pwc-archive/papers-with-abstracts huggingface dataset:
Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties
Authors: Ning Liu, Siavash Jafarzadeh, Brian Y. Lattimer, Shuna Ni, Jim Lua, Yue Yu
Date: 2024-07-03 00:00:00

Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis, and design. Their vast number of trainable parameters necessitates a wealth of data to achieve accuracy and mitigate overfitting. However, experimental measurements are often limited and costly to obtain in sufficient quantities for finetuning. To this end, we present a physics-based training pipeline that tackles the pathology of data scarcity. The core enabler is a physics-based modeling framework that generates a multitude of synthetic data to align the LLM to a physically consistent initial state before finetuning. Our framework features a two-phase training strategy: (1) utilizing the large-in-amount while less accurate synthetic data for supervised pretraining, and (2) finetuning the phase-1 model with limited experimental data. We empirically demonstrate that supervised pretraining is vital to obtaining accurate finetuned LLMs, via the lens of learning polymer flammability metrics where cone calorimeter data is sparse.
Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature
Authors: Deepak Ravikumar, Efstathia Soufleri, Kaushik Roy
Date: 2024-07-03 00:00:00
Tasks: Deep Learning, Inference Attack, Membership Inference Attack, Privacy Preserving

In this paper, we explore the properties of loss curvature with respect to input data in deep neural networks. Curvature of loss with respect to input (termed input loss curvature) is the trace of the Hessian of the loss with respect to the input. We investigate how input loss curvature varies between train and test sets, and its implications for train-test distinguishability. We develop a theoretical framework that derives an upper bound on the train-test distinguishability based on privacy and the size of the training set. This novel insight fuels the development of a new black box membership inference attack utilizing input loss curvature. We validate our theoretical findings through experiments in computer vision classification tasks, demonstrating that input loss curvature surpasses existing methods in membership inference effectiveness. Our analysis highlights how the performance of membership inference attack (MIA) methods varies with the size of the training set, showing that curvature-based MIA outperforms other methods on sufficiently large datasets. This condition is often met by real datasets, as demonstrated by our results on CIFAR10, CIFAR100, and ImageNet. These findings not only advance our understanding of deep neural network behavior but also improve the ability to test privacy-preserving techniques in machine learning.
Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis
Authors: Ljubomir Buturovic, Michael Mayhew, Roland Luethy, Kirindi Choi, Uros Midic, Nandita Damaraju, Yehudit Hasin-Brumshtein, Amitesh Pratap, Rhys M. Adams, Joao Fonseca, Ambika Srinath, Paul Fleming, Claudia Pereira, Oliver Liesenfeld, Purvesh Khatri, Timothy Sweeney
Date: 2024-07-03 00:00:00
Tasks: Prognosis

We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. Our solution consists of a Myrna (TM) Instrument and embedded TriVerity (TM) classifiers. The instrument measures abundances of 29 messenger RNAs in patient's blood, subsequently used as features for machine learning. The classifiers convert the input features to an intuitive test report comprising the separate likelihoods of (1) a bacterial infection (2) a viral infection, and (3) severity (need for Intensive Care Unit-level care). In internal validation, the system achieved AUROC = 0.83 on the three-class disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on binary prognosis of disease severity. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration (FDA). This engineering manuscript teaches the standard and novel machine learning methods used to translate an academic research concept to a clinical product aimed at improving patient care, and discusses lessons learned.
Practical Guide for Causal Pathways and Sub-group Disparity Analysis
Authors: Farnaz Kohankhaki, Shaina Raza, Oluwanifemi Bamgbose, Deval Pandya, Elham Dolatabadi
Date: 2024-07-02 00:00:00
Tasks: Attribute

In this study, we introduce the application of causal disparity analysis to unveil intricate relationships and causal pathways between sensitive attributes and the targeted outcomes within real-world observational data. Our methodology involves employing causal decomposition analysis to quantify and examine the causal interplay between sensitive attributes and outcomes. We also emphasize the significance of integrating heterogeneity assessment in causal disparity analysis to gain deeper insights into the impact of sensitive attributes within specific sub-groups on outcomes. Our two-step investigation focuses on datasets where race serves as the sensitive attribute. The results on two datasets indicate the benefit of leveraging causal analysis and heterogeneity assessment not only for quantifying biases in the data but also for disentangling their influences on outcomes. We demonstrate that the sub-groups identified by our approach to be affected the most by disparities are the ones with the largest ML classification errors. We also show that grouping the data only based on a sensitive attribute is not enough, and through these analyses, we can find sub-groups that are directly affected by disparities. We hope that our findings will encourage the adoption of such methodologies in future ethical AI practices and bias audits, fostering a more equitable and fair technological landscape.
A simple algorithm for output range analysis for deep neural networks
Authors: Helder Rojas, Nilton Rojas, Espinoza J. B., Luis Huamanchumo
Date: 2024-07-02 00:00:00

This paper presents a novel approach for the output range estimation problem in Deep Neural Networks (DNNs) by integrating a Simulated Annealing (SA) algorithm tailored to operate within constrained domains and ensure convergence towards global optima. The method effectively addresses the challenges posed by the lack of local geometric information and the high non-linearity inherent to DNNs, making it applicable to a wide variety of architectures, with a special focus on Residual Networks (ResNets) due to their practical importance. Unlike existing methods, our algorithm imposes minimal assumptions on the internal architecture of neural networks, thereby extending its usability to complex models. Theoretical analysis guarantees convergence, while extensive empirical evaluations-including optimization tests involving functions with multiple local minima-demonstrate the robustness of our algorithm in navigating non-convex response surfaces. The experimental results highlight the algorithm's efficiency in accurately estimating DNN output ranges, even in scenarios characterized by high non-linearity and complex constraints. For reproducibility, Python codes and datasets used in the experiments are publicly available through our GitHub repository.
Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps
Authors: Junchi Yang, Murat Yildirim, Qiu Feng
Date: 2024-07-02 00:00:00
Tasks: Distributed Optimization

In distributed machine learning, efficient training across multiple agents with different data distributions poses significant challenges. Even with a centralized coordinator, current algorithms that achieve optimal communication complexity typically require either large minibatches or compromise on gradient complexity. In this work, we tackle both centralized and decentralized settings across strongly convex, convex, and nonconvex objectives. We first demonstrate that a basic primal-dual method, (Accelerated) Gradient Ascent Multiple Stochastic Gradient Descent (GA-MSGD), applied to the Lagrangian of distributed optimization inherently incorporates local updates, because the inner loops of running Stochastic Gradient Descent on the primal variable require no inter-agent communication. Notably, for strongly convex objectives, (Accelerated) GA-MSGD achieves linear convergence in communication rounds despite the Lagrangian being only linear in the dual variables. This is due to a structural property where the dual variable is confined to the span of the coupling matrix, rendering the dual problem strongly concave. When integrated with the Catalyst framework, our approach achieves nearly optimal communication complexity across various settings without the need for minibatches.
Uniform Transformation: Refining Latent Representation in Variational Autoencoders
Authors: Ye Shi, C. S. George Lee
Date: 2024-07-02 00:00:00
Tasks: Density Estimation, Disentanglement, Representation Learning

Irregular distribution in latent space causes posterior collapse, misalignment between posterior and prior, and ill-sampling problem in Variational Autoencoders (VAEs). In this paper, we introduce a novel adaptable three-stage Uniform Transformation (UT) module -- Gaussian Kernel Density Estimation (G-KDE) clustering, non-parametric Gaussian Mixture (GM) Modeling, and Probability Integral Transform (PIT) -- to address irregular latent distributions. By reconfiguring irregular distributions into a uniform distribution in the latent space, our approach significantly enhances the disentanglement and interpretability of latent representations, overcoming the limitation of traditional VAE models in capturing complex data structures. Empirical evaluations demonstrated the efficacy of our proposed UT module in improving disentanglement metrics across benchmark datasets -- dSprites and MNIST. Our findings suggest a promising direction for advancing representation learning techniques, with implication for future research in extending this framework to more sophisticated datasets and downstream tasks.
LLMs Plagiarize: Ensuring Responsible Sourcing of Large Language Model Training Data Through Knowledge Graph Comparison
Authors: Devam Mondal, Carlo Lipizzi
Date: 2024-07-02 00:00:00
Tasks: Knowledge Graphs, Language Modeling, Language Modelling, Large Language Model

In light of recent legal allegations brought by publishers, newspapers, and other creators of copyrighted corpora against large language model developers who use their copyrighted materials for training or fine-tuning purposes, we propose a novel system, a variant of a plagiarism detection system, that assesses whether a knowledge source has been used in the training or fine-tuning of a large language model. Unlike current methods, we utilize an approach that uses Resource Description Framework (RDF) triples to create knowledge graphs from both a source document and an LLM continuation of that document. These graphs are then analyzed with respect to content using cosine similarity and with respect to structure using a normalized version of graph edit distance that shows the degree of isomorphism. Unlike traditional plagiarism systems that focus on content matching and keyword identification between a source and a target corpus, our approach enables a broader and more accurate evaluation of similarity between a source document and LLM continuation by focusing on relationships between ideas and their organization with regards to others. Additionally, our approach does not require access to LLM metrics like perplexity that may be unavailable in closed large language model "black-box" systems, as well as the training corpus. We thus assess whether an LLM has "plagiarized" a corpus in its continuation through similarity measures. A prototype of our system will be found on a hyperlinked GitHub repository.
Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity
Authors: Harshavardhan Kamarthi, Aditya B. Sasanur, Xinjie Tong, Xingyu Zhou, James Peters, Joe Czyzyk, B. Aditya Prakash
Date: 2024-07-02 00:00:00
Tasks: Demand Forecasting, Time Series, Time Series Forecasting

Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5\% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience.
Joint Segmentation and Image Reconstruction with Error Prediction in Photoacoustic Imaging using Deep Learning
Authors: Ruibo Shang, Geoffrey P. Luke, Matthew O'Donnell
Date: 2024-07-02 00:00:00
Tasks: Image Reconstruction

Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level.
Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point
Authors: Bokun Wang, Axel Berg, Durmus Alp Emre Acar, Chuteng Zhou
Date: 2024-07-02 00:00:00
Tasks: Federated Learning

Recent work has shown that 8-bit floating point (FP8) can be used for efficiently training neural networks with reduced computational overhead compared to training in FP32/FP16. In this work, we investigate the use of FP8 training in a federated learning context. This brings not only the usual benefits of FP8 which are desirable for on-device training at the edge, but also reduces client-server communication costs due to significant weight compression. We present a novel method for combining FP8 client training while maintaining a global FP32 server model and provide convergence analysis. Experiments with various machine learning models and datasets show that our method consistently yields communication reductions of at least 2.9x across a variety of tasks and models compared to an FP32 baseline.
Product Geometries on Cholesky Manifolds with Applications to SPD Manifolds
Authors: Ziheng Chen, Yue Song, Xiao-Jun Wu, Nicu Sebe
Date: 2024-07-02 00:00:00

This paper presents two new metrics on the Symmetric Positive Definite (SPD) manifold via the Cholesky manifold, i.e., the space of lower triangular matrices with positive diagonal elements. We first unveil that the existing popular Riemannian metric on the Cholesky manifold can be generally characterized as the product metric of a Euclidean metric and a Riemannian metric on the space of n-dimensional positive vectors. Based on this analysis, we propose two novel metrics on the Cholesky manifolds, i.e., Diagonal Power Euclidean Metric and Diagonal Generalized Bures-Wasserstein Metric, which are numerically stabler than the existing Cholesky metric. We also discuss the gyro structures and deformed metrics associated with our metrics. The gyro structures connect the linear and geometric properties, while the deformed metrics interpolate between our proposed metrics and the existing metric. Further, by Cholesky decomposition, the proposed deformed metrics and gyro structures are pulled back to SPD manifolds. Compared with existing Riemannian metrics on SPD manifolds, our metrics are easy to use, computationally efficient, and numerically stable.
Linear Submodular Maximization with Bandit Feedback
Authors: Wenjing Chen, Victoria G. Crawford
Date: 2024-07-02 00:00:00
Tasks: Data Summarization, Recommendation Systems

Submodular optimization with bandit feedback has recently been studied in a variety of contexts. In a number of real-world applications such as diversified recommender systems and data summarization, the submodular function exhibits additional linear structure. We consider developing approximation algorithms for the maximization of a submodular objective function $f:2^U\to\mathbb{R}_{\geq 0}$, where $f=\sum_{i=1}^dw_iF_{i}$. It is assumed that we have value oracle access to the functions $F_i$, but the coefficients $w_i$ are unknown, and $f$ can only be accessed via noisy queries. We develop algorithms for this setting inspired by adaptive allocation algorithms in the best-arm identification for linear bandit, with approximation guarantees arbitrarily close to the setting where we have value oracle access to $f$. Finally, we empirically demonstrate that our algorithms make vast improvements in terms of sample efficiency compared to algorithms that do not exploit the linear structure of $f$ on instances of move recommendation.
Towards More Realistic Extraction Attacks: An Adversarial Perspective
Authors: Yash More, Prakhar Ganesh, Golnoosh Farnadi
Date: 2024-07-02 00:00:00
Tasks: Memorization

Language models are prone to memorizing parts of their training data which makes them vulnerable to extraction attacks. Existing research often examines isolated setups--such as evaluating extraction risks from a single model or with a fixed prompt design. However, a real-world adversary could access models across various sizes and checkpoints, as well as exploit prompt sensitivity, resulting in a considerably larger attack surface than previously studied. In this paper, we revisit extraction attacks from an adversarial perspective, focusing on how to leverage the brittleness of language models and the multi-faceted access to the underlying data. We find significant churn in extraction trends, i.e., even unintuitive changes to the prompt, or targeting smaller models and earlier checkpoints, can extract distinct information. By combining information from multiple attacks, our adversary is able to increase the extraction risks by up to $2 \times$. Furthermore, even with mitigation strategies like data deduplication, we find the same escalation of extraction risks against a real-world adversary. We conclude with a set of case studies, including detecting pre-training data, copyright violations, and extracting personally identifiable information, showing how our more realistic adversary can outperform existing adversaries in the literature.
Diffusion Models for Tabular Data Imputation and Synthetic Data Generation
Authors: Mario Villaizán-Vallelado, Matteo Salvatori, Carlos Segura, Ioannis Arapakis
Date: 2024-07-02 00:00:00
Tasks: Decoder, Denoising, Imputation, Synthetic Data Generation

Data imputation and data generation have important applications for many domains, like healthcare and finance, where incomplete or missing data can hinder accurate analysis and decision-making. Diffusion models have emerged as powerful generative models capable of capturing complex data distributions across various data modalities such as image, audio, and time series data. Recently, they have been also adapted to generate tabular data. In this paper, we propose a diffusion model for tabular data that introduces three key enhancements: (1) a conditioning attention mechanism, (2) an encoder-decoder transformer as the denoising network, and (3) dynamic masking. The conditioning attention mechanism is designed to improve the model's ability to capture the relationship between the condition and synthetic data. The transformer layers help model interactions within the condition (encoder) or synthetic data (decoder), while dynamic masking enables our model to efficiently handle both missing data imputation and synthetic data generation tasks within a unified framework. We conduct a comprehensive evaluation by comparing the performance of diffusion models with transformer conditioning against state-of-the-art techniques, such as Variational Autoencoders, Generative Adversarial Networks and Diffusion Models, on benchmark datasets. Our evaluation focuses on the assessment of the generated samples with respect to three important criteria, namely: (1) Machine Learning efficiency, (2) statistical similarity, and (3) privacy risk mitigation. For the task of data imputation, we consider the efficiency of the generated samples across different levels of missing features.
Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors
Authors: Dinesh Cyril Selvaraj, Christian Vitale, Tania Panayiotou, Panayiotis Kolios, Carla Fabiana Chiasserini, Georgios Ellinas
Date: 2024-07-02 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning

In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier; (ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles.