs for DBA recognisable languages, rather than having to resort to nondeterministic ones. We believe that the restriction to DBAs and DCAs in equivalence queries also makes our algorithm more appealing to realistic applications, as the operations are cheap---NL---for DBAs and DCAs.
Efficiency Calibration of Implicit Regularization in Deep Networks via Self-paced Curriculum-Driven Singular Value Selection
Authors: Zhe Li; Shuo Chen; Jian Yang; Lei Luo
Venue: IJCAI 2024 (main)

Abstract: The generalization of neural networks has been a major focus of research in deep learning. It is often interpreted as an implicit bias towards solutions with specific properties. Especially, in practical applications, it has been observed that linear neural networks (LNN) tend to favor low-rank solutions for matrix completion tasks. However, most existing methods rely on increasing the depth of the neural network to enhance the low rank of solutions, resulting in higher complexity. In this paper, we propose a new explicit regularization method that calibrates the implicit bias towards low-rank trends in matrix completion tasks. Our approach automatically incorporates smaller singular values into the training process using a self-paced learning strategy, gradually restoring matrix information. By jointly using both implicit and explicit regularization, we effectively capture the low-rank structure of LNN and accelerate its convergence. We also analyze how our proposed penalty term interacts with implicit regularization and provide theoretical guarantees for our new model. To evaluate the effectiveness of our method, we conduct a series of experiments on both simulated and real-world data. Our experimental results clearly demonstrate that our method has better robustness and generalization ability compared with other methods.
Efficient Offline Meta-Reinforcement Learning via Robust Task Representations and Adaptive Policy Generation
Authors: Zhengwei Li; Zhenyang Lin; Yurou Chen; Zhiyong Liu
Venue: IJCAI 2024 (main)

Abstract: Zero-shot adaptation is crucial for agents facing new tasks. Offline Meta-Reinforcement Learning (OMRL), utilizing offline multi-task datasets to train policies, offers a way to attain this ability. Although most OMRL methods construct task representations via contrastive learning and merge them with states for policy input, these methods may have inherent problems. Specifically, integrating task representations with states for policy input limits learning efficiency, due to failing to leverage the similarities among tasks. Moreover, uniformly sampling an equal number of negative samples from different tasks in contrastive learning can hinder differentiation of more similar tasks, potentially diminishing task representation robustness. In this paper, we introduce an OMRL algorithm to tackle the aforementioned issues. We design a network structure for efficient learning by leveraging task similarity. It features shared lower layers for common feature extraction with a hypernetworks-driven upper layer, customized to process features per task's attributes. Furthermore, to achieve robust task representations for generating task-specific control policies, we utilize contrastive learning and introduce a novel method to construct negative sample pairs based on task similarity. Experimental results show that our method notably boosts learning efficiency and zero-shot adaptation in new tasks, surpassing previous methods across multiple challenging domains.
FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
Authors: Zhuohua Li; Maoli Liu; John C. S. Lui
Venue: IJCAI 2024 (main)

Abstract: Conversational recommender systems have emerged as a potent solution for efficiently eliciting user preferences. These systems interactively present queries associated with "key terms" to users and leverage user feedback to estimate user preferences more efficiently. Nonetheless, most existing algorithms adopt a centralized approach. In this paper, we introduce FedConPE, a phase elimination-based federated conversational bandit algorithm, where M agents collaboratively solve a global contextual linear bandit problem with the help of a central server while ensuring secure data management. To effectively coordinate all the clients and aggregate their collected data, FedConPE uses an adaptive approach to construct key terms that minimize uncertainty across all dimensions in the feature space. Furthermore, compared with existing federated linear bandit algorithms, FedConPE offers improved computational and communication efficiency as well as enhanced privacy protections. Our theoretical analysis shows that FedConPE is minimax near-optimal in terms of cumulative regret. We also establish upper bounds for communication costs and conversation frequency. Comprehensive evaluations demonstrate that FedConPE outperforms existing conversational bandit algorithms while using fewer conversations.
BATON: Aligning Text-to-Audio Model Using Human Preference Feedback
Authors: Huan Liao; Haonan Han; Kai Yang; Tianjiao Du; Rui Yang; Qinmei Xu; Zunnan Xu; Jingquan Liu; Jiasheng Lu; Xiu Li
Venue: IJCAI 2024 (main)

Abstract: With the development of AI-Generated Content (AIGC), text-to-audio models are gaining widespread attention. However, it is challenging for these models to generate audio aligned with human preference due to the inherent information density of natural language and limited model understanding ability. To alleviate this issue, we formulate the BATON, the first framework specifically designed to enhance the alignment between generated audio and text prompt using human preference feedback. Our BATON comprises three key stages: Firstly, we curated a dataset containing both prompts and the corresponding generated audio, which was then annotated based on human feedback. Secondly, we introduced a reward model using the constructed dataset, which can mimic human preference by assigning rewards to input text-audio pairs. Finally, we employed the reward model to fine-tune an off-the-shelf text-to-audio model. The experiment results demonstrate that our BATON can significantly improve the generation quality of the original text-to-audio models, concerning audio integrity, temporal relationship, and alignment with human preference. Project page is available at https://baton2024.github.io.
Scalable Federated Unlearning via Isolated and Coded Sharding
Authors: Yijing Lin; Zhipeng Gao; Hongyang Du; Dusit Niyato; Gui Gui; Shuguang Cui; Jinke Ren
Venue: IJCAI 2024 (main)

Abstract: Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models. However, the federated unlearning process often introduces extensive storage overhead and consumes substantial computational resources, thus hindering its implementation in practice. To address this issue, this paper proposes a scalable federated unlearning framework based on isolated sharding and coded computing. We first divide distributed clients into multiple isolated shards across stages to reduce the number of clients being affected. Then, to reduce the storage overhead of the central server, we develop a coded computing mechanism by compressing the model parameters across different shards. In addition, we provide the theoretical analysis of time efficiency and storage effectiveness for the isolated and coded sharding. Finally, extensive experiments on two typical learning tasks, i.e., classification and generation, demonstrate that our proposed framework can achieve better performance than three state-of-the-art frameworks in terms of accuracy, retraining time, storage overhead, and F1 scores for resisting membership inference attacks.
Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments
Authors: Yujie Lin; Chen Zhao; Minglai Shao; Baoluo Meng; Xujiang Zhao; Haifeng Chen
Venue: IJCAI 2024 (main)

Abstract: Recognizing domain generalization as a commonplace challenge in machine learning, data distribution might progressively evolve across a continuum of sequential domains in practical scenarios. While current methodologies primarily concentrate on bolstering model effectiveness within these new domains, they tend to neglect issues of fairness throughout the learning process. In response, we propose an innovative framework known as Disentanglement for Counterfactual Fairness-aware Domain Generalization (DCFDG). This approach adeptly removes domain-specific information and sensitive information from the embedded representation of classification features. To scrutinize the intricate interplay between semantic information, domain-specific information, and sensitive attributes, we systematically partition the exogenous factors into four latent variables. By incorporating fairness regularization, we utilize semantic information exclusively for classification purposes. Empirical validation on synthetic and authentic datasets substantiates the efficacy of our approach, demonstrating elevated accuracy levels while ensuring the preservation of fairness amidst the evolving landscape of continuous domains.
Dual Semantic Fusion Hashing for Multi-Label Cross-Modal Retrieval
Authors: Kaiming Liu; Yunhong Gong; Yu Cao; Zhenwen Ren; Dezhong Peng; Yuan Sun
Venue: IJCAI 2024 (main)

Abstract: Cross-modal hashing (CMH) has been widely used for multi-modal retrieval tasks due to its low storage cost and fast query speed. Although existing CMH methods achieve promising performance, most of them mainly rely on coarse-grained supervision information (\ie pairwise similarity matrix) to measure the semantic similarities between all instances, ignoring the impact of multi-label distribution. To address this issue, we construct fine-grained semantic similarity to explore the cluster-level semantic relationships between multi-label data, and propose a new dual semantic fusion hashing (DSFH) for multi-label cross-modal retrieval. Specifically, we first learn the modal-specific representation and consensus hash codes, thereby merging the specificity with consistency. Then, we fuse the coarse-grained and fine-grained semantics to mine multiple-level semantic relationships, thereby enhancing hash codes discrimination. Extensive experiments on three benchmarks demonstrate the superior performance of our DSFH compared with 16 state-of-the-art methods.
Meta-Learning via PAC-Bayesian with Data-Dependent Prior: Generalization Bounds from Local Entropy
Authors: Shiyu Liu; Wei Shi; Zenglin Xu; Shaogao Lv; Yehong Zhang; Hui Wang
Venue: IJCAI 2024 (main)

Abstract: Meta-learning accelerates the learning process on unseen learning tasks by acquiring prior knowledge through previous related tasks. The PAC-Bayesian theory provides a theoretical framework to analyze the generalization of meta-learning to unseen tasks. However, previous works still encounter two notable limitations: (1) they merely focus on the data-free priors, which often result in inappropriate regularization and loose generalization bounds; (2) more importantly, their optimization process usually involves nested optimization problems, incurring significant computational costs. To address these issues, we derive new generalization bounds and introduce a novel PAC-Bayesian framework for meta-learning that integrates data-dependent priors. This framework enables the extraction of optimal posteriors for each task in closed form, thereby allowing us to minimize generalization bounds incorporated data-dependent priors with only a simple local entropy. The resulting algorithm, which employs SGLD for sampling from the optimal posteriors, is stable, efficient, and computationally lightweight, eliminating the need for nested optimization. Extensive experimental results demonstrate that our proposed method outperforms the other baselines.
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator
Authors: Xiao-Yin Liu; Xiao-Hu Zhou; Guotao Li; Hao Li; Mei-Jiang Gui; Tian-Yu Xiang; De-Xing Huang; Zeng-Guang Hou
Venue: IJCAI 2024 (main)

Abstract: Offline reinforcement learning (RL) faces a significant challenge of distribution shift. Model-free offline RL penalizes the Q value for out-of-distribution (OOD) data or constrains the policy closed to the behavior policy to tackle this problem, but this inhibits the exploration of the OOD region. Model-based offline RL, which uses the trained environment model to generate more OOD data and performs conservative policy optimization within that model, has become an effective method for this problem. However, the current model-based algorithms rarely consider agent robustness when incorporating conservatism into policy. Therefore, the new model-based offline algorithm with a conservative Bellman operator (MICRO) is proposed. This method trades off performance and robustness via introducing the robust Bellman operator into the algorithm. Compared with previous model-based algorithms with robust adversarial models, MICRO can significantly reduce the computation cost by only choosing the minimal Q value in the state uncertainty set. Extensive experiments demonstrate that MICRO outperforms prior RL algorithms in offline RL benchmark and is considerably robust to adversarial perturbations.
Provable Acceleration of Nesterov’s Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks
Authors: Xin Liu; Wei Tao; Wei Li; Dazhi Zhan; Jun Wang; Zhisong Pan
Venue: IJCAI 2024 (main)

Abstract: Due to its simplicity and efficiency, the first-order gradient method has been extensively employed in training neural networks. Although the optimization problem of the neural network is non-convex, recent research has proved that the first-order method is capable of attaining a global minimum during training over-parameterized neural networks, where the number of parameters is significantly larger than that of training instances. Momentum methods, including the heavy ball (HB) method and Nesterov's accelerated gradient (NAG) method, are the workhorse of first-order gradient methods owning to their accelerated convergence. In practice, NAG often exhibits superior performance than HB. However, current theoretical works fail to distinguish their convergence difference in training neural networks. To fill this gap, we consider the training problem of the two-layer ReLU neural network under over-parameterization and random initialization. Leveraging high-resolution dynamical systems and neural tangent kernel (NTK) theory, our result not only establishes tighter upper bounds of the convergence rate for both HB and NAG, but also provides the first theoretical guarantee for the acceleration of NAG over HB in training neural networks. Finally, we validate our theoretical results on three benchmark datasets.