pwc-archive/papers-with-abstracts huggingface dataset:
The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research
Authors: Jasin Machkour, Michael Muma, Daniel P. Palomar
Date: 2024-10-07 00:00:00
Tasks: Variable Selection

Modern genomics research relies on genome-wide association studies (GWAS) to identify the few genetic variants among potentially millions that are associated with diseases of interest. Only reproducible discoveries of groups of associations improve our understanding of complex polygenic diseases and enable the development of new drugs and personalized medicine. Thus, fast multivariate variable selection methods that have a high true positive rate (TPR) while controlling the false discovery rate (FDR) are crucial. Recently, the T-Rex+GVS selector, a version of the T-Rex selector that uses the elastic net (EN) as a base selector to perform grouped variable election, was proposed. Although it significantly increased the TPR in simulated GWAS compared to the original T-Rex, its comparably high computational cost limits scalability. Therefore, we propose the informed elastic net (IEN), a new base selector that significantly reduces computation time while retaining the grouped variable selection property. We quantify its grouping effect and derive its formulation as a Lasso-type optimization problem, which is solved efficiently within the T-Rex framework by the terminated LARS algorithm. Numerical simulations and a GWAS study demonstrate that the proposed T-Rex+GVS (IEN) exhibits the desired grouping effect, reduces computation time, and achieves the same TPR as T-Rex+GVS (EN) but with lower FDR, which makes it a promising method for large-scale GWAS.
$\ell_1$-norm rank-one symmetric matrix factorization has no spurious second-order stationary points
Authors: Jiewen Guan, Anthony Man-Cho So
Date: 2024-10-07 00:00:00

This paper studies the nonsmooth optimization landscape of the $\ell_1$-norm rank-one symmetric matrix factorization problem using tools from second-order variational analysis. Specifically, as the main finding of this paper, we show that any second-order stationary point (and thus local minimizer) of the problem is actually globally optimal. Besides, some other results concerning the landscape of the problem, such as a complete characterization of the set of stationary points, are also developed, which should be interesting in their own rights. Furthermore, with the above theories, we revisit existing results on the generic minimizing behavior of simple algorithms for nonsmooth optimization and showcase the potential risk of their applications to our problem through several examples. Our techniques can potentially be applied to analyze the optimization landscapes of a variety of other more sophisticated nonsmooth learning problems, such as robust low-rank matrix recovery.
On subdifferential chain rule of matrix factorization and beyond
Authors: Jiewen Guan, Anthony Man-Cho So
Date: 2024-10-07 00:00:00

In this paper, we study equality-type Clarke subdifferential chain rules of matrix factorization and factorization machine. Specifically, we show for these problems that provided the latent dimension is larger than some multiple of the problem size (i.e., slightly overparameterized) and the loss function is locally Lipschitz, the subdifferential chain rules hold everywhere. In addition, we examine the tightness of the analysis through some interesting constructions and make some important observations from the perspective of optimization; e.g., we show that for all this type of problems, computing a stationary point is trivial. Some tensor generalizations and neural extensions are also discussed, albeit they remain mostly open.
Random-projection ensemble dimension reduction
Authors: Wenxing Zhou, Timothy I. Cannings
Date: 2024-10-07 00:00:00
Tasks: Dimensionality Reduction, regression

We introduce a new framework for dimension reduction in the context of high-dimensional regression. Our proposal is to aggregate an ensemble of random projections, which have been carefully chosen based on the empirical regression performance after being applied to the covariates. More precisely, we consider disjoint groups of independent random projections, apply a base regression method after each projection, and retain the projection in each group based on the empirical performance. We aggregate the selected projections by taking the singular value decomposition of their empirical average and then output the leading order singular vectors. A particularly appealing aspect of our approach is that the singular values provide a measure of the relative importance of the corresponding projection directions, which can be used to select the final projection dimension. We investigate in detail (and provide default recommendations for) various aspects of our general framework, including the projection distribution and the base regression method, as well as the number of random projections used. Additionally, we investigate the possibility of further reducing the dimension by applying our algorithm twice in cases where projection dimension recommended in the initial application is too large. Our theoretical results show that the error of our algorithm stabilises as the number of groups of projections increases. We demonstrate the excellent empirical performance of our proposal in a large numerical study using simulated and real data.
Approximate Maximum Likelihood Inference for Acoustic Spatial Capture-Recapture with Unknown Identities, Using Monte Carlo Expectation Maximization
Authors: Yuheng Wang, Juan Ye, Weiye Li, David L. Borchers
Date: 2024-10-06 00:00:00

Acoustic spatial capture-recapture (ASCR) surveys with an array of synchronized acoustic detectors can be an effective way of estimating animal density or call density. However, constructing the capture histories required for ASCR analysis is challenging, as recognizing which detections at different detectors are of which calls is not a trivial task. Because calls from different distances take different times to arrive at detectors, the order in which calls are detected is not necessarily the same as the order in which they are made, and without knowing which detections are of the same call, we do not know how many different calls are detected. We propose a Monte Carlo expectation-maximization (MCEM) estimation method to resolve this unknown call identity problem. To implement the MCEM method in this context, we sample the latent variables from a complete-data likelihood model in the expectation step and use a semi-complete-data likelihood or conditional likelihood in the maximization step. We use a parametric bootstrap to obtain confidence intervals. When we apply our method to a survey of moss frogs, it gives an estimate within 15% of the estimate obtained using data with call capture histories constructed by experts, and unlike this latter estimate, our confidence interval incorporates the uncertainty about call identities. Simulations show it to have a low bias (6%) and coverage probabilities close to the nominal 95% value.
Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky
Authors: Ethan N. Epperly, Joel A. Tropp, Robert J. Webber
Date: 2024-10-04 00:00:00
Tasks: Computational chemistry

Randomly pivoted Cholesky (RPCholesky) is an algorithm for constructing a low-rank approximation of a positive-semidefinite matrix using a small number of columns. This paper develops an accelerated version of RPCholesky that employs block matrix computations and rejection sampling to efficiently simulate the execution of the original algorithm. For the task of approximating a kernel matrix, the accelerated algorithm can run over $40\times$ faster. The paper contains implementation details, theoretical guarantees, experiments on benchmark data sets, and an application to computational chemistry.
Parameter Estimation of Long Memory Stochastic Processes with Deep Neural Networks
Authors: Bálint Csanády, Lóránt Nagy, Dániel Boros, Iván Ivkovic, Dávid Kovács, Dalma Tóth-Lakits, László Márkus, András Lukács
Date: 2024-10-03 00:00:00
Tasks: parameter estimation

We present a purely deep neural network-based approach for estimating long memory parameters of time series models that incorporate the phenomenon of long-range dependence. Parameters, such as the Hurst exponent, are critical in characterizing the long-range dependence, roughness, and self-similarity of stochastic processes. The accurate and fast estimation of these parameters holds significant importance across various scientific disciplines, including finance, physics, and engineering. We harnessed efficient process generators to provide high-quality synthetic training data, enabling the training of scale-invariant 1D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models. Our neural models outperform conventional statistical methods, even those augmented with neural networks. The precision, speed, consistency, and robustness of our estimators are demonstrated through experiments involving fractional Brownian motion (fBm), the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process, and the fractional Ornstein-Uhlenbeck (fOU) process. We believe that our work will inspire further research in the field of stochastic process modeling and parameter estimation using deep learning techniques.
On the SAGA algorithm with decreasing step
Authors: Luis Fredes, Bernard Bercu, Eméric Gbaguidi
Date: 2024-10-02 00:00:00
Tasks: Stochastic Optimization

Stochastic optimization naturally appear in many application areas, including machine learning. Our goal is to go further in the analysis of the Stochastic Average Gradient Accelerated (SAGA) algorithm. To achieve this, we introduce a new $\lambda$-SAGA algorithm which interpolates between the Stochastic Gradient Descent ($\lambda=0$) and the SAGA algorithm ($\lambda=1$). Firstly, we investigate the almost sure convergence of this new algorithm with decreasing step which allows us to avoid the restrictive strong convexity and Lipschitz gradient hypotheses associated to the objective function. Secondly, we establish a central limit theorem for the $\lambda$-SAGA algorithm. Finally, we provide the non-asymptotic $\mathbb{L}^p$ rates of convergence.
Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN
Authors: Fatemeh Lotfi, Fatemeh Afghah
Date: 2024-09-30 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Management, Meta-Learning, Meta Reinforcement Learning

As wireless networks grow to support more complex applications, the Open Radio Access Network (O-RAN) architecture, with its smart RAN Intelligent Controller (RIC) modules, becomes a crucial solution for real-time network data collection, analysis, and dynamic management of network resources including radio resource blocks and downlink power allocation. Utilizing artificial intelligence (AI) and machine learning (ML), O-RAN addresses the variable demands of modern networks with unprecedented efficiency and adaptability. Despite progress in using ML-based strategies for network optimization, challenges remain, particularly in the dynamic allocation of resources in unpredictable environments. This paper proposes a novel Meta Deep Reinforcement Learning (Meta-DRL) strategy, inspired by Model-Agnostic Meta-Learning (MAML), to advance resource block and downlink power allocation in O-RAN. Our approach leverages O-RAN's disaggregated architecture with virtual distributed units (DUs) and meta-DRL strategies, enabling adaptive and localized decision-making that significantly enhances network efficiency. By integrating meta-learning, our system quickly adapts to new network conditions, optimizing resource allocation in real-time. This results in a 19.8% improvement in network management performance over traditional methods, advancing the capabilities of next-generation wireless networks.
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services
Authors: Wei Yuan, Chaoqun Yang, Guanhua Ye, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin
Date: 2024-10-07 00:00:00
Tasks: Contrastive Learning, Privacy Preserving, Sequential Recommendation

Federated sequential recommendation (FedSeqRec) has gained growing attention due to its ability to protect user privacy. Unfortunately, the performance of FedSeqRec is still unsatisfactory because the models used in FedSeqRec have to be lightweight to accommodate communication bandwidth and clients' on-device computational resource constraints. Recently, large language models (LLMs) have exhibited strong transferable and generalized language understanding abilities and therefore, in the NLP area, many downstream tasks now utilize LLMs as a service to achieve superior performance without constructing complex models. Inspired by this successful practice, we propose a generic FedSeqRec framework, FELLAS, which aims to enhance FedSeqRec by utilizing LLMs as an external service. Specifically, FELLAS employs an LLM server to provide both item-level and sequence-level representation assistance. The item-level representation service is queried by the central server to enrich the original ID-based item embedding with textual information, while the sequence-level representation service is accessed by each client. However, invoking the sequence-level representation service requires clients to send sequences to the external LLM server. To safeguard privacy, we implement dx-privacy satisfied sequence perturbation, which protects clients' sensitive data with guarantees. Additionally, a contrastive learning-based method is designed to transfer knowledge from the noisy sequence representation to clients' sequential recommendation models. Furthermore, to empirically validate the privacy protection capability of FELLAS, we propose two interacted item inference attacks. Extensive experiments conducted on three datasets with two widely used sequential recommendation models demonstrate the effectiveness and privacy-preserving capability of FELLAS.
Correcting for Popularity Bias in Recommender Systems via Item Loss Equalization
Authors: Juno Prent, Masoud Mansoury
Date: 2024-10-07 00:00:00
Tasks: Recommendation Systems

Recommender Systems (RS) often suffer from popularity bias, where a small set of popular items dominate the recommendation results due to their high interaction rates, leaving many less popular items overlooked. This phenomenon disproportionately benefits users with mainstream tastes while neglecting those with niche interests, leading to unfairness among users and exacerbating disparities in recommendation quality across different user groups. In this paper, we propose an in-processing approach to address this issue by intervening in the training process of recommendation models. Drawing inspiration from fair empirical risk minimization in machine learning, we augment the objective function of the recommendation model with an additional term aimed at minimizing the disparity in loss values across different item groups during the training process. Our approach is evaluated through extensive experiments on two real-world datasets and compared against state-of-the-art baselines. The results demonstrate the superior efficacy of our method in mitigating the unfairness of popularity bias while incurring only negligible loss in recommendation accuracy.
Decoding MIE: A Novel Dataset Approach Using Topic Extraction and Affiliation Parsing
Authors: Ehsan Bitaraf, Maryam Jafarpour
Date: 2024-10-06 00:00:00
Tasks: Articles, Authorship Attribution, Diversity

The rapid expansion of medical informatics literature presents significant challenges in synthesizing and analyzing research trends. This study introduces a novel dataset derived from the Medical Informatics Europe (MIE) Conference proceedings, addressing the need for sophisticated analytical tools in the field. Utilizing the Triple-A software, we extracted and processed metadata and abstract from 4,606 articles published in the "Studies in Health Technology and Informatics" journal series, focusing on MIE conferences from 1996 onwards. Our methodology incorporated advanced techniques such as affiliation parsing using the TextRank algorithm. The resulting dataset, available in JSON format, offers a comprehensive view of bibliometric details, extracted topics, and standardized affiliation information. Analysis of this data revealed interesting patterns in Digital Object Identifier usage, citation trends, and authorship attribution across the years. Notably, we observed inconsistencies in author data and a brief period of linguistic diversity in publications. This dataset represents a significant contribution to the medical informatics community, enabling longitudinal studies of research trends, collaboration network analyses, and in-depth bibliometric investigations. By providing this enriched, structured resource spanning nearly three decades of conference proceedings, we aim to facilitate novel insights and advancements in the rapidly evolving field of medical informatics.
Metadata-based Data Exploration with Retrieval-Augmented Generation for Large Language Models
Authors: Teruaki Hayashi, Hiroki Sakaji, Jiayi Dai, Randy Goebel
Date: 2024-10-05 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation, Semantic Similarity, Semantic Textual Similarity

Developing the capacity to effectively search for requisite datasets is an urgent requirement to assist data users in identifying relevant datasets considering the very limited available metadata. For this challenge, the utilization of third-party data is emerging as a valuable source for improvement. Our research introduces a new architecture for data exploration which employs a form of Retrieval-Augmented Generation (RAG) to enhance metadata-based data discovery. The system integrates large language models (LLMs) with external vector databases to identify semantic relationships among diverse types of datasets. The proposed framework offers a new method for evaluating semantic similarity among heterogeneous data sources and for improving data exploration. Our study includes experimental results on four critical tasks: 1) recommending similar datasets, 2) suggesting combinable datasets, 3) estimating tags, and 4) predicting variables. Our results demonstrate that RAG can enhance the selection of relevant datasets, particularly from different categories, when compared to conventional metadata approaches. However, performance varied across tasks and models, which confirms the significance of selecting appropriate techniques based on specific use cases. The findings suggest that this approach holds promise for addressing challenges in data exploration and discovery, although further refinement is necessary for estimation tasks.
LLMTemporalComparator: A Tool for Analysing Differences in Temporal Adaptations of Large Language Models
Authors: Reinhard Friedrich Fritsch, Adam Jatowt
Date: 2024-10-05 00:00:00

This study addresses the challenges of analyzing temporal discrepancies in large language models (LLMs) trained on data from different time periods. To facilitate the automatic exploration of these differences, we propose a novel system that compares in a systematic way the outputs of two LLM versions based on user-defined queries. The system first generates a hierarchical topic structure rooted in a user-specified keyword, allowing for an organized comparison of topical categories. Subsequently, it evaluates the generated text by both LLMs to identify differences in vocabulary, information presentation, and underlying themes. This fully automated approach not only streamlines the identification of shifts in public opinion and cultural norms but also enhances our understanding of the adaptability and robustness of machine learning applications in response to temporal changes. By fostering research in continual model adaptation and comparative summarization, this work contributes to the development of more transparent machine learning models capable of capturing the nuances of evolving societal contexts.
Enhancing Retrieval in QA Systems with Derived Feature Association
Authors: Keyush Shah, Abhishek Goyal, Isaac Wasserman
Date: 2024-10-02 00:00:00
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Retrieval augmented generation (RAG) has become the standard in long context question answering (QA) systems. However, typical implementations of RAG rely on a rather naive retrieval mechanism, in which texts whose embeddings are most similar to that of the query are deemed most relevant. This has consequences in subjective QA tasks, where the most relevant text may not directly contain the answer. In this work, we propose a novel extension to RAG systems, which we call Retrieval from AI Derived Documents (RAIDD). RAIDD leverages the full power of the LLM in the retrieval process by deriving inferred features, such as summaries and example questions, from the documents at ingest. We demonstrate that this approach significantly improves the performance of RAG systems on long-context QA tasks.
Eigen-CNN: Eigenimages Plus Eigennoise Level Maps Guided Network for Hyperspectral Image Denoising
Authors: Lina Zhuang, Michael K. Ng, Lianru Gao, Zhicheng Wang
Date: 2024-03-19 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2024 3
Tasks: Denoising, Hyperspectral Image Denoising, Image Denoising

In recent years, neural network-based methods have shown promising results in hyperspectral image (HSI) denoising areas. Real HSIs exhibit the substantial variations in noise distribution due to various factors, such as different imaging techniques, camera variations, imaging environments, and hardware aging. In this article, we develop an eigenimage plus eigennoise level map-guided convolutional neural network (CNN) for HSI denoising. Our main idea is to perform eigendecomposition on HSIs, utilize the low-rank property of HSIs in the spectral dimension and approximate the spectral vectors in a low-dimensional orthogonal subspace, where representation coefficients are called eigenimages. Besides eigenimages, we make use of estimated eigennoise level map as an input to guide the network for denoising. The proposed network can be constructed without restriction in the number of eigencomponents by using all eigenimages and eigennoise level maps of training noisy-clean pairs. In the inference part, the trained network can be used to remove noise in observed eigenimages without restriction in the number of eigencomponents, and an underlying clean image HSI can be estimated by performing orthogonal projection back. Experimental results on both simulated and real HSIs demonstrate the effectiveness of our trained Eigen-CNN compared with state-of-the-art HSI denoising methods. A MATLAB demo of this work is available at: https://github.com/LinaZhuang/HSI-denoiser-Eigen-CNN for the sake of reproducibility.