pwc-archive/papers-with-abstracts huggingface dataset:
MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classification
Authors: Jiangping Wen, Jinyu Wen, Meie Fang
Date: 2025-03-11 00:00:00
Tasks: image-classification, Image Classification, Multiple Instance Learning

Bag-based Multiple Instance Learning (MIL) approaches have emerged as the mainstream methodology for Whole Slide Image (WSI) classification. However, most existing methods adopt a segmented training strategy, which first extracts features using a pre-trained feature extractor and then aggregates these features through MIL. This segmented training approach leads to insufficient collaborative optimization between the feature extraction network and the MIL network, preventing end-to-end joint optimization and thereby limiting the overall performance of the model. Additionally, conventional methods typically extract features from all patches of fixed size, ignoring the multi-scale observation characteristics of pathologists. This not only results in significant computational resource waste when tumor regions represent a minimal proportion (as in the Camelyon16 dataset) but may also lead the model to suboptimal solutions. To address these limitations, this paper proposes an end-to-end multi-scale WSI classification framework that integrates multi-scale feature extraction with multiple instance learning. Specifically, our approach includes: (1) a semantic feature filtering module to reduce interference from non-lesion areas; (2) a multi-scale feature extraction module to capture pathological information at different levels; and (3) a multi-scale fusion MIL module for global modeling and feature integration. Through an end-to-end training strategy, we simultaneously optimize both the feature extractor and MIL network, ensuring maximum compatibility between them. Experiments were conducted on three cross-center datasets (DigestPath2019, BCNB, and UBC-OCEAN). Results demonstrate that our proposed method outperforms existing state-of-the-art approaches in terms of both accuracy (ACC) and AUC metrics.
OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting
Authors: Yongsheng Yu, Ziyun Zeng, Haitian Zheng, Jiebo Luo
Date: 2025-03-11 00:00:00
Tasks: Hallucination, Object, Object Hallucination

Diffusion-based generative models have revolutionized object-oriented image editing, yet their deployment in realistic object removal and insertion remains hampered by challenges such as the intricate interplay of physical effects and insufficient paired training data. In this work, we introduce OmniPaint, a unified framework that re-conceptualizes object removal and insertion as interdependent processes rather than isolated tasks. Leveraging a pre-trained diffusion prior along with a progressive training pipeline comprising initial paired sample optimization and subsequent large-scale unpaired refinement via CycleFlow, OmniPaint achieves precise foreground elimination and seamless object insertion while faithfully preserving scene geometry and intrinsic properties. Furthermore, our novel CFD metric offers a robust, reference-free evaluation of context consistency and object hallucination, establishing a new benchmark for high-fidelity image editing. Project page: https://yeates.github.io/OmniPaint-Page/
COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection
Authors: Jinqi Xiao, Shen Sang, Tiancheng Zhi, Jing Liu, Qing Yan, Yuqian Zhang, Linjie Luo, Bo Yuan
Date: 2024-11-26 00:00:00
Proceeding: CVPR 2025 1
Tasks: Quantization

Training large-scale neural networks in vision, and multimodal domains demands substantial memory resources, primarily due to the storage of optimizer states. While LoRA, a popular parameter-efficient method, reduces memory usage, it often suffers from suboptimal performance due to the constraints of low-rank updates. Low-rank gradient projection methods (e.g., GaLore, Flora) reduce optimizer memory by projecting gradients and moment estimates into low-rank spaces via singular value decomposition or random projection. However, they fail to account for inter-projection correlation, causing performance degradation, and their projection strategies often incur high computational costs. In this paper, we present COAP (Correlation-Aware Gradient Projection), a memory-efficient method that minimizes computational overhead while maintaining training performance. Evaluated across various vision, language, and multimodal tasks, COAP outperforms existing methods in both training speed and model performance. For LLaMA-1B, it reduces optimizer memory by 61% with only 2% additional time cost, achieving the same PPL as AdamW. With 8-bit quantization, COAP cuts optimizer memory by 81% and achieves 4x speedup over GaLore for LLaVA-v1.5-7B fine-tuning, while delivering higher accuracy.
RandLoRA: Full-rank parameter-efficient fine-tuning of large models
Authors: Paul Albert, Frederic Z. Zhang, Hemanth Saratchandran, Cristian Rodriguez-Opazo, Anton Van Den Hengel, Ehsan Abbasnejad
Date: 2025-02-03 00:00:00
Tasks: parameter-efficient fine-tuning

Low-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the number of trainable parameters and memory requirements of large transformer networks while maintaining fine-tuning performance. The low-rank nature of the weight update inherently limits the representation power of fine-tuned models, however, thus potentially compromising performance on complex tasks. This raises a critical question: when a performance gap between LoRA and standard fine-tuning is observed, is it due to the reduced number of trainable parameters or the rank deficiency? This paper aims to answer this question by introducing RandLoRA, a parameter-efficient method that performs full-rank updates using a learned linear combinations of low-rank, non-trainable random matrices. Our method limits the number of trainable parameters by restricting optimization to diagonal scaling matrices applied to the fixed random matrices. This allows us to effectively overcome the low-rank limitations while maintaining parameter and memory efficiency during training. Through extensive experimentation across vision, language, and vision-language benchmarks, we systematically evaluate the limitations of LoRA and existing random basis methods. Our findings reveal that full-rank updates are beneficial across vision and language tasks individually, and even more so for vision-language tasks, where RandLoRA significantly reduces -- and sometimes eliminates -- the performance gap between standard fine-tuning and LoRA, demonstrating its efficacy.
Learning Pareto manifolds in high dimensions: How can regularization help?
Authors: Tobias Wegel, Filip Kovačević, Alexandru Ţifrea, Fanny Yang
Date: 2025-03-11 00:00:00
Tasks: Fairness

Simultaneously addressing multiple objectives is becoming increasingly important in modern machine learning. At the same time, data is often high-dimensional and costly to label. For a single objective such as prediction risk, conventional regularization techniques are known to improve generalization when the data exhibits low-dimensional structure like sparsity. However, it is largely unexplored how to leverage this structure in the context of multi-objective learning (MOL) with multiple competing objectives. In this work, we discuss how the application of vanilla regularization approaches can fail, and propose a two-stage MOL framework that can successfully leverage low-dimensional structure. We demonstrate its effectiveness experimentally for multi-distribution learning and fairness-risk trade-offs.
Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms
Authors: Meltem Tatlı, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam, Ali Tajer
Date: 2025-03-11 00:00:00

This paper introduces a general framework for risk-sensitive bandits that integrates the notions of risk-sensitive objectives by adopting a rich class of distortion riskmetrics. The introduced framework subsumes the various existing risk-sensitive models. An important and hitherto unknown observation is that for a wide range of riskmetrics, the optimal bandit policy involves selecting a mixture of arms. This is in sharp contrast to the convention in the multi-arm bandit algorithms that there is generally a solitary arm that maximizes the utility, whether purely reward-centric or risk-sensitive. This creates a major departure from the principles for designing bandit algorithms since there are uncountable mixture possibilities. The contributions of the paper are as follows: (i) it formalizes a general framework for risk-sensitive bandits, (ii) identifies standard risk-sensitive bandit models for which solitary arm selections is not optimal, (iii) and designs regret-efficient algorithms whose sampling strategies can accurately track optimal arm mixtures (when mixture is optimal) or the solitary arms (when solitary is optimal). The algorithms are shown to achieve a regret that scales according to $O((\log T/T )^{\nu})$, where $T$ is the horizon, and $\nu>0$ is a riskmetric-specific constant.
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions
Authors: Omer Noy Klein, Alihan Hüyük, Ron Shamir, Uri Shalit, Mihaela van der Schaar
Date: 2025-03-12 00:00:00
Tasks: Active Learning

Randomized Controlled Trials (RCTs) are the gold standard for evaluating the effect of new medical treatments. Treatments must pass stringent regulatory conditions in order to be approved for widespread use, yet even after the regulatory barriers are crossed, real-world challenges might arise: Who should get the treatment? What is its true clinical utility? Are there discrepancies in the treatment effectiveness across diverse and under-served populations? We introduce two new objectives for future clinical trials that integrate regulatory constraints and treatment policy value for both the entire population and under-served populations, thus answering some of the questions above in advance. Designed to meet these objectives, we formulate Randomize First Augment Next (RFAN), a new framework for designing Phase III clinical trials. Our framework consists of a standard randomized component followed by an adaptive one, jointly meant to efficiently and safely acquire and assign patients into treatment arms during the trial. Then, we propose strategies for implementing RFAN based on causal, deep Bayesian active learning. Finally, we empirically evaluate the performance of our framework using synthetic and real-world semi-synthetic datasets.
Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes
Authors: Keyue Jiang, Bohan Tang, Xiaowen Dong, Laura Toni
Date: 2025-03-11 00:00:00
Tasks: Graph structure learning

Inferring the graph structure from observed data is a key task in graph machine learning to capture the intrinsic relationship between data entities. While significant advancements have been made in learning the structure of homogeneous graphs, many real-world graphs exhibit heterogeneous patterns where nodes and edges have multiple types. This paper fills this gap by introducing the first approach for heterogeneous graph structure learning (HGSL). To this end, we first propose a novel statistical model for the data-generating process (DGP) of heterogeneous graph data, namely hidden Markov networks for heterogeneous graphs (H2MN). Then we formalize HGSL as a maximum a-posterior estimation problem parameterized by such DGP and derive an alternating optimization method to obtain a solution together with a theoretical justification of the optimization conditions. Finally, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate that our proposed method excels in learning structure on heterogeneous graphs in terms of edge type identification and edge weight recovery.
Enhanced Estimation Techniques for Certified Radii in Randomized Smoothing
Authors: Zixuan Liang
Date: 2025-03-11 00:00:00

This paper presents novel methods for estimating certified radii in randomized smoothing, a technique crucial for certifying the robustness of neural networks against adversarial perturbations. Our proposed techniques significantly improve the accuracy of certified test-set accuracy by providing tighter bounds on the certified radii. We introduce advanced algorithms for both discrete and continuous domains, demonstrating their effectiveness on CIFAR-10 and ImageNet datasets. The new methods show considerable improvements over existing approaches, particularly in reducing discrepancies in certified radii estimates. We also explore the impact of various hyperparameters, including sample size, standard deviation, and temperature, on the performance of these methods. Our findings highlight the potential for more efficient certification processes and pave the way for future research on tighter confidence sequences and improved theoretical frameworks. The study concludes with a discussion of potential future directions, including enhanced estimation techniques for discrete domains and further theoretical advancements to bridge the gap between empirical and theoretical performance in randomized smoothing.
Clustering by Nonparametric Smoothing
Authors: David P. Hofmeyr
Date: 2025-03-12 00:00:00
Tasks: Clustering

A novel formulation of the clustering problem is introduced in which the task is expressed as an estimation problem, where the object to be estimated is a function which maps a point to its distribution of cluster membership. Unlike existing approaches which implicitly estimate such a function, like Gaussian Mixture Models (GMMs), the proposed approach bypasses any explicit modelling assumptions and exploits the flexible estimation potential of nonparametric smoothing. An intuitive approach for selecting the tuning parameters governing estimation is provided, which allows the proposed method to automatically determine both an appropriate level of flexibility and also the number of clusters to extract from a given data set. Experiments on a large collection of publicly available data sets are used to document the strong performance of the proposed approach, in comparison with relevant benchmarks from the literature. R code to implement the proposed approach is available from https://github.com/DavidHofmeyr/ CNS
A Novel Approach for Intrinsic Dimension Estimation
Authors: Kadir Özçoban, Murat Manguoğlu, Emrullah Fatih Yetkin
Date: 2025-03-12 00:00:00
Tasks: Dimensionality Reduction

The real-life data have a complex and non-linear structure due to their nature. These non-linearities and the large number of features can usually cause problems such as the empty-space phenomenon and the well-known curse of dimensionality. Finding the nearly optimal representation of the dataset in a lower-dimensional space (i.e. dimensionality reduction) offers an applicable mechanism for improving the success of machine learning tasks. However, estimating the required data dimension for the nearly optimal representation (intrinsic dimension) can be very costly, particularly if one deals with big data. We propose a highly efficient and robust intrinsic dimension estimation approach that only relies on matrix-vector products for dimensionality reduction methods. An experimental study is also conducted to compare the performance of proposed method with state of the art approaches.
Fig Tree-Wasp Symbiotic Coevolutionary Optimization Algorithm
Authors: Anand J Kulkarni, Isha Purnapatre, Apoorva S Shastri
Date: 2025-03-12 00:00:00
Tasks: Metaheuristic Optimization

The nature inspired algorithms are becoming popular due to their simplicity and wider applicability. In the recent past several such algorithms have been developed. They are mainly bio-inspired, swarm based, physics based and socio-inspired; however, the domain based on symbiotic relation between creatures is still to be explored. A novel metaheuristic optimization algorithm referred to as Fig Tree-Wasp Symbiotic Coevolutionary (FWSC) algorithm is proposed. It models the symbiotic coevolutionary relationship between fig trees and wasps. More specifically, the mating of wasps, pollinating the figs, searching for new trees for pollination and wind effect drifting of wasps are modeled in the algorithm. These phenomena help in balancing the two important aspects of exploring the search space efficiently as well as exploit the promising regions. The algorithm is successfully tested on a variety of test problems. The results are compared with existing methods and algorithms. The Wilcoxon Signed Rank Test and Friedman Test are applied for the statistical validation of the algorithm performance. The algorithm is also further applied to solve the real-world engineering problems. The performance of the FWSC underscored that the algorithm can be applied to wider variety of real-world problems.
Large Language Model as Meta-Surrogate for Data-Driven Many-Task Optimization: A Proof-of-Principle Study
Authors: Xian-Rong Zhang, Yue-Jiao Gong, Jun Zhang
Date: 2025-03-11 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Prediction, Transfer Learning

In many-task optimization scenarios, surrogate models are valuable for mitigating the computational burden of repeated fitness evaluations across tasks. This study proposes a novel meta-surrogate framework to assist many-task optimization, by leveraging the knowledge transfer strengths and emergent capabilities of large language models (LLMs). We formulate a unified framework for many-task fitness prediction, by defining a universal model with metadata to fit a group of problems. Fitness prediction is performed on metadata and decision variables, enabling efficient knowledge sharing across tasks and adaptability to new tasks. The LLM-based meta-surrogate treats fitness prediction as conditional probability estimation, employing a unified token sequence representation for task metadata, inputs, and outputs. This approach facilitates efficient inter-task knowledge sharing through shared token embeddings and captures complex task dependencies via multi-task model training. Experimental results demonstrate the model's emergent generalization ability, including zero-shot performance on problems with unseen dimensions. When integrated into evolutionary transfer optimization (ETO), our framework supports dual-level knowledge transfer -- at both the surrogate and individual levels -- enhancing optimization efficiency and robustness. This work establishes a novel foundation for applying LLMs in surrogate modeling, offering a versatile solution for many-task optimization.
Combining Local Symmetry Exploitation and Reinforcement Learning for Optimised Probabilistic Inference -- A Work In Progress
Authors: Sagad Hamid, Tanya Braun
Date: 2025-03-11 00:00:00
Tasks: Tensor Networks

Efficient probabilistic inference by variable elimination in graphical models requires an optimal elimination order. However, finding an optimal order is a challenging combinatorial optimisation problem for models with a large number of random variables. Most recently, a reinforcement learning approach has been proposed to find efficient contraction orders in tensor networks. Due to the duality between graphical models and tensor networks, we adapt this approach to probabilistic inference in graphical models. Furthermore, we incorporate structure exploitation into the process of finding an optimal order. Currently, the agent's cost function is formulated in terms of intermediate result sizes which are exponential in the number of indices (i.e., random variables). We show that leveraging specific structures during inference allows for introducing compact encodings of intermediate results which can be significantly smaller. By considering the compact encoding sizes for the cost function instead, we enable the agent to explore more efficient contraction orders. The structure we consider in this work is the presence of local symmetries (i.e., symmetries within a model's factors).
Imitation Learning of Correlated Policies in Stackelberg Games
Authors: Kunag-Da Wang, Ping-Chun Hsieh, Wen-Chih Peng
Date: 2025-03-11 00:00:00
Tasks: Imitation Learning

Stackelberg games, widely applied in domains like economics and security, involve asymmetric interactions where a leader's strategy drives follower responses. Accurately modeling these dynamics allows domain experts to optimize strategies in interactive scenarios, such as turn-based sports like badminton. In multi-agent systems, agent behaviors are interdependent, and traditional Multi-Agent Imitation Learning (MAIL) methods often fail to capture these complex interactions. Correlated policies, which account for opponents' strategies, are essential for accurately modeling such dynamics. However, even methods designed for learning correlated policies, like CoDAIL, struggle in Stackelberg games due to their asymmetric decision-making, where leaders and followers cannot simultaneously account for each other's actions, often leading to non-correlated policies. Furthermore, existing MAIL methods that match occupancy measures or use adversarial techniques like GAIL or Inverse RL face scalability challenges, particularly in high-dimensional environments, and suffer from unstable training. To address these challenges, we propose a correlated policy occupancy measure specifically designed for Stackelberg games and introduce the Latent Stackelberg Differential Network (LSDN) to match it. LSDN models two-agent interactions as shared latent state trajectories and uses multi-output Geometric Brownian Motion (MO-GBM) to effectively capture joint policies. By leveraging MO-GBM, LSDN disentangles environmental influences from agent-driven transitions in latent space, enabling the simultaneous learning of interdependent policies. This design eliminates the need for adversarial training and simplifies the learning process. Extensive experiments on Iterative Matrix Games and multi-agent particle environments demonstrate that LSDN can better reproduce complex interaction dynamics than existing MAIL methods.
AI-Driven Decision Support in Oncology: Evaluating Data Readiness for Skin Cancer Treatment
Authors: Joscha Grüger, Tobias Geyer, Tobias Brix, Michael Storck, Sonja Leson, Laura Bley, Carsten Weishaupt, Ralph Bergmann, Stephan A. Braun
Date: 2025-03-12 00:00:00
Tasks: Decision Making

This research focuses on evaluating and enhancing data readiness for the development of an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) in the context of skin cancer treatment. The study, conducted at the Skin Tumor Center of the University Hospital M\"unster, delves into the essential role of data quality, availability, and extractability in implementing effective AI applications in oncology. By employing a multifaceted methodology, including literature review, data readiness assessment, and expert workshops, the study addresses the challenges of integrating AI into clinical decision-making. The research identifies crucial data points for skin cancer treatment decisions, evaluates their presence and quality in various information systems, and highlights the difficulties in extracting information from unstructured data. The findings underline the significance of high-quality, accessible data for the success of AI-driven CDSS in medical settings, particularly in the complex field of oncology.