pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction
Authors: Mingchen Li, Di Zhuang, Keyu Chen, Dumindu Samaraweera, Morris Chang
Date: 2025-04-08 00:00:00
Tasks: Link Prediction, Meta-Learning, Prediction, Recommendation Systems

Link prediction in graph data utilizes various algorithms and machine learning/deep learning models to predict potential relationships between graph nodes. This technique has found widespread use in numerous real-world applications, including recommendation systems, community networks, and biological structures. However, recent research has highlighted the vulnerability of link prediction models to adversarial attacks, such as poisoning and evasion attacks. Addressing the vulnerability of these models is crucial to ensure stable and robust performance in link prediction applications. While many works have focused on enhancing the robustness of the Graph Convolution Network (GCN) model, the Variational Graph Auto-Encoder (VGAE), a sophisticated model for link prediction, has not been thoroughly investigated in the context of graph adversarial attacks. To bridge this gap, this article proposes an unweighted graph poisoning attack approach using meta-learning techniques to undermine VGAE's link prediction performance. We conducted comprehensive experiments on diverse datasets to evaluate the proposed method and its parameters, comparing it with existing approaches in similar settings. Our results demonstrate that our approach significantly diminishes link prediction performance and outperforms other state-of-the-art methods.
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization
Authors: Moncef Garouani
Date: 2025-04-08 00:00:00
Tasks: AutoML, Meta-Learning

Considerable progress has been made in the recent literature studies to tackle the Algorithms Selection and Parametrization (ASP) problem, which is diversified in multiple meta-learning setups. Yet there is a lack of surveys and comparative evaluations that critically analyze, summarize and assess the performance of existing methods. In this paper, we provide an overview of the state of the art in this continuously evolving field. The survey sheds light on the motivational reasons for pursuing classifiers selection through meta-learning. In this regard, Automated Machine Learning (AutoML) is usually treated as an ASP problem under the umbrella of the democratization of machine learning. Accordingly, AutoML makes machine learning techniques accessible to domain scientists who are interested in applying advanced analytics but lack the required expertise. It can ease the task of manually selecting ML algorithms and tuning related hyperparameters. We comprehensively discuss the different phases of classifiers selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we propose a benchmark knowledge base of 4 millions previously learned models and present extensive comparative evaluations of the prominent methods for classifiers selection based on 08 classification algorithms and 400 benchmark datasets. The comparative study quantitatively assesses the performance of algorithms selection methods along while emphasizing the strengths and limitations of existing studies.
Assessing how hyperparameters impact Large Language Models' sarcasm detection performance
Authors: Montgomery Gole, Andriy Miranskyy
Date: 2025-04-08 00:00:00
Tasks: Natural Language Understanding, Sarcasm Detection

Sarcasm detection is challenging for both humans and machines. This work explores how model characteristics impact sarcasm detection in OpenAI's GPT, and Meta's Llama-2 models, given their strong natural language understanding, and popularity. We evaluate fine-tuned and zero-shot models across various sizes, releases, and hyperparameters. Experiments were conducted on the political and balanced (pol-bal) portion of the popular Self-Annotated Reddit Corpus (SARC2.0) sarcasm dataset. Fine-tuned performance improves monotonically with model size within a model family, while hyperparameter tuning also impacts performance. In the fine-tuning scenario, full precision Llama-2-13b achieves state-of-the-art accuracy and $F_1$-score, both measured at 0.83, comparable to average human performance. In the zero-shot setting, one GPT-4 model achieves competitive performance to prior attempts, yielding an accuracy of 0.70 and an $F_1$-score of 0.75. Furthermore, a model's performance may increase or decline with each release, highlighting the need to reassess performance after each release.
Memory-Modular Classification: Learning to Generalize with Memory Replacement
Authors: Dahyun Kang, Ahmet Iscen, Eunchan Jo, Sua Choi, Minsu Cho, Cordelia Schmid
Date: 2025-04-08 00:00:00
Tasks: Classification, image-classification, Image Classification, Memorization, World Knowledge

We propose a novel memory-modular learner for image classification that separates knowledge memorization from reasoning. Our model enables effective generalization to new classes by simply replacing the memory contents, without the need for model retraining. Unlike traditional models that encode both world knowledge and task-specific skills into their weights during training, our model stores knowledge in the external memory of web-crawled image and text data. At inference time, the model dynamically selects relevant content from the memory based on the input image, allowing it to adapt to arbitrary classes by simply replacing the memory contents. The key differentiator that our learner meta-learns to perform classification tasks with noisy web data from unseen classes, resulting in robust performance across various classification scenarios. Experimental results demonstrate the promising performance and versatility of our approach in handling diverse classification tasks, including zero-shot/few-shot classification of unseen classes, fine-grained classification, and class-incremental classification.
Meta-Continual Learning of Neural Fields
Authors: Seungyoon Woo, Junhyeog Yun, Gunhee Kim
Date: 2025-04-08 00:00:00
Tasks: Continual Learning, Meta-Learning, NeRF, Video Reconstruction

Neural Fields (NF) have gained prominence as a versatile framework for complex data representation. This work unveils a new problem setting termed \emph{Meta-Continual Learning of Neural Fields} (MCL-NF) and introduces a novel strategy that employs a modular architecture combined with optimization-based meta-learning. Focused on overcoming the limitations of existing methods for continual learning of neural fields, such as catastrophic forgetting and slow convergence, our strategy achieves high-quality reconstruction with significantly improved learning speed. We further introduce Fisher Information Maximization loss for neural radiance fields (FIM-NeRF), which maximizes information gains at the sample level to enhance learning generalization, with proved convergence guarantee and generalization bound. We perform extensive evaluations across image, audio, video reconstruction, and view synthesis tasks on six diverse datasets, demonstrating our method's superiority in reconstruction quality and speed over existing MCL and CL-NF approaches. Notably, our approach attains rapid adaptation of neural fields for city-scale NeRF rendering with reduced parameter requirement.
The challenge of uncertainty quantification of large language models in medicine
Authors: Zahra Atf, Seyed Amir Ahmad Safavi-Naini, Peter R. Lewis, Aref Mahjoubfar, Nariman Naderi, Thomas R. Savage, Ali Soroush
Date: 2025-04-07 00:00:00
Tasks: Bayesian Inference, Data Integration, Decision Making, Meta-Learning, Uncertainty Quantification

This study investigates uncertainty quantification in large language models (LLMs) for medical applications, emphasizing both technical innovations and philosophical implications. As LLMs become integral to clinical decision-making, accurately communicating uncertainty is crucial for ensuring reliable, safe, and ethical AI-assisted healthcare. Our research frames uncertainty not as a barrier but as an essential part of knowledge that invites a dynamic and reflective approach to AI design. By integrating advanced probabilistic methods such as Bayesian inference, deep ensembles, and Monte Carlo dropout with linguistic analysis that computes predictive and semantic entropy, we propose a comprehensive framework that manages both epistemic and aleatoric uncertainties. The framework incorporates surrogate modeling to address limitations of proprietary APIs, multi-source data integration for better context, and dynamic calibration via continual and meta-learning. Explainability is embedded through uncertainty maps and confidence metrics to support user trust and clinical interpretability. Our approach supports transparent and ethical decision-making aligned with Responsible and Reflective AI principles. Philosophically, we advocate accepting controlled ambiguity instead of striving for absolute predictability, recognizing the inherent provisionality of medical knowledge.
GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision
Authors: Aditya Hemant Shahane, Prathosh A. P, Sandeep Kumar
Date: 2025-04-07 00:00:00
Tasks: Attribute, class-incremental learning, Class Incremental Learning, Incremental Learning, Knowledge Distillation, Node Classification, Recommendation Systems

Graphs are growing rapidly, along with the number of distinct label categories associated with them. Applications like e-commerce, healthcare, recommendation systems, and various social media platforms are rapidly moving towards graph representation of data due to their ability to capture both structural and attribute information. One crucial task in graph analysis is node classification, where unlabeled nodes are categorized into predefined classes. In practice, novel classes appear incrementally sometimes with just a few labels (seen classes) or even without any labels (unseen classes), either because they are new or haven't been explored much. Traditional methods assume abundant labeled data for training, which isn't always feasible. We investigate a broader objective: \emph{Graph Class Incremental Learning under Weak Supervision (GCL)}, addressing this challenge by meta-training on base classes with limited labeled instances. During the incremental streams, novel classes can have few-shot or zero-shot representation. Our proposed framework GOTHAM efficiently accommodates these unlabeled nodes by finding the closest prototype representation, serving as class representatives in the attribute space. For Text-Attributed Graphs (TAGs), our framework additionally incorporates semantic information to enhance the representation. By employing teacher-student knowledge distillation to mitigate forgetting, GOTHAM achieves promising results across various tasks. Experiments on datasets such as Cora-ML, Amazon, and OBGN-Arxiv showcase the effectiveness of our approach in handling evolving graph data under limited supervision. The repository is available here: \href{https://github.com/adityashahane10/GOTHAM--Graph-based-Class-Incremental-Learning-Framework-under-Weak-Supervision}{\small \textcolor{blue}{Code}}
An Efficient Approach for Cooperative Multi-Agent Learning Problems
Authors: Ángel Aso-Mollar, Eva Onaindia
Date: 2025-04-07 00:00:00

In this article, we propose a centralized Multi-Agent Learning framework for learning a policy that models the simultaneous behavior of multiple agents that need to coordinate to solve a certain task. Centralized approaches often suffer from the explosion of an action space that is defined by all possible combinations of individual actions, known as joint actions. Our approach addresses the coordination problem via a sequential abstraction, which overcomes the scalability problems typical to centralized methods. It introduces a meta-agent, called \textit{supervisor}, which abstracts joint actions as sequential assignments of actions to each agent. This sequential abstraction not only simplifies the centralized joint action space but also enhances the framework's scalability and efficiency. Our experimental results demonstrate that the proposed approach successfully coordinates agents across a variety of Multi-Agent Learning environments of diverse sizes.
Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors
Authors: Fan Nie, Lan Feng, Haotian Ye, Weixin Liang, Pan Lu, Huaxiu Yao, Alexandre Alahi, James Zou
Date: 2025-04-07 00:00:00
Tasks: GPU

Efficiently leveraging of the capabilities of contemporary large language models (LLMs) is increasingly challenging, particularly when direct fine-tuning is expensive and often impractical. Existing training-free methods, including manually or automated designed workflows, typically demand substantial human effort or yield suboptimal results. This paper proposes Weak-for-Strong Harnessing (W4S), a novel framework that customizes smaller, cost-efficient language models to design and optimize workflows for harnessing stronger models. W4S formulates workflow design as a multi-turn markov decision process and introduces reinforcement learning for agentic workflow optimization (RLAO) to train a weak meta-agent. Through iterative interaction with the environment, the meta-agent learns to design increasingly effective workflows without manual intervention. Empirical results demonstrate the superiority of W4S that our 7B meta-agent, trained with just one GPU hour, outperforms the strongest baseline by 2.9% ~ 24.6% across eleven benchmarks, successfully elevating the performance of state-of-the-art models such as GPT-3.5-Turbo and GPT-4o. Notably, W4S exhibits strong generalization capabilities across both seen and unseen tasks, offering an efficient, high-performing alternative to directly fine-tuning strong models.
A Classification View on Meta Learning Bandits
Authors: Mirco Mutti, Jeongyeol Kwon, Shie Mannor, Aviv Tamar
Date: 2025-04-06 00:00:00
Tasks: Classification, Meta-Learning, Multi-Armed Bandits, Sequential Decision Making

Contextual multi-armed bandits are a popular choice to model sequential decision-making. E.g., in a healthcare application we may perform various tests to asses a patient condition (exploration) and then decide on the best treatment to give (exploitation). When humans design strategies, they aim for the exploration to be fast, since the patient's health is at stake, and easy to interpret for a physician overseeing the process. However, common bandit algorithms are nothing like that: The regret caused by exploration scales with $\sqrt{H}$ over $H$ rounds and decision strategies are based on opaque statistical considerations. In this paper, we use an original classification view to meta learn interpretable and fast exploration plans for a fixed collection of bandits $\mathbb{M}$. The plan is prescribed by an interpretable decision tree probing decisions' payoff to classify the test bandit. The test regret of the plan in the stochastic and contextual setting scales with $O (\lambda^{-2} C_{\lambda} (\mathbb{M}) \log^2 (MH))$, being $M$ the size of $\mathbb{M}$, $\lambda$ a separation parameter over the bandits, and $C_\lambda (\mathbb{M})$ a novel classification-coefficient that fundamentally links meta learning bandits with classification. Through a nearly matching lower bound, we show that $C_\lambda (\mathbb{M})$ inherently captures the complexity of the setting.
Adaptive Elicitation of Latent Information Using Natural Language
Authors: Jimmy Wang, Thomas Zollo, Richard Zemel, Hongseok Namkoong
Date: 2025-04-05 00:00:00
Tasks: Uncertainty Quantification, World Knowledge

Eliciting information to reduce uncertainty about a latent entity is a critical task in many application domains, e.g., assessing individual student learning outcomes, diagnosing underlying diseases, or learning user preferences. Though natural language is a powerful medium for this purpose, large language models (LLMs) and existing fine-tuning algorithms lack mechanisms for strategically gathering information to refine their own understanding of the latent entity. To harness the generalization power and world knowledge of LLMs in developing effective information-gathering strategies, we propose an adaptive elicitation framework that actively reduces uncertainty on the latent entity. Since probabilistic modeling of an abstract latent entity is difficult, our framework adopts a predictive view of uncertainty, using a meta-learned language model to simulate future observations and enable scalable uncertainty quantification over complex natural language. Through autoregressive forward simulation, our model quantifies how new questions reduce epistemic uncertainty, enabling the development of sophisticated information-gathering strategies to choose the most informative next queries. In experiments on the 20 questions game, dynamic opinion polling, and adaptive student assessment, our method consistently outperforms baselines in identifying critical unknowns and improving downstream predictions, illustrating the promise of strategic information gathering in natural language settings.
EMF: Event Meta Formers for Event-based Real-time Traffic Object Detection
Authors: Muhammad Ahmed Ullah Khan, Abdul Hannan Khan, Andreas Dengel
Date: 2025-04-05 00:00:00
Tasks: Autonomous Driving, Object, object-detection, Object Detection, Specificity, Traffic Object Detection

Event cameras have higher temporal resolution, and require less storage and bandwidth compared to traditional RGB cameras. However, due to relatively lagging performance of event-based approaches, event cameras have not yet replace traditional cameras in performance-critical applications like autonomous driving. Recent approaches in event-based object detection try to bridge this gap by employing computationally expensive transformer-based solutions. However, due to their resource-intensive components, these solutions fail to exploit the sparsity and higher temporal resolution of event cameras efficiently. Moreover, these solutions are adopted from the vision domain, lacking specificity to the event cameras. In this work, we explore efficient and performant alternatives to recurrent vision transformer models and propose a novel event-based object detection backbone. The proposed backbone employs a novel Event Progression Extractor module, tailored specifically for event data, and uses Metaformer concept with convolution-based efficient components. We evaluate the resultant model on well-established traffic object detection benchmarks and conduct cross-dataset evaluation to test its ability to generalize. The proposed model outperforms the state-of-the-art on Prophesee Gen1 dataset by 1.6 mAP while reducing inference time by 14%. Our proposed EMF becomes the fastest DNN-based architecture in the domain by outperforming most efficient event-based object detectors. Moreover, the proposed model shows better ability to generalize to unseen data and scales better with the abundance of data.
Towards An Efficient and Effective En Route Travel Time Estimation Framework
Authors: Zekai Shen, Haitao Yuan, Xiaowei Mao, Congkang Lv, Shengnan Guo, Youfang Lin, Huaiyu Wan
Date: 2025-04-05 00:00:00
Tasks: Meta-Learning, Travel Time Estimation

En route travel time estimation (ER-TTE) focuses on predicting the travel time of the remaining route. Existing ER-TTE methods always make re-estimation which significantly hinders real-time performance, especially when faced with the computational demands of simultaneous user requests. This results in delays and reduced responsiveness in ER-TTE services. We propose a general efficient framework U-ERTTE combining an Uncertainty-Guided Decision mechanism (UGD) and Fine-Tuning with Meta-Learning (FTML) to address these challenges. UGD quantifies the uncertainty and provides confidence intervals for the entire route. It selectively re-estimates only when the actual travel time deviates from the predicted confidence intervals, thereby optimizing the efficiency of ER-TTE. To ensure the accuracy of confidence intervals and accurate predictions that need to re-estimate, FTML is employed to train the model, enabling it to learn general driving patterns and specific features to adapt to specific tasks. Extensive experiments on two large-scale real datasets demonstrate that the U-ERTTE framework significantly enhances inference speed and throughput while maintaining high effectiveness. Our code is available at https://github.com/shenzekai/U-ERTTE
OmniCaptioner: One Captioner to Rule Them All
Authors: Yiting Lu, Jiakang Yuan, Zhen Li, Shitian Zhao, Qi Qin, Xinyue Li, Le Zhuo, Licheng Wen, Dongyang Liu, Yuewen Cao, Xiangchao Yan, Xin Li, Botian Shi, Tao Chen, Zhibo Chen, Lei Bai, Bo Zhang, Peng Gao
Date: 2025-04-09 00:00:00
Tasks: All, Image Captioning, Image Generation, Text to Image Generation, Text-to-Image Generation, Visual Reasoning

We propose OmniCaptioner, a versatile visual captioning framework for generating fine-grained textual descriptions across a wide variety of visual domains. Unlike prior methods limited to specific image types (e.g., natural images or geometric visuals), our framework provides a unified solution for captioning natural images, visual text (e.g., posters, UIs, textbooks), and structured visuals (e.g., documents, tables, charts). By converting low-level pixel information into semantically rich textual representations, our framework bridges the gap between visual and textual modalities. Our results highlight three key advantages: (i) Enhanced Visual Reasoning with LLMs, where long-context captions of visual modalities empower LLMs, particularly the DeepSeek-R1 series, to reason effectively in multimodal scenarios; (ii) Improved Image Generation, where detailed captions improve tasks like text-to-image generation and image transformation; and (iii) Efficient Supervised Fine-Tuning (SFT), which enables faster convergence with less data. We believe the versatility and adaptability of OmniCaptioner can offer a new perspective for bridging the gap between language and visual modalities.
Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer
Authors: Shi Pan, Jianan Chen, Maria Secrier
Date: 2025-04-09 00:00:00
Tasks: Knowledge Distillation, parameter-efficient fine-tuning, Transfer Learning

Gene expression profiling provides critical insights into cellular heterogeneity, biological processes and disease mechanisms. There has been an increasing interest in computational approaches that can predict gene expression directly from digitalized histopathology images. While image foundation models have shown promise in a variety of pathology downstream analysis, their performances on gene-expression prediction are still limited. Explicitly incorporating information from the transcriptomic models can help image models to address domain shift, yet the fine-tuning and alignment of foundation models can be expensive. In the work, we propose Parameter Efficient Knowledge trAnsfer (PEKA), a novel framework that leverages Block-Affine Adaptation and integrates knowledge distillation and structure alignment losses for cross-modal knowledge transfer. We evaluated PEKA for gene expression prediction using multiple spatial transcriptomics datasets (comprising 206,123 image tiles with matched gene expression profiles) that encompassed various types of tissue. PEKA achieved at least 5\% performance improvement over baseline foundation models while also outperforming alternative parameter-efficient fine-tuning strategies. We will release the code, datasets and aligned models after peer-review to facilitate broader adoption and further development for parameter efficient model alignment.