bluuebunny/crossref_metadata_2025 huggingface dataset:
The Trembling-Hand Problem for LTLf Planning
Authors: Yu Pian , Zhu Shufang , De Giacomo Giuseppe , Kwiatkowska Marta , Vardi Moshe
Published: July 2024

Consider an agent acting to achieve its temporal goal, but with a ``trembling hand". In this case, the agent may mistakenly instruct, with a certain (typically small) probability, actions that are not intended due to faults or imprecision in its action selection mechanism, thereby leading to possible goal failure. We study the trembling-hand problem in the context of reasoning about actions and planning for temporally extended goals expressed in Linear Temporal Logic on finite traces (LTLf), where we want to synthesize a strategy (aka plan) that maximizes the probability of satisfying the LTLf goal in spite of the trembling hand. We consider both deterministic and nondeterministic (adversarial) domains. We propose solution techniques for both cases by relying respectively on Markov Decision Processes and on Markov Decision Processes with Set-valued Transitions with LTLf objectives, where the set-valued probabilistic transitions capture both the nondeterminism from the environment and the possible action instruction errors from the agent. We formally show the correctness of our solution techniques and demonstrate their effectiveness experimentally through a proof-of-concept implementation.
Fine-tuning Pre-trained Models for Robustness under Noisy Labels
Authors: Ahn Sumyeong , Kim Sihyeon , Ko Jongwoo , Yun Se-Young
Published: July 2024

The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. In response to this issue, researchers have focused on identifying clean samples and reducing the influence of noisy labels. Recent works in this field have achieved notable success in terms of generalizability, albeit at the expense of extensive computing resources. Therefore, reducing computational costs remains a crucial challenge. Concurrently, in other research areas, there has been a focus on developing fine-tuning techniques to efficiently achieve high generalization performance. Despite their proven efficiently achievable generalization capabilities, these techniques have seen limited exploration from a label noise point of view. In this research, we aim to find an effective approach to fine-tune pre-trained models for noisy labeled datasets. To achieve this goal, we empirically investigate the characteristics of pre-trained models on noisy labels and propose an algorithm, named TURN. We present the results of extensive testing and demonstrate both efficient and improved denoising performance on various benchmarks, surpassing previous methods.
Contract Scheduling with Distributional and Multiple Advice
Authors: Angelopoulos Spyros , Bienkowski Marcin , Dürr Christoph , Simon Bertrand
Published: July 2024

Contract scheduling is a widely studied framework for designing real-time systems with interruptible capabilities. Previous work has showed that a prediction on the interruption time can help improve the performance of contract-based systems, however it has relied on a single prediction that is provided by a deterministic oracle. In this work, we introduce and study more general and realistic learning-augmented settings in which the prediction is in the form of a probability distribution, or it is given as a set of multiple possible interruption times. For both prediction settings, we design and analyze schedules which perform optimally if the prediction is accurate, while simultaneously guaranteeing the best worst-case performance if the prediction is adversarial. We also provide evidence that the resulting system is robust to prediction errors in the distributional setting. Last, we present an experimental evaluation that confirms the theoretical findings, and illustrates the performance improvements that can be attained in practice.
On the Computation of Example-Based Abductive Explanations for Random Forests
Authors: Audemard Gilles , Lagniez Jean-Marie , Marquis Pierre , Szczepanski Nicolas
Published: July 2024

We show how to define and compute example-based abductive explanations. Such explanations are guaranteed to be 100% correct, fairly general, and persuasive enough since they cover sufficiently many reference instances furnished by the explainee. We prove that the latter coverage condition yields a complexity shift to the second level of the polynomial hierarchy. We present a CEGAR-based algorithm to derive such explanations, and show how to modify it to derive most anchored example-based abductive explanations, i.e., example-based abductive explanations that cover as many reference instances as possible. We also explain how to reduce example-based abductive explanations to get subset-minimal explanations. Experiments in the case of random forest classifiers show that our CEGAR-based algorithm is quite efficient in practice.
Deriving Provably Correct Explanations for Decision Trees: The Impact of Domain Theories
Authors: Audemard Gilles , Lagniez Jean-Marie , Marquis Pierre , Szczepanski Nicolas
Published: July 2024

We are interested in identifying the complexity of computing local explanations of various types given a decision tree, when the Boolean conditions used in the tree are not independent. This is usually the case when decision trees are learned from instances described using numerical or categorical attributes. In such a case, considering the domain theory indicating how the Boolean conditions occurring in the tree are logically connected is paramount to derive provably correct explanations. However, the nature of the domain theory may have a strong impact on the complexity of generating explanations. In this paper, we identify the complexity of deriving local explanations (abductive or contrastive) given a decision tree in the general case, and under several natural restrictions about the domain theory.
Towards Exact Computation of Inductive Bias
Authors: Boopathy Akhilan , Yue William , Hwang Jaedong , Iyer Abhiram , Fiete Ila
Published: July 2024

Much research in machine learning involves finding appropriate inductive biases (e.g. convolutional neural networks, momentum-based optimizers, transformers) to promote generalization on tasks. However, quantification of the amount of inductive bias associated with these architectures and hyperparameters has been limited. We propose a novel method for efficiently computing the inductive bias required for generalization on a task with a fixed training data budget; formally, this corresponds to the amount of information required to specify well-generalizing models within a specific hypothesis space of models. Our approach involves modeling the loss distribution of random hypotheses drawn from a hypothesis space to estimate the required inductive bias for a task relative to these hypotheses. Unlike prior work, our method provides a direct estimate of inductive bias without using bounds and is applicable to diverse hypothesis spaces. Moreover, we derive approximation error bounds for our estimation approach in terms of the number of sampled hypotheses. Consistent with prior results, our empirical results demonstrate that higher dimensional tasks require greater inductive bias. We show that relative to other expressive model classes, neural networks as a model class encode large amounts of inductive bias. Furthermore, our measure quantifies the relative difference in inductive bias between different neural network architectures. Our proposed inductive bias metric provides an information-theoretic interpretation of the benefits of specific model architectures for certain tasks and provides a quantitative guide to developing tasks requiring greater inductive bias, thereby encouraging the development of more powerful inductive biases.
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
Authors: Duan Wei , Lu Jie , Xuan Junyu
Published: July 2024

Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Existing methods for learning this graph primarily focus on agent-pair relations, neglecting higher-order relationships. While several approaches attempt to extend cooperation modelling to encompass behaviour similarities within groups, they commonly fall short in concurrently learning the latent graph, thereby constraining the information exchange among partially observed agents. To overcome these limitations, we present a novel approach to infer the Group-Aware Coordination Graph (GACG), which is designed to capture both the cooperation between agent pairs based on current observations and group-level dependencies from behaviour patterns observed across trajectories.

This graph is further used in graph convolution for information exchange between agents during decision-making. To further ensure behavioural consistency among agents within the same group, we introduce a group distance loss, which promotes group cohesion and encourages specialization between groups. Our evaluations, conducted on StarCraft II micromanagement tasks, demonstrate GACG's superior performance. An ablation study further provides experimental evidence of the effectiveness of each component of our method.
Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints
Authors: Gao Shiqing , Ding Jiaxin , Fu Luoyi , Wang Xinbing , Zhou Chenghu
Published: July 2024

In Constrained Reinforcement Learning (CRL), agents explore the environment to learn the optimal policy while satisfying constraints. The penalty function method has recently been studied as an effective approach for handling constraints, which imposes constraints penalties on the objective to transform the constrained problem into an unconstrained one. However, it is challenging to choose appropriate penalties that balance policy performance and constraint satisfaction efficiently. In this paper, we propose a theoretically guaranteed penalty function method, Exterior Penalty Policy Optimization (EPO), with adaptive penalties generated by a Penalty Metric Network (PMN). PMN responds appropriately to varying degrees of constraint violations, enabling efficient constraint satisfaction and safe exploration. We theoretically prove that EPO consistently improves constraint satisfaction with a convergence guarantee. We propose a new surrogate function and provide worst-case constraint violation and approximation error. In practice, we propose an effective smooth penalty function, which can be easily implemented with a first-order optimizer. Extensive experiments are conducted, showing that EPO outperforms the baselines in terms of policy performance and constraint satisfaction with a stable training process, particularly on complex tasks.
History Repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting
Authors: Gastinger Julia , Meilicke Christian , Errica Federico , Sztyler Timo , Schülke Anett , Stuckenschmidt Heiner
Published: July 2024

Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and rigorous comparison across TKG models are available, but the importance of simple baselines is often neglected in the evaluation, which prevents researchers from discerning actual and fictitious progress. We propose to close this gap by designing an intuitive baseline for TKG Forecasting based on predicting recurring facts. Compared to most TKG models, it requires little hyperparameter tuning and no iterative training. Further, it can help to identify failure modes in existing approaches. The empirical findings are quite unexpected: compared to 11 methods on five datasets, our baseline ranks first or third in three of them, painting a radically different picture of the predictive quality of the state of the art.
Temporal Graph ODEs for Irregularly-Sampled Time Series
Authors: Gravina Alessio , Zambon Daniele , Bacciu Davide , Alippi Cesare
Published: July 2024

Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social networks and physical systems are characterized by continuous dynamics and sporadic observations. To address this limitation, we introduce the Temporal Graph Ordinary Differential Equation (TG-ODE) framework, which learns both the temporal and spatial dynamics from graph streams where the intervals between observations are not regularly spaced. We empirically validate the proposed approach on several graph benchmarks, showing that TG-ODE can achieve state-of-the-art performance in irregular graph stream tasks.
Unlearning during Learning: An Efficient Federated Machine Unlearning Method
Authors: Gu Hanlin , Zhu Gongxi , Zhang Jie , Zhao Xinyuan , Han Yuxing , Fan Lixin , Yang Qiang
Published: July 2024

In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the "right to be forgotten," the concept of federated machine unlearning (FMU) has also emerged. However, current FMU approaches often involve additional time-consuming steps and may not offer comprehensive unlearning capabilities, which renders them less practical in real FL scenarios. In this paper, we introduce FedAU, an innovative and efficient FMU framework aimed at overcoming these limitations. Specifically, FedAU incorporates a lightweight auxiliary unlearning module into the learning process and employs a straightforward linear operation to facilitate unlearning. This approach eliminates the requirement for extra time-consuming steps, rendering it well-suited for FL.

Furthermore, FedAU exhibits remarkable versatility. It not only enables multiple clients to carry out unlearning tasks concurrently but also supports unlearning at various levels of granularity, including individual data samples, specific classes, and even at the client level. We conducted extensive experiments on MNIST, CIFAR10, and CIFAR100 datasets to evaluate the performance of FedAU. The results demonstrate that FedAU effectively achieves the desired unlearning effect while maintaining model accuracy.
P2P: Transforming from Point Supervision to Explicit Visual Prompt for Object Detection and Segmentation
Authors: Guo Guangqian , Shao Dian , Zhu Chenguang , Meng Sha , Wang Xuan , Gao Shan
Published: July 2024

Point-supervised vision tasks, including detection and segmentation, aiming to learn a network that transforms from points to pseudo labels, have attracted much attention in recent years. However, the lack of precise object size and boundary annotations in the point-supervised condition results in a large performance gap between point- and fully-supervised methods. In this paper, we propose a novel iterative learning framework, Point to Prompt (P2P), for point-supervised object detection and segmentation, with the key insight of transforming from point supervision to explicit visual prompt of the foundation model. The P2P is formulated as an iterative refinement process of two stages: Semantic Explicit Prompt Generation (SEPG) and Prompt Guided Spatial Refinement (PGSR). Specifically, SEPG serves as a prompt generator for generating semantic-explicit prompts from point input via a group-based learning strategy. In the PGSR stage, prompts guide the visual foundation model to further refine the object regions, by leveraging the outstanding generalization ability of the foundation model. The two stages are iterated multiple times to improve the quality of predictions progressively. Experimental results on multiple datasets demonstrate that P2P achieves SOTA performance in both detection and segmentation tasks, further narrowing the performance gap with fully-supervised methods. The source code and supplementary material can be found at https://github.com/guangqian-guo/P2P.
Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
Authors: Hong Chenxing , Jin Yan , Kang Zhiqi , Chen Yizhou , Li Mengke , Lu Yang , Wang Hanzi
Published: July 2024

Existing continual learning literature relies heavily on a strong assumption that tasks arrive with a balanced data stream, which is often unrealistic in real-world applications. In this work, we explore task-imbalanced continual learning (TICL) scenarios where the distribution of task data is non-uniform across the whole learning process. We find that imbalanced tasks significantly challenge the capability of models to control the trade-off between stability and plasticity from the perspective of recent prompt-based continual learning methods. On top of the above finding, we propose Dynamically Anchored Prompting (DAP), a prompt-based method that only maintains a single general prompt to adapt to the shifts within a task stream dynamically. This general prompt is regularized in the prompt space with two specifically designed prompt anchors, called boosting anchor and stabilizing anchor, to balance stability and plasticity in TICL. Remarkably, DAP achieves this balance by only storing a prompt across the data stream, therefore offering a substantial advantage in rehearsal-free CL. Extensive experiments demonstrate that the proposed DAP results in 4.5% to 15% absolute improvements over state-of-the-art methods on benchmarks under task-imbalanced settings. Our code is available at https://github.com/chenxing6666/DAP.
Practical Hybrid Gradient Compression for Federated Learning Systems
Authors: Hu Sixu , Jiang Linshan , He Bingsheng
Published: July 2024

The high communication cost is a major challenge in the federated learning (FL) training process. Several methods have been proposed to reduce communication costs on the uplink channel, primarily sparsification-based methods, which have overlooked the impact of downlink channels. However, model accuracy and communication cost issues arise when applying them in practical FL applications, especially when the bandwidth is limited both on the uplink and downlink channels. In this paper, we propose a novel secure-FL-compatible hybrid gradient compression framework (HGC) that handles both uplink and downlink communication. Specifically, HGC identifies and exploits three types of redundancies in the FL training process. With proposed optimization methods based on compression ratio correction and dynamic momentum correction, HGC improves the trade-off between communication cost and model performance. The extensive theoretical and empirical analysis demonstrates the effectiveness of our framework in achieving a high compression ratio for both uplink and downlink communications with negligible loss of model accuracy, surpassing the state-of-the-art compression methods.
Symplectic Neural Gaussian Processes for Meta-learning Hamiltonian Dynamics
Authors: Iwata Tomoharu , Tanaka Yusuke
Published: July 2024

We propose a meta-learning method for modeling Hamiltonian dynamics from a limited number of data. Although Hamiltonian neural networks have been successfully used for modeling dynamics that obey the energy conservation law, they require many data to achieve high performance. The proposed method meta-learns our neural network-based model using datasets in various dynamical systems, such that our model can predict vector fields of unseen systems. In our model, a system representation is inferred from given small data using an encoder network. Then, the system-specific vector field is predicted by modeling the Hamiltonian using a Gaussian process (GP) with neural network-based mean and kernel functions that depend on the inferred system representation. This GP-based Hamiltonian allows us to analytically obtain predictions that are adapted to small data while imposing the constraint of the conservation law. The neural networks are shared across systems, which enables us to learn knowledge from multiple systems, and use it for unseen systems. In our experiments, we demonstrate that the proposed method outperforms existing methods for predicting dynamics from a small number of observations in target systems.
QFormer: An Efficient Quaternion Transformer for Image Denoising
Authors: Jiang Bo , Lu Yao , Lu Guangming , Zhang Bob
Published: July 2024

Since Deep Convolutional Neural Networks (DCNNs) and Vision Transformer perform well in learning generalizable image priors from large-scale data, these models have been widely used in image denoising tasks. However, vanilla DCNNs and Transformer suffer from two problems. First, the vanilla DCNNs and Transformer only accumulate the output along the channel axis, ignoring the internal relationship among channels. This results in the severely inadequate color structure representation retrieved from color images. Secondly, the DCNNs or Transformer-based image denoising models usually have a large number of parameters, high computational complexity, and slow inference speed. To resolve these issues, this paper proposes a highly-efficient Quaternion Transformer (QFormer) for image denoising. Specifically, the proposed Quaternion Transformer Block (QTB) simplifies the typical Transformer from a multi-branch structure to an elaborately sequential structure mainly with quaternion transformations, to alternately capture both long-range dependencies and local contextual features with color structure information. Furthermore, the proposed QTB can also avoid considerable element-wise multiplications of computing the self-attention matrices. Thus, our QTB can significantly reduce the computational complexity and its sequential structure can further improve the practical inference speed. Comprehensive experiments demonstrate that the proposed QFormer produces state-of-the-art results in both denoising performance and efficiency. We hope that our work will encourage further research to explore the Quaternion Transformer architecture for image denoising tasks.
Efficient and Stable Offline-to-online Reinforcement Learning via Continual Policy Revitalization
Authors: Kong Rui , Wu Chenyang , Gao Chen-Xiao , Zhang Zongzhang , Li Ming
Published: July 2024

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.
A Density-driven Iterative Prototype Optimization for Transductive Few-shot Learning
Authors: Li Jingcong , Ye Chunjin , Wang Fei , Pan Jiahui
Published: July 2024

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.
Continual Multi-Objective Reinforcement Learning via Reward Model Rehearsal
Authors: Li Lihe , Chen Ruotong , Zhang Ziqian , Wu Zhichao , Li Yi-Chen , Guan Cong , Yu Yang , Yuan Lei
Published: July 2024

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.
Prompt Learning with Extended Kalman Filter for Pre-trained Language Models
Authors: Li Quan , Xie Xike , Wang Chao , Zhou S. Kevin
Published: July 2024

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.
Efficiency Calibration of Implicit Regularization in Deep Networks via Self-paced Curriculum-Driven Singular Value Selection
Authors: Li Zhe , Chen Shuo , Yang Jian , Luo Lei
Published: July 2024

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.
BATON: Aligning Text-to-Audio Model Using Human Preference Feedback
Authors: Liao Huan , Han Haonan , Yang Kai , Du Tianjiao , Yang Rui , Xu Qinmei , Xu Zunnan , Liu Jingquan , Lu Jiasheng , Li Xiu
Published: July 2024

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.
Meta-Learning via PAC-Bayesian with Data-Dependent Prior: Generalization Bounds from Local Entropy
Authors: Liu Shiyu , Shi Wei , Xu Zenglin , Lv Shaogao , Zhang Yehong , Wang Hui
Published: July 2024

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.
Relevant Irrelevance: Generating Alterfactual Explanations for Image Classifiers
Authors: Mertes Silvan , Huber Tobias , Karle Christina , Weitz Katharina , Schlagowski Ruben , Conati Cristina , André Elisabeth
Published: July 2024

In this paper, we demonstrate the feasibility of alterfactual explanations for black box image classifiers.

Traditional explanation mechanisms from the field of Counterfactual Thinking are a widely-used paradigm for Explainable Artificial Intelligence (XAI), as they follow a natural way of reasoning that humans are familiar with. However, most common approaches from this field are based on communicating information about features or characteristics that are especially important for an AI's decision.

However, to fully understand a decision, not only knowledge about relevant features is needed, but the awareness of irrelevant information also highly contributes to the creation of a user's mental model of an AI system.

To this end, a novel approach for explaining AI systems called alterfactual explanations was recently proposed on a conceptual level.

It is based on showing an alternative reality where irrelevant features of an AI's input are altered.

By doing so, the user directly sees which input data characteristics can change arbitrarily without influencing the AI's decision.

In this paper, we show for the first time that it is possible to apply this idea to black box models based on neural networks.

To this end, we present a GAN-based approach to generate these alterfactual explanations for binary image classifiers.

Further, we present a user study that gives interesting insights on how alterfactual explanations can complement counterfactual explanations.
Learning Label-Specific Multiple Local Metrics for Multi-Label Classification
Authors: Mao Jun-Xiang , Hang Jun-Yi , Zhang Min-Ling
Published: July 2024

Multi-label metric learning serve as an effective strategy to facilitate multi-label classification, aiming to learn better similarity metrics from multi-label examples. Existing multi-label metric learning approaches learn consistent metrics across all multi-label instances in the label space. However, such consistent metric learning approaches are suboptimal as they neglect the nonlinear distribution characteristics of multi-label instances. In this paper, we present LSMM, a label-specific multi-metric learning framework for multi-label classification, where nonlinear distribution characteristics of multi-label examples are considered by learning label-specific multiple local metrics for different instances on the shoulder of a global one. Specifically, multi-label instances within each label space can be divided into several disjoint partitions through either semantic-based or cluster-based partition strategies, in each of which a local metric is trained to separate the instances locally. Besides, a global metric is introduced to implicitly exploit high-order label correlations across all labels. The combination of the global metric and label-specific local metrics is utilized to measure the semantic similarities between multi-label instances in each label space, under which similar intra-class instances are pushed closer and inter-class instances are pulled apart. Comprehensive experiments on benchmark multi-label data sets validate the superiority of LSMM in learning effective similarity metrics for multi-label classification.
Let’s Start Over: Retraining with Selective Samples for Generalized Category Discovery
Authors: Peng Zhimao , Wang Enguang , Liu Xialei , Cheng Ming-Ming
Published: July 2024

Generalized Category Discovery (GCD) presents a realistic

and challenging problem in open-world learning. Given a par-

tially labeled dataset, GCD aims to categorize unlabeled data

by leveraging visual knowledge from the labeled data, where

the unlabeled data includes both known and unknown classes.

Existing methods based on parametric/non-parametric classi-

fiers attempt to generate pseudo-labels/relationships for the

unlabeled data to enhance representation learning. However,

the lack of ground-truth labels for novel classes often leads

to noisy pseudo-labels/relationships, resulting in suboptimal

representation learning. This paper introduces a novel method

using Nearest Neighbor Distance-aware Label Consistency

sample selection. It creates class-consistent subsets for novel

class sample clusters from the current GCD method, acting

as “pseudo-labeled sets” to mitigate representation bias. We

propose progressive supervised representation learning with

selected samples to optimize the trade-off between quantity

and purity in each subset. Our method is versatile and appli-

cable to various GCD methods, whether parametric or non-

parametric. We conducted extensive experiments on multiple

generic and fine-grained image classification datasets to eval-

uate the effectiveness of our approach. The results demon-

strate the superiority of our method in achieving improved

performance in generalized category discovery tasks.
Robust Losses for Decision-Focused Learning
Authors: Schutte Noah , Postek Krzysztof , Yorke-Smith Neil
Published: July 2024

Optimization models used to make discrete decisions often contain uncertain parameters that are context-dependent and estimated through prediction. To account for the quality of the decision made based on the prediction, decision-focused learning (end-to-end predict-then-optimize) aims at training the predictive model to minimize regret, i.e., the loss incurred by making a suboptimal decision. Despite the challenge of the gradient of this loss w.r.t. the predictive model parameters being zero almost everywhere for optimization problems with a linear objective, effective gradient-based learning approaches have been proposed to minimize the expected loss, using the empirical loss as a surrogate. However, empirical regret can be an ineffective surrogate because empirical optimal decisions can vary substantially from expected optimal decisions. To understand the impact of this deficiency, we evaluate the effect of aleatoric and epistemic uncertainty on the accuracy of empirical regret as a surrogate. Next, we propose three novel loss functions that approximate expected regret more robustly. Experimental results show that training two state-of-the-art decision-focused learning approaches using robust regret losses improves test-sample empirical regret in general while keeping computational time equivalent relative to the number of training epochs.
Learning with Posterior Sampling for Revenue Management under Time-varying Demand
Authors: Shimizu Kazuma , Honda Junya , Ito Shinji , Nakadai Shinji
Published: July 2024

This paper discusses the revenue management (RM) problem to maximize revenue by pricing items or services. One challenge in this problem is that the demand distribution is unknown and varies over time in real applications such as airline and retail industries. In particular, the time-varying demand has not been well studied under scenarios of unknown demand due to the difficulty of jointly managing the remaining inventory and estimating the demand. To tackle this challenge, we first introduce an episodic generalization of the RM problem motivated by typical application scenarios. We then propose a computationally efficient algorithm based on posterior sampling, which effectively optimizes prices by solving linear programming. We derive a Bayesian regret upper bound of this algorithm for general models where demand parameters can be correlated between time periods, while also deriving a regret lower bound for generic algorithms. Our empirical study shows that the proposed algorithm performs better than other benchmark algorithms and comparably to the optimal policy in hindsight. We also propose a heuristic modification of the proposed algorithm, which further efficiently learns the pricing policy in the experiments. An extended version of this paper with appendixes is available at: http://arxiv.org/abs/2405.04910.
Deep Hierarchical Graph Alignment Kernels
Authors: Tang Shuhao , Tian Hao , Cao Xiaofeng , Ye Wei
Published: July 2024

Typical R-convolution graph kernels invoke the kernel functions that decompose graphs into non-isomorphic substructures and compare them. However, overlooking implicit similarities and topological position information between those substructures limits their performances. In this paper, we introduce Deep Hierarchical Graph Alignment Kernels (DHGAK) to resolve this problem. Specifically, the relational substructures are hierarchically aligned to cluster distributions in their deep embedding space. The substructures belonging to the same cluster are assigned the same feature map in the Reproducing Kernel Hilbert Space (RKHS), where graph feature maps are derived by kernel mean embedding. Theoretical analysis guarantees that DHGAK is positive semi-definite and has linear separability in the RKHS. Comparison with state-of-the-art graph kernels on various benchmark datasets demonstrates the effectiveness and efficiency of DHGAK. The code is available at Github (https://github.com/EWesternRa/DHGAK).
Dual Calibration-based Personalised Federated Learning
Authors: Tang Xiaoli , Yu Han , Tang Run , Ren Chao , Li Anran , Li Xiaoxiao
Published: July 2024

Personalized federated learning (PFL) is designed for scenarios with non-independent and identically distributed (non-IID) client data. Existing model mixup-based methods, one of the main approaches of PFL, can only extract either global or personalized features during training, thereby limiting effective knowledge sharing among clients. To address this limitation, we propose the Dual Calibration-based PFL (DC-PFL). It divides local models into a heterogeneous feature extractor and a homogeneous classifier. The FL server utilizes mean and covariance representations from clients' feature extractors to train a global generalized classifier, facilitating information exchange while preserving privacy. To enhance personalization and convergence, we design a feature extractor-level calibration method with an auxiliary loss for local models to refine feature extractors using global knowledge. Furthermore, DC-PFL refines the global classifier through the global classifier-level calibration, utilizing sample representations derived from an approximate Gaussian distribution model specific to each class. This method precludes the need to transmit original data representations, further enhancing privacy preservation. Extensive experiments on widely used benchmark datasets demonstrate that DC-PFL outperforms eight state-of-the-art methods, surpassing the best-performing baseline by 1.22% and 9.22% in terms of accuracy on datasets CIFAR-10 and CIFAR-100, respectively.
Interpretable Tensor Fusion
Authors: Varshneya Saurabh , Ledent Antoine , Liznerski Philipp , Balinskyy Andriy , Mehta Purvanshi , Mustafa Waleed , Kloft Marius
Published: July 2024

Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type. However, practical applications may encompass data of diverse types, such as text, images, and audio. We introduce interpretable tensor fusion (InTense), a multimodal learning method training a neural network to simultaneously learn multiple data representations and their interpretable fusion. InTense can separately capture both linear combinations and multiplicative interactions of the data types, thereby disentangling higher-order interactions from the individual effects of each modality. InTense provides interpretability out of the box by assigning relevance scores to modalities and their associations, respectively. The approach is theoretically grounded and yields meaningful relevance scores on multiple synthetic and real-world datasets. Experiments on four real-world datasets show that InTense outperforms existing state-of-the-art multimodal interpretable approaches in terms of accuracy and interpretability.
A Grassmannian Manifold Self-Attention Network for Signal Classification
Authors: Wang Rui , Hu Chen , Chen Ziheng , Wu Xiao-Jun , Song Xiaoning
Published: July 2024

In the community of artificial intelligence, significant progress has been made in encoding sequential data using deep learning techniques. Nevertheless, how to effectively mine useful information from channel dimensions remains a major challenge, as these features have a submanifold structure. Linear subspace, the basic element of the Grassmannian manifold, has proven to be an effective manifold-valued feature descriptor in statistical representation. Besides, the Euclidean self-attention mechanism has shown great success in capturing long-range relationships of data. Inspired by these facts, we extend the self-attention mechanism to the Grassmannian manifold. Our framework can effectively characterize the spatiotemporal fluctuations of sequential data encoded in the Grassmannian manifold. Extensive experimental results on three benchmarking datasets (a drone recognition dataset and two EEG signal classification datasets) demonstrate the superiority of our method over the state-of-the-art. The code and supplementary material for this work can be found at https://github.com/ChenHu-ML/GDLNet.
Scale and Direction Guided GAN for Inertial Sensor Signal Enhancement
Authors: Wang Yifeng , Zhao Yi
Published: July 2024

Inertial sensors, serving as attitude and motion sensing components, are extensively used in various portable devices spanning consumer electronics, sports health, aerospace, etc. However, the severe intrinsic errors of inertial sensors greatly restrict their capability to implement advanced functions, such as motion tracking and semantic recognition. Although generative models hold significant potential for signal enhancement, unsupervised or weakly-supervised generative methods may not achieve ideal generation results due to the absence of guidance from paired data. To address this, we propose a scale and direction-guided generative adversarial network (SDG-GAN), which provides dual guidance mechanisms for GAN with unpaired data across two practical application scenarios. In the unsupervised scenario where only unpaired signals of varying quality are available, our scale-guided GAN (SG-GAN) forces the generator to learn high-quality signal characteristics at different scales simultaneously via the proposed self-supervised zoom constraint, thereby facilitating multi-scale interactive learning. In the weakly-supervised scenario, where additional experimental equipment can provide some motion information, our direction-guided GAN (DG-GAN) introduces auxiliary tasks to supervise signal generation while avoiding interference from auxiliary tasks on the main generation task. Extensive experiments demonstrate that both the unsupervised SG-GAN and the weakly-supervised DG-GAN significantly outperform all comparison methods, including fully-supervised approaches. The combined SDG-GAN achieves remarkable results, enabling unimaginable tasks based on the original inertial signal, such as 3D motion tracking.