pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing PPO with Trajectory-Aware Hybrid Policies
Authors: Qisai Liu, Zhanhong Jiang, Hsin-Jung Yang, Mahsa Khosravi, Joshua R. Waite, Soumik Sarkar
Date: 2025-02-21 00:00:00
Tasks: continuous-control, Continuous Control

Proximal policy optimization (PPO) is one of the most popular state-of-the-art on-policy algorithms that has become a standard baseline in modern reinforcement learning with applications in numerous fields. Though it delivers stable performance with theoretical policy improvement guarantees, high variance, and high sample complexity still remain critical challenges in on-policy algorithms. To alleviate these issues, we propose Hybrid-Policy Proximal Policy Optimization (HP3O), which utilizes a trajectory replay buffer to make efficient use of trajectories generated by recent policies. Particularly, the buffer applies the "first in, first out" (FIFO) strategy so as to keep only the recent trajectories to attenuate the data distribution drift. A batch consisting of the trajectory with the best return and other randomly sampled ones from the buffer is used for updating the policy networks. The strategy helps the agent to improve its capability on top of the most recent best performance and in turn reduce variance empirically. We theoretically construct the policy improvement guarantees for the proposed algorithm. HP3O is validated and compared against several baseline algorithms using multiple continuous control environments. Our code is available here.
Near Optimal Decision Trees in a SPLIT Second
Authors: Varun Babbar, Hayden McTavish, Cynthia Rudin, Margo Seltzer
Date: 2025-02-21 00:00:00
Tasks: Interpretable Machine Learning

Decision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is fast but provably suboptimal. Recent approaches find the global optimum using branch and bound with dynamic programming, showing substantial improvements in accuracy and sparsity at great cost to scalability. An ideal solution would have the accuracy of an optimal method and the scalability of a greedy method. We introduce a family of algorithms called SPLIT (SParse Lookahead for Interpretable Trees) that moves us significantly forward in achieving this ideal balance. We demonstrate that not all sub-problems need to be solved to optimality to find high quality trees; greediness suffices near the leaves. Since each depth adds an exponential number of possible trees, this change makes our algorithms orders of magnitude faster than existing optimal methods, with negligible loss in performance. We extend this algorithm to allow scalable computation of sets of near-optimal trees (i.e., the Rashomon set).
Mean-Shift Distillation for Diffusion Mode Seeking
Authors: Vikas Thamizharasan, Nikitas Chatzis, Iliyan Georgiev, Matthew Fisher, Evangelos Kalogerakis, Difan Liu, Nanxuan Zhao, Michal Lukac
Date: 2025-02-21 00:00:00
Tasks: Text to 3D

We present mean-shift distillation, a novel diffusion distillation technique that provides a provably good proxy for the gradient of the diffusion output distribution. This is derived directly from mean-shift mode seeking on the distribution, and we show that its extrema are aligned with the modes. We further derive an efficient product distribution sampling procedure to evaluate the gradient. Our method is formulated as a drop-in replacement for score distillation sampling (SDS), requiring neither model retraining nor extensive modification of the sampling procedure. We show that it exhibits superior mode alignment as well as improved convergence in both synthetic and practical setups, yielding higher-fidelity results when applied to both text-to-image and text-to-3D applications with Stable Diffusion.
Human Guided Learning of Transparent Regression Models
Authors: Lukas Pensel, Stefan Kramer
Date: 2025-02-21 00:00:00
Tasks: regression

We present a human-in-the-loop (HIL) approach to permutation regression, the novel task of predicting a continuous value for a given ordering of items. The model is a gradient boosted regression model that incorporates simple human-understandable constraints of the form x < y, i.e. item x has to be before item y, as binary features. The approach, HuGuR (Human Guided Regression), lets a human explore the search space of such transparent regression models. Interacting with HuGuR, users can add, remove, and refine order constraints interactively, while the coefficients are calculated on the fly. We evaluate HuGuR in a user study and compare the performance of user-built models with multiple baselines on 9 data sets. The results show that the user-built models outperform the compared methods on small data sets and in general perform on par with the other methods, while being in principle understandable for humans. On larger datasets from the same domain, machine-induced models begin to outperform the user-built models. Further work will study the trust users have in models when constructed by themselves and how the scheme can be transferred to other pattern domains, such as strings, sequences, trees, or graphs.
News Sentiment as a Predictor for American Domestic Migration
Authors: Benjamin Lane, Simeon Sayer
Date: 2025-02-21 00:00:00

This paper goes into depth on the effect that US News Sentiment from national newspapers has on US interstate migration trends. Through harnessing data from the New York Times between 2010 and 2020, an average sentiment score was calculated, allowing for data to be entered into a neural network. Then a logistic regression model was used to predict interstate migration. The results indicate the model was highly accurate as the mean margin of error was +/- 900 citizens. The predictions from the model were compared with the US Census data from 2010 to 2020 that was used to train the model. Since the input for the model was not exposed to any migration data, the model clearly demonstrated that its results were drawn from sentiment data alone. These findings are significant as they indicate that the role of the press could be used as a predictor for domestic migration which can help the government and businesses understand better what is influencing people to move to certain places.
Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data
Authors: Tamal K. Dey, Shreyas N. Samaga
Date: 2025-02-22 00:00:00
Tasks: Sleep Stage Detection

In this paper, we propose Quasi Zigzag Persistent Homology (QZPH) as a framework for analyzing time-varying data by integrating multiparameter persistence and zigzag persistence. To this end, we introduce a stable topological invariant that captures both static and dynamic features at different scales. We present an algorithm to compute this invariant efficiently. We show that it enhances the machine learning models when applied to tasks such as sleep-stage detection, demonstrating its effectiveness in capturing the evolving patterns in time-varying datasets.
Single Domain Generalization with Model-aware Parametric Batch-wise Mixup
Authors: Marzi Heidari, Yuhong Guo
Date: 2025-02-22 00:00:00
Tasks: Data Augmentation, Domain Generalization

Single Domain Generalization (SDG) remains a formidable challenge in the field of machine learning, particularly when models are deployed in environments that differ significantly from their training domains. In this paper, we propose a novel data augmentation approach, named as Model-aware Parametric Batch-wise Mixup (MPBM), to tackle the challenge of SDG. MPBM deploys adversarial queries generated with stochastic gradient Langevin dynamics, and produces model-aware augmenting instances with a parametric batch-wise mixup generator network that is carefully designed through an innovative attention mechanism. By exploiting inter-feature correlations, the parameterized mixup generator introduces additional versatility in combining features across a batch of instances, thereby enhancing the capacity to generate highly adaptive and informative synthetic instances for specific queries. The synthetic data produced by this adaptable generator network, guided by informative queries, is expected to significantly enrich the representation space covered by the original training dataset and subsequently enhance the prediction model's generalizability across diverse and previously unseen domains. To prevent excessive deviation from the training data, we further incorporate a real-data alignment-based adversarial loss into the learning process of MPBM, regularizing any tendencies toward undesirable expansions. We conduct extensive experiments on several benchmark datasets. The empirical results demonstrate that by augmenting the training set with informative synthesis data, our proposed MPBM method achieves the state-of-the-art performance for single domain generalization.
Category-free Out-of-Distribution Node Detection with Feature Resonance
Authors: Shenzhi Yang, Junbo Zhao, Shouqing Yang, Yixuan Li, Dingyu Yang, Xiaofang Zhang, Haobo Wang
Date: 2025-02-22 00:00:00

Detecting out-of-distribution (OOD) nodes in the graph-based machine-learning field is challenging, particularly when in-distribution (ID) node multi-category labels are unavailable. Thus, we focus on feature space rather than label space and find that, ideally, during the optimization of known ID samples, unknown ID samples undergo more significant representation changes than OOD samples, even if the model is trained to fit random targets, which we called the Feature Resonance phenomenon. The rationale behind it is that even without gold labels, the local manifold may still exhibit smooth resonance. Based on this, we further develop a novel graph OOD framework, dubbed Resonance-based Separation and Learning (RSL), which comprises two core modules: (i) a more practical micro-level proxy of feature resonance that measures the movement of feature vectors in one training step. (ii) integrate with synthetic OOD nodes strategy to train an effective OOD classifier. Theoretically, we derive an error bound showing the superior separability of OOD nodes during the resonance period. Empirically, RSL achieves state-of-the-art performance, reducing the FPR95 metric by an average of 18.51% across five real-world datasets.
Integrating Weather Station Data and Radar for Precipitation Nowcasting: SmaAt-fUsion and SmaAt-Krige-GNet
Authors: Aleksej Cornelissen, Jie Shi, Siamak Mehrkanoon
Date: 2025-02-22 00:00:00
Tasks: Data Integration, Representation Learning

In recent years, data-driven, deep learning-based approaches for precipitation nowcasting have attracted significant attention, showing promising results. However, many existing models fail to fully exploit the extensive atmospheric information available, relying primarily on precipitation data alone. This study introduces two novel deep learning architectures, SmaAt-fUsion and SmaAt-Krige-GNet, specifically designed to enhance precipitation nowcasting by integrating multi-variable weather station data with radar datasets. By leveraging additional meteorological information, these models improve representation learning in the latent space, resulting in enhanced nowcasting performance. The SmaAt-fUsion model extends the SmaAt-UNet framework by incorporating weather station data through a convolutional layer, integrating it into the bottleneck of the network. Conversely, the SmaAt-Krige-GNet model combines precipitation maps with weather station data processed using Kriging, a geo-statistical interpolation method, to generate variable-specific maps. These maps are then utilized in a dual-encoder architecture based on SmaAt-GNet, allowing multi-level data integration. Experimental evaluations were conducted using four years (2016--2019) of weather station and precipitation radar data from the Netherlands. Results demonstrate that SmaAt-Krige-GNet outperforms the standard SmaAt-UNet, which relies solely on precipitation radar data, in low precipitation scenarios, while SmaAt-fUsion surpasses SmaAt-UNet in both low and high precipitation scenarios. This highlights the potential of incorporating discrete weather station data to enhance the performance of deep learning-based weather nowcasting models.
FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes
Authors: Fucheng Guo, Zeyu Luan, Qing Li, Dan Zhao, Yong Jiang
Date: 2025-02-22 00:00:00
Tasks: Federated Learning, Privacy Preserving

Federated Learning (FL) has emerged as an essential framework for distributed machine learning, especially with its potential for privacy-preserving data processing. However, existing FL frameworks struggle to address statistical and model heterogeneity, which severely impacts model performance. While Heterogeneous Federated Learning (HtFL) introduces prototype-based strategies to address the challenges, current approaches face limitations in achieving optimal separation of prototypes. This paper presents FedORGP, a novel HtFL algorithm designed to improve global prototype separation through orthogonality regularization, which not only encourages intra-class prototype similarity but also significantly expands the inter-class angular separation. With the guidance of the global prototype, each client keeps its embeddings aligned with the corresponding prototype in the feature space, promoting directional independence that integrates seamlessly with the cross-entropy (CE) loss. We provide theoretical proof of FedORGP's convergence under non-convex conditions. Extensive experiments demonstrate that FedORGP outperforms seven state-of-the-art baselines, achieving up to 10.12\% accuracy improvement in scenarios where statistical and model heterogeneity coexist.
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets
Authors: Vijay Kumar Sutrakar, Nikhil Mogre
Date: 2025-02-22 00:00:00
Tasks: Clustering, Dimensionality Reduction, Word Embeddings

In this paper, an improved clustering technique for large textual datasets by leveraging fine-tuned word embeddings is presented. WEClustering technique is used as the base model. WEClustering model is fur-ther improvements incorporating fine-tuning contextual embeddings, advanced dimensionality reduction methods, and optimization of clustering algorithms. Experimental results on benchmark datasets demon-strate significant improvements in clustering metrics such as silhouette score, purity, and adjusted rand index (ARI). An increase of 45% and 67% of median silhouette score is reported for the proposed WE-Clustering_K++ (based on K-means) and WEClustering_A++ (based on Agglomerative models), respec-tively. The proposed technique will help to bridge the gap between semantic understanding and statistical robustness for large-scale text-mining tasks.
DUPRE: Data Utility Prediction for Efficient Data Valuation
Authors: Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Quoc Phong Nguyen, See Kiong Ng, Bryan Kian Hsiang Low
Date: 2025-02-22 00:00:00
Tasks: Data Valuation, Prediction

Data valuation is increasingly used in machine learning (ML) to decide the fair compensation for data owners and identify valuable or harmful data for improving ML models. Cooperative game theory-based data valuation, such as Data Shapley, requires evaluating the data utility (e.g., validation accuracy) and retraining the ML model for multiple data subsets. While most existing works on efficient estimation of the Shapley values have focused on reducing the number of subsets to evaluate, our framework, \texttt{DUPRE}, takes an alternative yet complementary approach that reduces the cost per subset evaluation by predicting data utilities instead of evaluating them by model retraining. Specifically, given the evaluated data utilities of some data subsets, \texttt{DUPRE} fits a \emph{Gaussian process} (GP) regression model to predict the utility of every other data subset. Our key contribution lies in the design of our GP kernel based on the sliced Wasserstein distance between empirical data distributions. In particular, we show that the kernel is valid and positive semi-definite, encodes prior knowledge of similarities between different data subsets, and can be efficiently computed. We empirically verify that \texttt{DUPRE} introduces low prediction error and speeds up data valuation for various ML models, datasets, and utility functions.
Advanced Text Analytics -- Graph Neural Network for Fake News Detection in Social Media
Authors: Anantram Patel, Vijay Kumar Sutrakar
Date: 2025-02-22 00:00:00
Tasks: Clustering, Fake News Detection, Graph Neural Network, Text Clustering

Traditional Graph Neural Network (GNN) approaches for fake news detection (FND) often depend on auxiliary, non-textual data such as user interaction histories or content dissemination patterns. However, these data sources are not always accessible, limiting the effectiveness and applicability of such methods. Additionally, existing models frequently struggle to capture the detailed and intricate relationships within textual information, reducing their overall accuracy. In order to address these challenges Advanced Text Analysis Graph Neural Network (ATA-GNN) is proposed in this paper. The proposed model is designed to operate solely on textual data. ATA-GNN employs innovative topic modelling (clustering) techniques to identify typical words for each topic, leveraging multiple clustering dimensions to achieve a comprehensive semantic understanding of the text. This multi-layered design enables the model to uncover intricate textual patterns while contextualizing them within a broader semantic framework, significantly enhancing its interpretative capabilities. Extensive evaluations on widely used benchmark datasets demonstrate that ATA-GNN surpasses the performance of current GNN-based FND methods. These findings validate the potential of integrating advanced text clustering within GNN architectures to achieve more reliable and text-focused detection solutions.
Co-evolution-based Metal-binding Residue Prediction with Graph Neural Networks
Authors: Sayedmohammadreza Rastegari, Sina Tabakhi, Xianyuan Liu, Wei Sang, Haiping Lu
Date: 2025-02-22 00:00:00
Tasks: Graph Neural Network, Prediction

In computational structural biology, predicting metal-binding sites and their corresponding metal types is challenging due to the complexity of protein structures and interactions. Conventional sequence- and structure-based prediction approaches cannot capture the complex evolutionary relationships driving these interactions to facilitate understanding, while recent co-evolution-based approaches do not fully consider the entire structure of the co-evolved residue network. In this paper, we introduce MBGNN (Metal-Binding Graph Neural Network) that utilizes the entire co-evolved residue network and effectively captures the complex dependencies within protein structures via graph neural networks to enhance the prediction of co-evolved metal-binding residues and their associated metal types. Experimental results on a public dataset show that MBGNN outperforms existing co-evolution-based metal-binding prediction methods, and it is also competitive against recent sequence-based methods, showing the potential of integrating co-evolutionary insights with advanced machine learning to deepen our understanding of protein-metal interactions. The MBGNN code is publicly available at https://github.com/SRastegari/MBGNN.
FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths
Authors: Xuqi Mao, Zhenying He, X. Sean Wang
Date: 2025-02-22 00:00:00
Tasks: Graph Embedding, Link Prediction, Node Classification

Graph neural networks (GNNs) have emerged as the state of the art for a variety of graph-related tasks and have been widely used in Heterogeneous Graphs (HetGs), where meta-paths help encode specific semantics between various node types. Despite the revolutionary representation capabilities of existing heterogeneous GNNs (HGNNs) due to their focus on improving the effectiveness of heterogeneity capturing, the huge training costs hinder their practical deployment in real-world scenarios that frequently require handling ad-hoc queries with user-defined meta-paths. To address this, we propose FHGE, a Fast Heterogeneous Graph Embedding designed for efficient, retraining-free generation of meta-path-guided graph embeddings. The key design of the proposed framework is two-fold: segmentation and reconstruction modules. It employs Meta-Path Units (MPUs) to segment the graph into local and global components, enabling swift integration of node embeddings from relevant MPUs during reconstruction and allowing quick adaptation to specific meta-paths. In addition, a dual attention mechanism is applied to enhance semantics capturing. Extensive experiments across diverse datasets demonstrate the effectiveness and efficiency of FHGE in generating meta-path-guided graph embeddings and downstream tasks, such as link prediction and node classification, highlighting its significant advantages for real-time graph analysis in ad-hoc queries.
HetFS: A Method for Fast Similarity Search with Ad-hoc Meta-paths on Heterogeneous Information Networks
Authors: Xuqi Mao, Zhenyi Chen, Zhenying He, Yinan Jing, Kai Zhang, X. Sean Wang
Date: 2025-02-22 00:00:00
Tasks: Graph Neural Network, Link Prediction, Node Classification

Numerous real-world information networks form Heterogeneous Information Networks (HINs) with diverse objects and relations represented as nodes and edges in heterogeneous graphs. Similarity between nodes quantifies how closely two nodes resemble each other, mainly depending on the similarity of the nodes they are connected to, recursively. Users may be interested in only specific types of connections in the similarity definition, represented as meta-paths, i.e., a sequence of node and edge types. Existing Heterogeneous Graph Neural Network (HGNN)-based similarity search methods may accommodate meta-paths, but require retraining for different meta-paths. Conversely, existing path-based similarity search methods may switch flexibly between meta-paths but often suffer from lower accuracy, as they rely solely on path information. This paper proposes HetFS, a Fast Similarity method for ad-hoc queries with user-given meta-paths on Heterogeneous information networks. HetFS provides similarity results based on path information that satisfies the meta-path restriction, as well as node content. Extensive experiments demonstrate the effectiveness and efficiency of HetFS in addressing ad-hoc queries, outperforming state-of-the-art HGNNs and path-based approaches, and showing strong performance in downstream applications, including link prediction, node classification, and clustering.
Risk-Averse Reinforcement Learning: An Optimal Transport Perspective on Temporal Difference Learning
Authors: Zahra Shahrooei, Ali Baheri
Date: 2025-02-22 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Safe Reinforcement Learning

The primary goal of reinforcement learning is to develop decision-making policies that prioritize optimal performance, frequently without considering risk or safety. In contrast, safe reinforcement learning seeks to reduce or avoid unsafe states. This letter introduces a risk-averse temporal difference algorithm that uses optimal transport theory to direct the agent toward predictable behavior. By incorporating a risk indicator, the agent learns to favor actions with predictable consequences. We evaluate the proposed algorithm in several case studies and show its effectiveness in the presence of uncertainty. The results demonstrate that our method reduces the frequency of visits to risky states while preserving performance. A Python implementation of the algorithm is available at https:// github.com/SAILRIT/Risk-averse-TD-Learning.