pwc-archive/papers-with-abstracts huggingface dataset:
RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks
Authors: Guglielmo Gattiglio, Lyudmila Grigoryeva, Massimiliano Tamborrino
Date: 2024-11-09 00:00:00

Parallel-in-time (PinT) techniques have been proposed to solve systems of time-dependent differential equations by parallelizing the temporal domain. Among them, Parareal computes the solution sequentially using an inaccurate (fast) solver, and then "corrects" it using an accurate (slow) integrator that runs in parallel across temporal subintervals. This work introduces RandNet-Parareal, a novel method to learn the discrepancy between the coarse and fine solutions using random neural networks (RandNets). RandNet-Parareal achieves speed gains up to x125 and x22 compared to the fine solver run serially and Parareal, respectively. Beyond theoretical guarantees of RandNets as universal approximators, these models are quick to train, allowing the PinT solution of partial differential equations on a spatial mesh of up to $10^5$ points with minimal overhead, dramatically increasing the scalability of existing PinT approaches. RandNet-Parareal's numerical performance is illustrated on systems of real-world significance, such as the viscous Burgers' equation, the Diffusion-Reaction equation, the two- and three-dimensional Brusselator, and the shallow water equation.
Variational Bayes Portfolio Construction
Authors: Nicolas Nguyen, James Ridgway, Claire Vernade
Date: 2024-11-09 00:00:00
Tasks: Decision Making

Portfolio construction is the science of balancing reward and risk; it is at the core of modern finance. In this paper, we tackle the question of optimal decision-making within a Bayesian paradigm, starting from a decision-theoretic formulation. Despite the inherent intractability of the optimal decision in any interesting scenarios, we manage to rewrite it as a saddle-point problem. Leveraging the literature on variational Bayes (VB), we propose a relaxation of the original problem. This novel methodology results in an efficient algorithm that not only performs well but is also provably convergent. Furthermore, we provide theoretical results on the statistical consistency of the resulting decision with the optimal Bayesian decision. Using real data, our proposal significantly enhances the speed and scalability of portfolio selection problems. We benchmark our results against state-of-the-art algorithms, as well as a Monte Carlo algorithm targeting the optimal decision.
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
Authors: Mark D. Risser, Marcus M. Noack, Hengrui Luo, Ronald Pandolfi
Date: 2024-11-07 00:00:00
Tasks: Gaussian Processes

The Gaussian process (GP) is a widely used probabilistic machine learning method with implicit uncertainty characterization for stochastic function approximation, stochastic modeling, and analyzing real-world measurements of nonlinear processes. Traditional implementations of GPs involve stationary kernels (also termed covariance functions) that limit their flexibility, and exact methods for inference that prevent application to data sets with more than about ten thousand points. Modern approaches to address stationarity assumptions generally fail to accommodate large data sets, while all attempts to address scalability focus on approximating the Gaussian likelihood, which can involve subjectivity and lead to inaccuracies. In this work, we explicitly derive an alternative kernel that can discover and encode both sparsity and nonstationarity. We embed the kernel within a fully Bayesian GP model and leverage high-performance computing resources to enable the analysis of massive data sets. We demonstrate the favorable performance of our novel kernel relative to existing exact and approximate GP methods across a variety of synthetic data examples. Furthermore, we conduct space-time prediction based on more than one million measurements of daily maximum temperature and verify that our results outperform state-of-the-art methods in the Earth sciences. More broadly, having access to exact GPs that use ultra-scalable, sparsity-discovering, nonstationary kernels allows GP methods to truly compete with a wide variety of machine learning methods.
A Fundamental Accuracy--Robustness Trade-off in Regression and Classification
Authors: Sohail Bahmani
Date: 2024-11-06 00:00:00
Tasks: Adversarial Robustness, regression

We derive a fundamental trade-off between standard and adversarial risk in a rather general situation that formalizes the following simple intuition: "If no (nearly) optimal predictor is smooth, adversarial robustness comes at the cost of accuracy." As a concrete example, we evaluate the derived trade-off in regression with polynomial ridge functions under mild regularity conditions.
$\spadesuit$ SPADE $\spadesuit$ Split Peak Attention DEcomposition
Authors: Malcolm Wolff, Kin G. Olivares, Boris Oreshkin, Sunny Ruan, Sitan Yang, Abhinav Katoch, Shankar Ramasubramanian, Youxin Zhang, Michael W. Mahoney, Dmitry Efimov, Vincent Quenneville-Bélair
Date: 2024-11-06 00:00:00
Tasks: Demand Forecasting

Demand forecasting faces challenges induced by Peak Events (PEs) corresponding to special periods such as promotions and holidays. Peak events create significant spikes in demand followed by demand ramp down periods. Neural networks like MQCNN and MQT overreact to demand peaks by carrying over the elevated PE demand into subsequent Post-Peak-Event (PPE) periods, resulting in significantly over-biased forecasts. To tackle this challenge, we introduce a neural forecasting model called Split Peak Attention DEcomposition, SPADE. This model reduces the impact of PEs on subsequent forecasts by modeling forecasting as consisting of two separate tasks: one for PEs; and the other for the rest. Its architecture then uses masked convolution filters and a specialized Peak Attention module. We show SPADE's performance on a worldwide retail dataset with hundreds of millions of products. Our results reveal an overall PPE improvement of 4.5%, a 30% improvement for most affected forecasts after promotions and holidays, and an improvement in PE accuracy by 3.9%, relative to current production models.
Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? Review and Intercomparison of Existing Models
Authors: Jose González-Abad, José Manuel Gutiérrez
Date: 2024-11-06 00:00:00
Tasks: Prognosis

Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation. Unlike emulators, PP downscaling models are trained on observational data, so it remains an open question whether they can plausibly extrapolate unseen conditions and changes in future emissions scenarios. Here we focus on this problem as the main drawback for the operationalization of these methods and present the results of 1) a literature review to identify state-of-the-art DL models for PP downscaling and 2) an intercomparison experiment to evaluate the performance of these models and to assess their extrapolation capability using a common experimental framework, taking into account the sensitivity of results to different training replicas. We focus on minimum and maximum temperatures and precipitation over Spain, a region with a range of climatic conditions with different influential regional processes. We conclude with a discussion of the findings, limitations of existing methods, and prospects for future development.
Fully Automated Correlated Time Series Forecasting in Minutes
Authors: Xinle Wu, Xingjian Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Bin Yang, Christian S. Jensen
Date: 2024-11-06 00:00:00
Tasks: Correlated Time Series Forecasting, Time Series, Time Series Forecasting

Societal and industrial infrastructures and systems increasingly leverage sensors that emit correlated time series. Forecasting of future values of such time series based on recorded historical values has important benefits. Automatically designed models achieve higher accuracy than manually designed models. Given a forecasting task, which includes a dataset and a forecasting horizon, automated design methods automatically search for an optimal forecasting model for the task in a manually designed search space, and then train the identified model using the dataset to enable the forecasting. Existing automated methods face three challenges. First, the search space is constructed by human experts, rending the methods only semi-automated and yielding search spaces prone to subjective biases. Second, it is time consuming to search for an optimal model. Third, training the identified model for a new task is also costly. These challenges limit the practicability of automated methods in real-world settings. To contend with the challenges, we propose a fully automated and highly efficient correlated time series forecasting framework where the search and training can be done in minutes. The framework includes a data-driven, iterative strategy to automatically prune a large search space to obtain a high-quality search space for a new forecasting task. It includes a zero-shot search strategy to efficiently identify the optimal model in the customized search space. And it includes a fast parameter adaptation strategy to accelerate the training of the identified model. Experiments on seven benchmark datasets offer evidence that the framework is capable of state-of-the-art accuracy and is much more efficient than existing methods.
Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimization
Authors: Shamima Nasrin Tumpa, Kehelwala Dewage Gayan Maduranga
Date: 2024-11-05 00:00:00
Tasks: Prediction, Time Series

This study explores the use of Recurrent Neural Networks (RNN) for real-time cryptocurrency price prediction and optimized trading strategies. Given the high volatility of the cryptocurrency market, traditional forecasting models often fall short. By leveraging RNNs' capability to capture long-term patterns in time-series data, this research aims to improve accuracy in price prediction and develop effective trading strategies. The project follows a structured approach involving data collection, preprocessing, and model refinement, followed by rigorous backtesting for profitability and risk assessment. This work contributes to both the academic and practical fields by providing a robust predictive model and optimized trading strategies that address the challenges of cryptocurrency trading.
The Shapley index for music streaming platforms
Authors: Gustavo Bergantiños, Juan D. Moreno-Ternero
Date: 2024-11-11 00:00:00

We study an index to measure the popularity of artists in music streaming platforms. This index, which can be used to allocate the amount raised via paid subscriptions among participating artists, is based on the Shapley value, a centerpiece in cooperative game theory. We characterize this Shapley index combining several axioms formalizing principles with normative appeal. This permits to place the index in the literature, as an alternative to the well-known (and widely used in the industry) pro-rata and user-centric indices.
Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation
Authors: Ziwei Liu, Liang Zhang, Qian Li, Jianghua Wu, Guangxu Zhu
Date: 2024-11-11 00:00:00
Tasks: Hallucination, Information Retrieval, Open-Domain Question Answering, Question Answering, RAG, Representation Learning, Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) has shown impressive capability in providing reliable answer predictions and addressing hallucination problems. A typical RAG implementation uses powerful retrieval models to extract external information and large language models (LLMs) to generate answers. In contrast, recent LLM-based retrieval has gained attention for its substantial improvements in information retrieval (IR) due to the LLMs' semantic understanding capability. However, directly applying LLM to RAG systems presents challenges. This may cause feature locality problems as massive parametric knowledge can hinder effective usage of global information across the corpus; for example, an LLM-based retriever often inputs document summaries instead of full documents. Moreover, various pre-trained tasks in LLMs introduce variance, further weakening performance as a retriever. To address these issues, we propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning to tackle feature locality issues. To enhance retrieval performance, we develop two patterns (invariant and variant patterns) and an invariance loss to reduce LLM variance. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information. Experimental results show that Invar-RAG significantly outperforms existing baselines across three open-domain question answering (ODQA) datasets. Code is available in the Supplementary Material for reproducibility.
LLM-Assisted Relevance Assessments: When Should We Ask LLMs for Help?
Authors: Rikiya Takehi, Ellen M. Voorhees, Tetsuya Sakai, Ian Soboroff
Date: 2024-11-11 00:00:00
Tasks: Information Retrieval

Test collections are information retrieval tools that allow researchers to quickly and easily evaluate ranking algorithms. While test collections have become an integral part of IR research, the process of data creation involves significant effort in manual annotations, which often makes it very expensive and time-consuming. Thus, test collections could become too small when the budget is limited, which may lead to unstable evaluations. As a cheaper alternative, recent studies have proposed the use of large language models (LLMs) to completely replace human assessors. However, while LLMs seem to somewhat correlate with human judgments, their predictions are not perfect and often show bias. Thus a complete replacement with LLMs is argued to be too risky and not fully reliable. Thus, in this paper, we propose LLM-Assisted Relevance Assessments (LARA), an effective method to balance manual annotations with LLM annotations, which helps to build a rich and reliable test collection even under a low budget. We use the LLM's predicted relevance probabilities to select the most profitable documents to manually annotate under a budget constraint. With theoretical reasoning, LARA effectively guides the human annotation process by actively learning to calibrate the LLM's predicted relevance probabilities. Then, using the calibration model learned from the limited manual annotations, LARA debiases the LLM predictions to annotate the remaining non-assessed data. Empirical evaluations on TREC-7 Ad Hoc, TREC-8 Ad Hoc, TREC Robust 2004, and TREC-COVID datasets show that LARA outperforms alternative solutions under almost any budget constraint.
Boosting the Targeted Transferability of Adversarial Examples via Salient Region & Weighted Feature Drop
Authors: Shanjun Xu, Linghui Li, Kaiguo Yuan, Bingyu Li
Date: 2024-11-11 00:00:00

Deep neural networks can be vulnerable to adversarially crafted examples, presenting significant risks to practical applications. A prevalent approach for adversarial attacks relies on the transferability of adversarial examples, which are generated from a substitute model and leveraged to attack unknown black-box models. Despite various proposals aimed at improving transferability, the success of these attacks in targeted black-box scenarios is often hindered by the tendency for adversarial examples to overfit to the surrogate models. In this paper, we introduce a novel framework based on Salient region & Weighted Feature Drop (SWFD) designed to enhance the targeted transferability of adversarial examples. Drawing from the observation that examples with higher transferability exhibit smoother distributions in the deep-layer outputs, we propose the weighted feature drop mechanism to modulate activation values according to weights scaled by norm distribution, effectively addressing the overfitting issue when generating adversarial examples. Additionally, by leveraging salient region within the image to construct auxiliary images, our method enables the adversarial example's features to be transferred to the target category in a model-agnostic manner, thereby enhancing the transferability. Comprehensive experiments confirm that our approach outperforms state-of-the-art methods across diverse configurations. On average, the proposed SWFD raises the attack success rate for normally trained models and robust models by 16.31% and 7.06% respectively.
Generating Mixcode Popular Songs with Artificial Intelligence: Concepts, Plans, and Speculations
Authors: Abhishek Kaushik, Kayla Rush
Date: 2024-11-10 00:00:00

Music is a potent form of expression that can communicate, accentuate or even create the emotions of an individual or a collective. Both historically and in contemporary experiences, musical expression was and is commonly instrumentalized for social, political and/or economic purposes. Generative artificial intelligence provides a wealth of both opportunities and challenges with regard to music and its role in society. This paper discusses a proposed project integrating artificial intelligence and popular music, with the ultimate goal of creating a powerful tool for implementing music for social transformation, education, healthcare, and emotional well-being. Given that it is being presented at the outset of a collaboration between a computer scientist/data analyst and an ethnomusicologist/social anthropologist. it is mainly conceptual and somewhat speculative in nature.
GuidelineGuard: An Agentic Framework for Medical Note Evaluation with Guideline Adherence
Authors: MD Ragib Shahriyear
Date: 2024-11-09 00:00:00

Although rapid advancements in Large Language Models (LLMs) are facilitating the integration of artificial intelligence-based applications and services in healthcare, limited research has focused on the systematic evaluation of medical notes for guideline adherence. This paper introduces GuidelineGuard, an agentic framework powered by LLMs that autonomously analyzes medical notes, such as hospital discharge and office visit notes, to ensure compliance with established healthcare guidelines. By identifying deviations from recommended practices and providing evidence-based suggestions, GuidelineGuard helps clinicians adhere to the latest standards from organizations like the WHO and CDC. This framework offers a novel approach to improving documentation quality and reducing clinical errors.
Annotative Indexing
Authors: Charles L. A. Clarke
Date: 2024-11-09 00:00:00
Tasks: Entity Retrieval, Knowledge Graphs, Retrieval, Retrieval-augmented Generation

This paper introduces annotative indexing, a novel framework that unifies and generalizes traditional inverted indexes, column stores, object stores, and graph databases. As a result, annotative indexing can provide the underlying indexing framework for databases that support retrieval augmented generation, knowledge graphs, entity retrieval, semi-structured data, and ranked retrieval. While we primarily focus on human language data in the form of text, annotative indexing is sufficiently general to support a range of other datatypes, and we provide examples of SQL-like queries over a JSON store that includes numbers and dates. Taking advantage of the flexibility of annotative indexing, we also demonstrate a fully dynamic annotative index incorporating support for ACID properties of transactions with hundreds of multiple concurrent readers and writers.
KeyB2: Selecting Key Blocks is Also Important for Long Document Ranking with Large Language Models
Authors: Minghan Li, Eric Gaussier, Juntao Li, Guodong Zhou
Date: 2024-11-09 00:00:00
Tasks: Document Ranking, GPU, Information Retrieval, Reranking, Retrieval

The rapid development of large language models (LLMs) like Llama has significantly advanced information retrieval (IR) systems. However, using LLMs for long documents, as in RankLLaMA, remains challenging due to computational complexity, especially concerning input token length. Furthermore, the internal mechanisms of LLMs during ranking are still not fully understood. In this paper, we first explore the internal workings of LLMs during relevance judgement and identify that specific attention heads play a crucial role in aligning relevant tokens. This observation inspires us to revisit the block pre-ranking strategy used in KeyB, which remains state-of-the-art (SOTA) on the TREC 2019 DL document ranking dataset. Building on these insights, we develop KeyB2, an advanced long document IR approach that integrates block pre-ranking with the performance of LLMs. KeyB2 efficiently identifies and processes the most relevant blocks, reducing computational costs and improving ranking effectiveness. Additionally, we introduce a new bi-encoder block matching strategy for KeyB2. Comprehensive experiments on long-document datasets, including TREC 2019 DL, Robust04, and MLDR-zh, show that KeyB2 outperforms baselines like RankLLaMA and KeyB by reducing reranking time and GPU memory usage while enhancing retrieval performance, achieving new SOTA results on TREC 2019 DL with higher NDCG@10 and MAP scores.
Interpret the Internal States of Recommendation Model with Sparse Autoencoder
Authors: Jiayin Wang, XiaoYu Zhang, Weizhi Ma, Min Zhang
Date: 2024-11-09 00:00:00
Tasks: Explainable Recommendation, Fairness, Recommendation Systems

Explainable recommendation systems are important to enhance transparency, accuracy, and fairness. Beyond result-level explanations, model-level interpretations can provide valuable insights that allow developers to optimize system designs and implement targeted improvements. However, most current approaches depend on specialized model designs, which often lack generalization capabilities. Given the various kinds of recommendation models, existing methods have limited ability to effectively interpret them. To address this issue, we propose RecSAE, an automatic, generalizable probing method for interpreting the internal states of Recommendation models with Sparse AutoEncoder. RecSAE serves as a plug-in module that does not affect original models during interpretations, while also enabling predictable modifications to their behaviors based on interpretation results. Firstly, we train an autoencoder with sparsity constraints to reconstruct internal activations of recommendation models, making the RecSAE latents more interpretable and monosemantic than the original neuron activations. Secondly, we automated the construction of concept dictionaries based on the relationship between latent activations and input item sequences. Thirdly, RecSAE validates these interpretations by predicting latent activations on new item sequences using the concept dictionary and deriving interpretation confidence scores from precision and recall. We demonstrate RecSAE's effectiveness on two datasets, identifying hundreds of highly interpretable concepts from pure ID-based models. Latent ablation studies further confirm that manipulating latent concepts produces corresponding changes in model output behavior, underscoring RecSAE's utility for both understanding and targeted tuning recommendation models. Code and data are publicly available at https://github.com/Alice1998/RecSAE.