GenAI4ELab/papercli-papers huggingface dataset:
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
Authors: Aneesh Komanduri; Yongkai Wu; Feng Chen; Xintao Wu
Venue: IJCAI 2024 (main)

Abstract: Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels. We model causal mechanisms using nonlinear learnable flow-based diffeomorphic functions to map noise variables to latent causal variables. Further, to promote the disentanglement of causal factors, we propose a causal disentanglement prior learned from auxiliary labels and the latent causal structure. We theoretically show the identifiability of causal factors and mechanisms up to permutation and elementwise reparameterization. We empirically demonstrate that our framework induces highly disentangled causal factors, improves interventional robustness, and is compatible with counterfactual generation.
Efficient and Stable Offline-to-online Reinforcement Learning via Continual Policy Revitalization
Authors: Rui Kong; Chenyang Wu; Chen-Xiao Gao; Zongzhang Zhang; Ming Li
Venue: IJCAI 2024 (main)

Abstract: In offline Reinforcement Learning (RL), the pre-trained policies are utilized for initialization and subsequent online fine-tuning. However, existing methods suffer from instability and low sample efficiency compared to pure online learning. This paper identifies these limitations stemming from direct policy initialization using offline-trained policy models. We propose Continual Policy Revitalization (CPR) as a novel efficient, stable fine-tuning method. CPR incorporates a periodic policy revitalization technique, restoring the overtrained policy network to full learning capacity while ensuring stable initial performance. This approach enables fine-tuning without being adversely affected by low-quality pre-trained policies. In contrast to previous research, CPR initializes the new policy with an adaptive policy constraint in policy optimization. Such optimization keeps the new policy close to behavior policy constructed from historical policies. This contributes to stable policy improvement and optimal converged performance. Practically, CPR can seamlessly integrate into existing offline RL algorithms with minimal modification. We empirically validate the effectiveness of our method through extensive experiments, demonstrating substantial improvements in learning stability and efficiency compared to previous approaches. Our code is available at https://github.com/LAMDA-RL/CPR.
Exploiting Multi-Label Correlation in Label Distribution Learning
Authors: Zhiqiang Kou; Jing Wang; Jiawei Tang; Yuheng Jia; Boyu Shi; Xin Geng
Venue: IJCAI 2024 (main)

Abstract: Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Numerous LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space; among these, many exploited the low-rank structure of label distribution to capture label correlation. However, recent research has unveiled that label distribution matrices typically maintain full rank, posing a challenge to approaches relying on low-rank label correlation. Notably, low-rank label correlation finds widespread adoption in multi-label learning (MLL) literature due to the often low-rank nature of multi-label matrices. Inspired by that, we introduce an auxiliary MLL process within the LDL framework, focusing on capturing low-rank label correlation within this auxiliary MLL component rather than the LDL itself. By doing so, we adeptly exploited low-rank label correlation in our LDL methods. We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL.
ROCES: Robust Class Expression Synthesis in Description Logics via Iterative Sampling
Authors: N'Dah Jean Kouagou; Stefan Heindorf; Caglar Demir; Axel-Cyrille Ngonga Ngomo
Venue: IJCAI 2024 (main)

Abstract: We consider the problem of class expression learning using cardinality-minimal sets of examples. Recent class expression learning approaches employ deep neural networks and have demonstrated tremendous performance improvements in execution time and quality of the computed solutions. However, they lack generalization capabilities when it comes to the number of examples used in a learning problem, i.e., they often perform poorly on unseen learning problems where only a few examples are given. In this work, we propose a generalization of the classical class expression learning problem to address the limitations above. In short, our generalized learning problem (GLP) forces learning systems to solve the classical class expression learning problem using the smallest possible subsets of examples, thereby improving the learning systems' ability to solve unseen learning problems with arbitrary numbers of examples. Moreover, we develop ROCES, a learning algorithm for synthesis-based approaches to solve GLP. Experimental results suggest that post training, ROCES outperforms existing synthesis-based approaches on out-of-distribution learning problems while remaining highly competitive overall.
Look-ahead Search on Top of Policy Networks in Imperfect Information Games
Authors: Ondřej Kubíček; Neil Burch; Viliam Lisý
Venue: IJCAI 2024 (main)

Abstract: Search in test time is often used to improve the performance of reinforcement learning algorithms. Performing theoretically sound search in fully adversarial two-player games with imperfect information is notoriously difficult and requires a complicated training process. We present a method for adding test-time search to an arbitrary policy-gradient algorithm that learns from sampled trajectories. Besides the policy network, the algorithm trains an additional critic network, which estimates the expected values of players following various transformations of the policies given by the policy network. These values are then used for depth-limited search. We show how the values from this critic can create a value function for imperfect information games. Moreover, they can be used to compute the summary statistics necessary to start the search from an arbitrary decision point in the game. The presented algorithm is scalable to very large games since it does not require any search during train time. We evaluate the algorithm's performance when trained along Regularized Nash Dynamics, and we evaluate the benefit of using the search in the standard benchmark game of Leduc hold'em, multiple variants of imperfect information Goofspiel, and Battleships.
Towards a Framework for Learning of Algorithms: The Case of Learned Comparison Sorts
Authors: Philipp Kunz; Ilche Georgievski; Marco Aiello
Venue: IJCAI 2024 (main)

Abstract: Designing algorithms is cumbersome and error-prone. This, among other things, has increasingly led to efforts to extend or even replace designing algorithms with machine learning models. While previous research has demonstrated that some machine learning models possess Turing-completeness, the findings are largely theoretical, and solutions for specific algorithmic tasks remain unclear. With this in mind, we investigate the feasibility of learning representations of classical algorithms from data on their execution, enabling their application to different inputs. We propose a novel and general framework for algorithm learning consisting of a model of computation that facilitates algorithm analysis across various levels of abstraction. We formalize the problem of learning an algorithm using an algebraic approach for graph traversal. We apply this framework to comparison sorts and evaluate the inferred machine learning models' performance, demonstrating the applicability of the approach in terms of accuracy and sensitivity.
Deep Neural Networks via Complex Network Theory: A Perspective
Authors: Emanuele La Malfa; Gabriele La Malfa; Giuseppe Nicosia; Vito Latora
Venue: IJCAI 2024 (main)

Abstract: Deep Neural Networks (DNNs) can be represented as graphs whose links and vertices iteratively process data and solve tasks sub-optimally. Complex Network Theory (CNT), merging statistical physics with graph theory, provides a method for interpreting neural networks by analysing their weights and neuron structures. However, classic works adapt CNT metrics that only permit a topological analysis as they do not account for the effect of the input data. In addition, CNT metrics have been applied to a limited range of architectures, mainly including Fully Connected neural networks. In this work, we extend the existing CNT metrics with measures that sample from the DNNs' training distribution, shifting from a purely topological analysis to one that connects with the interpretability of deep learning. For the novel metrics, in addition to the existing ones, we provide a mathematical formalisation for Fully Connected, AutoEncoder, Convolutional and Recurrent neural networks, of which we vary the activation functions and the number of hidden layers. We show that these metrics differentiate DNNs based on the architecture, the number of hidden layers, and the activation function. Our contribution provides a method rooted in physics for interpreting DNNs that offers insights beyond the traditional input-output relationship and the CNT topological analysis.
A De-singularity Subgradient Approach for the Extended Weber Location Problem
Authors: Zhao-Rong Lai; Xiaotian Wu; Liangda Fang; Ziliang Chen
Venue: IJCAI 2024 (main)

Abstract: The extended Weber location problem is a classical optimization problem that has inspired some new works in several machine learning scenarios recently. However, most existing algorithms may get stuck due to the singularity at the data points when the power of the cost function 1\<= q<2, such as the widely-used iterative Weiszfeld approach. In this paper, we establish a de-singularity subgradient approach for this problem. We also provide a complete proof of convergence which has fixed some incomplete statements of the proofs for some previous Weiszfeld algorithms. Moreover, we deduce a new theoretical result of superlinear convergence for the iteration sequence in a special case where the minimum point is a singular point. We conduct extensive experiments in a real-world machine learning scenario to show that the proposed approach solves the singularity problem, produces the same results as in the non-singularity cases, and shows a reasonable rate of linear convergence. The results also indicate that the q-th power case (1<q<2) is more advantageous than the 1-st power case and the 2-nd power case in some situations. Hence the de-singularity subgradient approach is beneficial to advancing both theory and practice for the extended Weber location problem.
Toward a Manifold-Preserving Temporal Graph Network in Hyperbolic Space
Authors: Viet Quan Le; Viet Cuong Ta
Venue: IJCAI 2024 (main)

Abstract: Hyperbolic geometry provides an ideal setting to represent the scale-free or hierarchical characteristics of an input graph naturally. Utilizing hyperbolic geometry for learning dynamic graph representation has gained a growing interest in recent years. However, the majority of hyperbolic-based approaches rely on tangent spaces to perform graph operations, which could distort the structure of the dynamic graph when the graph grows over time. To avoid the distortion in tangent space, we propose a Hyperbolic Manifold-Preserving Temporal Graph Network that works directly on the hyperbolic manifold. The model includes a graph convolution module for learning the spatial dependencies, an attention architecture for capturing the temporal properties, and a gated recurrent unit for extracting the spatio-temporal relationships. By evaluating on diverse real-world dynamic graphs, our model has achieved significant improvements in link prediction and new link prediction tasks, in comparison with other baselines.
Unbiased Active Semi-supervised Binary Classification Models
Authors: JooChul Lee; Weidong Ma; Ziyang Wang
Venue: IJCAI 2024 (main)

Abstract: Active learning is known to be a well-motivated algorithm that aims to maximize model performance with relatively small data, but it introduces sampling bias due to active selection. To adjust the bias, current literature utilizes corrective weights in a supervised learning approach. However, those methods consider only a small amount of actively sampled data and thus estimation efficiency can be improved using unsampled data together. In this paper, we develop an actively improved augmented estimation equation (AI-AEE) based on corrective weights as well as imputation models that allow us to leverage unlabeled data. The asymptotic distribution of the proposed estimator as the solution to the AI-AEE is derived, and an optimal sampling scheme to minimize the asymptotic mean squared error of the estimator is proposed. We then propose a general practical algorithm for training prediction models in the active and semi-supervised learning framework. The superiority of our method is demonstrated on synthetic and real data examples.
Hypergraph Self-supervised Learning with Sampling-efficient Signals
Authors: Fan Li; Xiaoyang Wang; Dawei Cheng; Wenjie Zhang; Ying Zhang; Xuemin Lin
Venue: IJCAI 2024 (main)

Abstract: Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly based on contrastive methods with the instance-level discrimination strategy, suffering from two significant limitations: (1) They select negative samples arbitrarily, which is unreliable in deciding similar and dissimilar pairs, causing training bias. (2) They often require a large number of negative samples, resulting in expensive computational costs. To address the above issues, we propose SE-HSSL, a hypergraph SSL framework with three sampling-efficient self-supervised signals. Specifically, we introduce two sampling-free objectives leveraging the canonical correlation analysis as the node-level and group-level self-supervised signals. Additionally, we develop a novel hierarchical membership-level contrast objective motivated by the cascading overlap relationship in hypergraphs, which can further reduce membership sampling bias and improve the efficiency of sample utilization. Through comprehensive experiments on 7 real-world hypergraphs, we demonstrate the superiority of our approach over the state-of-the-art method in terms of both effectiveness and efficiency.
Combinatorial Routing for Neural Trees
Authors: Jiahao Li; Ruichu Cai; Yuguang Yan
Venue: IJCAI 2024 (main)

Abstract: Neural trees benefit from the high-level representation of neural networks and the interpretability of decision trees. Therefore, the existing works on neural trees perform outstandingly on various tasks such as architecture search. However, these works require every router to provide only one successor for each sample, causing the predictions to be dominated by the elite branch and its derivative architectures. To break this branch dominance, we propose the combinatorial routing neural tree method, termed CombRo. Unlike the previous methods employing unicast routing, CombRo performs multicast schema in each iteration, allowing the features to be routed to any combination of successors at every non-leaf. The weights of each architecture are then evaluated accordingly. We update the weights by training the routing subnetwork, and the architecture with the top weight is selected in the final step. We compare CombRo with the existing algorithms on 3 public image datasets, demonstrating its superior performance in terms of accuracy. Visualization results further validate the effectiveness of the multicast routing schema. Code is available at https://github.com/JiahaoLi-gdut/CombRo.
A Density-driven Iterative Prototype Optimization for Transductive Few-shot Learning
Authors: Jingcong Li; Chunjin Ye; Fei Wang; Jiahui Pan
Venue: IJCAI 2024 (main)

Abstract: Few-shot learning (FSL) poses a considerable challenge since it aims to improve the model generalization ability with limited labeled data. Previous works usually attempt to construct class-specific prototypes and then predict novel classes using these prototypes. However, the feature distribution represented by the limited labeled data is coarse-grained, leading to large information gap between the labeled and unlabeled data as well as biases in the prototypes. In this paper, we investigate the correlation between sample quality and density, and propose a Density-driven Iterative Prototype Optimization to acquire high-quality prototypes, and further improve few-shot learning performance. Specifically, the proposed method consists of two optimization strategies. The similarity-evaluating strategy is for capturing the information gap between the labeled and unlabeled data by reshaping the feature manifold for the novel feature distribution. The density-driven strategy is proposed to iteratively refine the prototypes in the direction of density growth. The proposed method could reach or even exceed the state-of-the-art performance on four benchmark datasets, including mini-ImageNet, tiered-ImageNet, CUB, and CIFAR-FS. The code will be available soon at https://github.com/tailofcat/DIPO.
DWLR: Domain Adaptation under Label Shift for Wearable Sensor
Authors: Juren Li; Yang Yang; Youmin Chen; Jianfeng Zhang; Zeyu Lai; Lujia Pan
Venue: IJCAI 2024 (main)

Abstract: Wearable sensors play a crucial role in real-world scenarios, such as human activity recognition, sleep monitoring and electrocardiogram monitoring. However, deploying classifiers on them is challenged by distribution shifts across users and devices. Unsupervised domain adaptation (UDA) is proposed to address this, yet existing methods mostly focus on feature distribution shift, neglecting the potential misclassification due to label shift. In this paper, we propose Domain adaptation under label shift for Wearable sensor with Learnable Reweighting (DWLR) to handle both feature and label shifts. Specifically, DWLR employs learnable reweighting to align label distributions between source and target domains. It incorporates elements of information gain during the reweighting process to counter potential distribution shift that could emerge from over-reliance on data with high-confidence pseudo labels. Importantly, since wearable sensor data is time-series data, and can be subjected to distribution shifts originating from either the time domain, the frequency domain, or both, DWLR performs reweighting and alignment separately in these two domains to more robustly handle potential feature distribution shifts. Extensive experiments on three distinct wearable sensor datasets demonstrate the effectiveness of DWLR, yielding a remarkable average performance improvement of 5.85%.
Continual Multi-Objective Reinforcement Learning via Reward Model Rehearsal
Authors: Lihe Li; Ruotong Chen; Ziqian Zhang; Zhichao Wu; Yi-Chen Li; Cong Guan; Yang Yu; Lei Yuan
Venue: IJCAI 2024 (main)

Abstract: Multi-objective reinforcement learning (MORL) approaches address real-world problems with multiple objectives by learning policies maximizing returns weighted by different user preferences. Typical methods assume the objectives remain unchanged throughout the agent's lifetime. However, in some real-world situations, the agent may encounter dynamically changing learning objectives, i.e., different vector-valued reward functions at different learning stages. This issue has not been considered in problem formulation or algorithm design. To address this issue, we formalize the setting as a continual MORL (CMORL) problem for the first time, accounting for the evolution of objectives throughout the learning process. Subsequently, we propose Continual Multi-Objective Reinforcement Learning via Reward Model Rehearsal (CORe3), incorporating a dynamic agent network for rapid adaptation to new objectives. Moreover, we develop a reward model rehearsal technique to recover the reward signals for previous objectives, thus alleviating catastrophic forgetting. Experiments on four CMORL benchmarks showcase that CORe3 effectively learns policies satisfying different preferences on all encountered objectives, and outperforms the best baseline by 171%, highlighting the capability of CORe3 to handle situations with evolving objectives.
DarkFed: A Data-Free Backdoor Attack in Federated Learning
Authors: Minghui Li; Wei Wan; Yuxuan Ning; Shengshan Hu; Lulu Xue; Leo Yu Zhang; Yichen Wang
Venue: IJCAI 2024 (main)

Abstract: Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development. To bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL. Specifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset. Subsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed.
Prompt Learning with Extended Kalman Filter for Pre-trained Language Models
Authors: Quan Li; Xike Xie; Chao Wang; S. Kevin Zhou
Venue: IJCAI 2024 (main)

Abstract: Prompt learning has gained popularity as a means to leverage the knowledge embedded in pre-trained language models (PLMs) for NLP tasks while using a limited number of trainable parameters. While it has shown promise in tasks like sentiment classification and natural language inference, generating suitable prompts for PLMs, as opposed to human prompts, remains a challenge. In this paper, we introduce an abstraction of the prompt learning process using an extended Kalman filter. Our approach, called Conditional Extended Kalman Filter based on Neural Networks (CEKFNN), effectively infers more appropriate prompt tokens by enhancing the classic extended Kalman filter with PLM's contextual representation power. Specifically, CEKFNN learns transition and emission functions from PLM embeddings of input sentences to infer latent prompt tokens. We refine CEKFNN using an alternate-training approach, retraining a PLM's emission function with prompt tokens inferred by prompt models (PMs), as well as the initial and transition functions. PLM's output labels assist in PMs' training. When updating the pre-trained language model (PLM), we use an adapter approach with few trainable parameters, leaving PLM parameters frozen. We evaluate CEKFNN across open-source PLMs, demonstrating performance improvements over state-of-the-art methods while using a limited number of trainable parameters. It shows that CEKFNN performs on-par or better than fine-tuning, which requires updating all parameters in the PLM.