pwc-archive/papers-with-abstracts huggingface dataset:
Efficient and Scalable Deep Reinforcement Learning for Mean Field Control Games
Authors: Nianli Peng, Yilin Wang
Date: 2024-12-28 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Mean Field Control Games (MFCGs) provide a powerful theoretical framework for analyzing systems of infinitely many interacting agents, blending elements from Mean Field Games (MFGs) and Mean Field Control (MFC). However, solving the coupled Hamilton-Jacobi-Bellman and Fokker-Planck equations that characterize MFCG equilibria remains a significant computational challenge, particularly in high-dimensional or complex environments. This paper presents a scalable deep Reinforcement Learning (RL) approach to approximate equilibrium solutions of MFCGs. Building on previous works, We reformulate the infinite-agent stochastic control problem as a Markov Decision Process, where each representative agent interacts with the evolving mean field distribution. We use the actor-critic based algorithm from a previous paper (Angiuli et.al., 2024) as the baseline and propose several versions of more scalable and efficient algorithms, utilizing techniques including parallel sample collection (batching); mini-batching; target network; proximal policy optimization (PPO); generalized advantage estimation (GAE); and entropy regularization. By leveraging these techniques, we effectively improved the efficiency, scalability, and training stability of the baseline algorithm. We evaluate our method on a linear-quadratic benchmark problem, where an analytical solution to the MFCG equilibrium is available. Our results show that some versions of our proposed approach achieve faster convergence and closely approximate the theoretical optimum, outperforming the baseline algorithm by an order of magnitude in sample efficiency. Our work lays the foundation for adapting deep RL to solve more complicated MFCGs closely related to real life, such as large-scale autonomous transportation systems, multi-firm economic competition, and inter-bank borrowing problems.
AdvAnchor: Enhancing Diffusion Model Unlearning with Adversarial Anchors
Authors: Mengnan Zhao, Lihe Zhang, Xingyi Yang, Tianhang Zheng, BaoCai Yin
Date: 2024-12-28 00:00:00
Tasks: model

Security concerns surrounding text-to-image diffusion models have driven researchers to unlearn inappropriate concepts through fine-tuning. Recent fine-tuning methods typically align the prediction distributions of unsafe prompts with those of predefined text anchors. However, these techniques exhibit a considerable performance trade-off between eliminating undesirable concepts and preserving other concepts. In this paper, we systematically analyze the impact of diverse text anchors on unlearning performance. Guided by this analysis, we propose AdvAnchor, a novel approach that generates adversarial anchors to alleviate the trade-off issue. These adversarial anchors are crafted to closely resemble the embeddings of undesirable concepts to maintain overall model performance, while selectively excluding defining attributes of these concepts for effective erasure. Extensive experiments demonstrate that AdvAnchor outperforms state-of-the-art methods. Our code is publicly available at https://anonymous.4open.science/r/AdvAnchor.
Transforming CCTV cameras into NO$_2$ sensors at city scale for adaptive policymaking
Authors: Mohamed R. Ibrahim, Terry Lyons
Date: 2024-12-28 00:00:00

Air pollution in cities, especially NO\textsubscript{2}, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO\textsubscript{2} sensors. Using a predictive graph deep model, we utilised traffic flow from London's cameras in addition to environmental and spatial factors, generating NO\textsubscript{2} predictions from over 133 million frames. Our analysis of London's mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO\textsubscript{2} levels, sometimes with temporal lags of up to 6 hours. For instance, if trucks only drive at night, their effects on NO\textsubscript{2} levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO\textsubscript{2} and other pollutants.
VisTabNet: Adapting Vision Transformers for Tabular Data
Authors: Witold Wydmański, Ulvi Movsum-zada, Jacek Tabor, Marek Śmieja
Date: 2024-12-28 00:00:00
Tasks: Transfer Learning

Although deep learning models have had great success in natural language processing and computer vision, we do not observe comparable improvements in the case of tabular data, which is still the most common data type used in biological, industrial and financial applications. In particular, it is challenging to transfer large-scale pre-trained models to downstream tasks defined on small tabular datasets. To address this, we propose VisTabNet -- a cross-modal transfer learning method, which allows for adapting Vision Transformer (ViT) with pre-trained weights to process tabular data. By projecting tabular inputs to patch embeddings acceptable by ViT, we can directly apply a pre-trained Transformer Encoder to tabular inputs. This approach eliminates the conceptual cost of designing a suitable architecture for processing tabular data, while reducing the computational cost of training the model from scratch. Experimental results on multiple small tabular datasets (less than 1k samples) demonstrate VisTabNet's superiority, outperforming both traditional ensemble methods and recent deep learning models. The proposed method goes beyond conventional transfer learning practice and shows that pre-trained image models can be transferred to solve tabular problems, extending the boundaries of transfer learning.
Training-free Heterogeneous Model Merging
Authors: Zhengqi Xu, Han Zheng, Jie Song, Li Sun, Mingli Song
Date: 2024-12-29 00:00:00
Tasks: model

Model merging has attracted significant attention as a powerful paradigm for model reuse, facilitating the integration of task-specific models into a singular, versatile framework endowed with multifarious capabilities. Previous studies, predominantly utilizing methods such as Weight Average (WA), have shown that model merging can effectively leverage pretrained models without the need for laborious retraining. However, the inherent heterogeneity among models poses a substantial constraint on its applicability, particularly when confronted with discrepancies in model architectures. To overcome this challenge, we propose an innovative model merging framework designed for heterogeneous models, encompassing both depth and width heterogeneity. To address depth heterogeneity, we introduce a layer alignment strategy that harmonizes model layers by segmenting deeper models, treating consecutive layers with similar representations as a cohesive segment, thus enabling the seamless merging of models with differing layer depths. For width heterogeneity, we propose a novel elastic neuron zipping algorithm that projects the weights from models of varying widths onto a common dimensional space, eliminating the need for identical widths. Extensive experiments validate the efficacy of these proposed methods, demonstrating that the merging of structurally heterogeneous models can achieve performance levels comparable to those of homogeneous merging, across both vision and NLP tasks. Our code is publicly available at https://github.com/zju-vipa/training_free_heterogeneous_model_merging.
"Generative Models for Financial Time Series Data: Enhancing Signal-to-Noise Ratio and Addressing Data Scarcity in A-Share Market
Authors: Guangming Che
Date: 2024-12-29 00:00:00
Tasks: Data Augmentation, Denoising, Time Series

The financial industry is increasingly seeking robust methods to address the challenges posed by data scarcity and low signal-to-noise ratios, which limit the application of deep learning techniques in stock market analysis. This paper presents two innovative generative model-based approaches to synthesize stock data, specifically tailored for different scenarios within the A-share market in China. The first method, a sector-based synthesis approach, enhances the signal-to-noise ratio of stock data by classifying the characteristics of stocks from various sectors in China's A-share market. This method employs an Approximate Non-Local Total Variation algorithm to smooth the generated data, a bandpass filtering method based on Fourier Transform to eliminate noise, and Denoising Diffusion Implicit Models to accelerate sampling speed. The second method, a recursive stock data synthesis approach based on pattern recognition, is designed to synthesize data for stocks with short listing periods and limited comparable companies. It leverages pattern recognition techniques and Markov models to learn and generate variable-length stock sequences, while introducing a sub-time-level data augmentation method to alleviate data scarcity issues.We validate the effectiveness of these methods through extensive experiments on various datasets, including those from the main board, STAR Market, Growth Enterprise Market Board, Beijing Stock Exchange, NASDAQ, NYSE, and AMEX. The results demonstrate that our synthesized data not only improve the performance of predictive models but also enhance the signal-to-noise ratio of individual stock signals in price trading strategies. Furthermore, the introduction of sub-time-level data significantly improves the quality of synthesized data.
Predicting Preschoolers' Externalizing Problems with Mother-Child Interaction Dynamics and Deep Learning
Authors: Xi Chen, Yu Ji, Cong Xia, Wen Wu
Date: 2024-12-29 00:00:00

Objective: Predicting children's future levels of externalizing problems helps to identify children at risk and guide targeted prevention. Existing studies have shown that mothers providing support in response to children's dysregulation was associated with children's lower levels of externalizing problems. The current study aims to evaluate and improve the accuracy of predicting children's externalizing problems with mother-child interaction dynamics. Method: This study used mother-child interaction dynamics during a challenging puzzle task to predict children's externalizing problems six months later (N=101, 46 boys, Mage=57.41 months, SD=6.58). Performance of the Residual Dynamic Structural Equation Model (RDSEM) was compared with the Attention-based Sequential Behavior Interaction Modeling (ASBIM) model, developed using the deep learning techniques. Results: The RDSEM revealed that children whose mothers provided more autonomy support after increases of child defeat had lower levels of externalizing problems. Five-fold cross-validation showed that the RDSEM had good prediction accuracy. The ASBIM model further improved prediction accuracy, especially after including child inhibitory control as a personalized individual feature. Conclusions: The dynamic process of mother-child interaction provides important information for predicting children's externalizing problems, especially maternal autonomy supportive response to child defeat. The deep learning model is a useful tool to further improve prediction accuracy.
Open-Book Neural Algorithmic Reasoning
Authors: Hefei Li, Chao Peng, Chenyang Xu, Zhengfeng Yang
Date: 2024-12-30 00:00:00

Neural algorithmic reasoning is an emerging area of machine learning that focuses on building neural networks capable of solving complex algorithmic tasks. Recent advancements predominantly follow the standard supervised learning paradigm -- feeding an individual problem instance into the network each time and training it to approximate the execution steps of a classical algorithm. We challenge this mode and propose a novel open-book learning framework. In this framework, whether during training or testing, the network can access and utilize all instances in the training dataset when reasoning for a given instance. Empirical evaluation is conducted on the challenging CLRS Algorithmic Reasoning Benchmark, which consists of 30 diverse algorithmic tasks. Our open-book learning framework exhibits a significant enhancement in neural reasoning capabilities. Further, we notice that there is recent literature suggesting that multi-task training on CLRS can improve the reasoning accuracy of certain tasks, implying intrinsic connections between different algorithmic tasks. We delve into this direction via the open-book framework. When the network reasons for a specific task, we enable it to aggregate information from training instances of other tasks in an attention-based manner. We show that this open-book attention mechanism offers insights into the inherent relationships among various tasks in the benchmark and provides a robust tool for interpretable multi-task training.
A Novel Framework for Learning Stochastic Representations for Sequence Generation and Recognition
Authors: Jungsik Hwang, Ahmadreza Ahmadi
Date: 2024-12-30 00:00:00
Tasks: Motion Generation

The ability to generate and recognize sequential data is fundamental for autonomous systems operating in dynamic environments. Inspired by the key principles of the brain-predictive coding and the Bayesian brain-we propose a novel stochastic Recurrent Neural Network with Parametric Biases (RNNPB). The proposed model incorporates stochasticity into the latent space using the reparameterization trick used in variational autoencoders. This approach enables the model to learn probabilistic representations of multidimensional sequences, capturing uncertainty and enhancing robustness against overfitting. We tested the proposed model on a robotic motion dataset to assess its performance in generating and recognizing temporal patterns. The experimental results showed that the stochastic RNNPB model outperformed its deterministic counterpart in generating and recognizing motion sequences. The results highlighted the proposed model's capability to quantify and adjust uncertainty during both learning and inference. The stochasticity resulted in a continuous latent space representation, facilitating stable motion generation and enhanced generalization when recognizing novel sequences. Our approach provides a biologically inspired framework for modeling temporal patterns and advances the development of robust and adaptable systems in artificial intelligence and robotics.
Machine Learning-Based Security Policy Analysis
Authors: Krish Jain, Joann Sum, Pranav Kapoor, Amir Eaman
Date: 2024-12-30 00:00:00
Tasks: Anomaly Detection

Security-Enhanced Linux (SELinux) is a robust security mechanism that enforces mandatory access controls (MAC), but its policy language's complexity creates challenges for policy analysis and management. This research investigates the automation of SELinux policy analysis using graph-based techniques combined with machine learning approaches to detect policy anomalies. The study addresses two key questions: Can SELinux policy analysis be automated through graph analysis, and how do different anomaly detection models compare in analyzing SELinux policies? We will be comparing different machine learning models by evaluating their effectiveness in detecting policy violations and anomalies. Our approach utilizes Neo4j for graph representation of policies, with Node2vec transforming these graph structures into meaningful vector embeddings that can be processed by our machine learning models. In our results, the MLP Neural Network consistently demonstrated superior performance across different dataset sizes, achieving 95% accuracy with balanced precision and recall metrics, while both Random Forest and SVM models showed competitive but slightly lower performance in detecting policy violations. This combination of graph-based modeling and machine learning provides a more sophisticated and automated approach to understanding and analyzing complex SELinux policies compared to traditional manual analysis methods.
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework
Authors: Jihan Ghanim, Mariette Awad
Date: 2024-12-30 00:00:00
Tasks: Anomaly Detection, Model Selection, Reinforcement Learning (RL), Time Series, Unsupervised Anomaly Detection

Anomaly detection (AD) plays a crucial role in time series applications, primarily because time series data is employed across real-world scenarios. Detecting anomalies poses significant challenges since anomalies take diverse forms making them hard to pinpoint accurately. Previous research has explored different AD models, making specific assumptions with varying sensitivity toward particular anomaly types. To address this issue, we propose a novel model selection for unsupervised AD using a combination of time series forest (TSF) and reinforcement learning (RL) approaches that dynamically chooses an AD technique. Our approach allows for effective AD without explicitly depending on ground truth labels that are often scarce and expensive to obtain. Results from the real-time series dataset demonstrate that the proposed model selection approach outperforms all other AD models in terms of the F1 score metric. For the synthetic dataset, our proposed model surpasses all other AD models except for KNN, with an impressive F1 score of 0.989. The proposed model selection framework also exceeded the performance of GPT-4 when prompted to act as an anomaly detector on the synthetic dataset. Exploring different reward functions revealed that the original reward function in our proposed AD model selection approach yielded the best overall scores. We evaluated the performance of the six AD models on an additional three datasets, having global, local, and clustered anomalies respectively, showing that each AD model exhibited distinct performance depending on the type of anomalies. This emphasizes the significance of our proposed AD model selection framework, maintaining high performance across all datasets, and showcasing superior performance across different anomaly types.
Urban Water Consumption Forecasting Using Deep Learning and Correlated District Metered Areas
Authors: Kleanthis Malialis, Nefeli Mavri, Stelios G. Vrachimis, Marios S. Kyriakou, Demetrios G. Eliades, Marios M. Polycarpou
Date: 2024-12-30 00:00:00
Tasks: Deep Learning

Accurate water consumption forecasting is a crucial tool for water utilities and policymakers, as it helps ensure a reliable supply, optimize operations, and support infrastructure planning. Urban Water Distribution Networks (WDNs) are divided into District Metered Areas (DMAs), where water flow is monitored to efficiently manage resources. This work focuses on short-term forecasting of DMA consumption using deep learning and aims to address two key challenging issues. First, forecasting based solely on a DMA's historical data may lack broader context and provide limited insights. Second, DMAs may experience sensor malfunctions providing incorrect data, or some DMAs may not be monitored at all due to computational costs, complicating accurate forecasting. We propose a novel method that first identifies DMAs with correlated consumption patterns and then uses these patterns, along with the DMA's local data, as input to a deep learning model for forecasting. In a real-world study with data from five DMAs, we show that: i) the deep learning model outperforms a classical statistical model; ii) accurate forecasting can be carried out using only correlated DMAs' consumption patterns; and iii) even when a DMA's local data is available, including correlated DMAs' data improves accuracy.
Class-based Subset Selection for Transfer Learning under Extreme Label Shift
Authors: Akul Goyal, Carl Edwards
Date: 2024-12-30 00:00:00
Tasks: Transfer Learning

Existing work within transfer learning often follows a two-step process -- pre-training over a large-scale source domain and then finetuning over limited samples from the target domain. Yet, despite its popularity, this methodology has been shown to suffer in the presence of distributional shift -- specifically when the output spaces diverge. Previous work has focused on increasing model performance within this setting by identifying and classifying only the shared output classes between distributions. However, these methods are inherently limited as they ignore classes outside the shared class set, disregarding potential information relevant to the model transfer. This paper proposes a new process for few-shot transfer learning that selects and weighs classes from the source domain to optimize the transfer between domains. More concretely, we use Wasserstein distance to choose a set of source classes and their weights that minimize the distance between the source and target domain. To justify our proposed algorithm, we provide a generalization analysis of the performance of the learned classifier over the target domain and show that our method corresponds to a bound minimization algorithm. We empirically demonstrate the effectiveness of our approach (WaSS) by experimenting on several different datasets and presenting superior performance within various label shift settings, including the extreme case where the label spaces are disjoint.
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection
Authors: Hongrui Shi, Valentin Radu, Po Yang
Date: 2024-12-30 00:00:00
Tasks: Federated Learning, Privacy Preserving

With the rapid expansion of edge devices, such as IoT devices, where crucial data needed for machine learning applications is generated, it becomes essential to promote their participation in privacy-preserving Federated Learning (FL) systems. The best way to achieve this desiderate is by reducing their training workload to match their constrained computational resources. While prior FL research has address the workload constrains by introducing lightweight models on the edge, limited attention has been given to optimizing on-device training efficiency through reducing the amount of data need during training. In this work, we propose FedFT-EDS, a novel approach that combines Fine-Tuning of partial client models with Entropy-based Data Selection to reduce training workloads on edge devices. By actively selecting the most informative local instances for learning, FedFT-EDS reduces training data significantly in FL and demonstrates that not all user data is equally beneficial for FL on all rounds. Our experiments on CIFAR-10 and CIFAR-100 show that FedFT-EDS uses only 50% user data while improving the global model performance compared to baseline methods, FedAvg and FedProx. Importantly, FedFT-EDS improves client learning efficiency by up to 3 times, using one third of training time on clients to achieve an equivalent performance to the baselines. This work highlights the importance of data selection in FL and presents a promising pathway to scalable and efficient Federate Learning.