pwc-archive/papers-with-abstracts huggingface dataset:
Neural Networks for Portfolio-Level Risk Management: Portfolio Compression, Static Hedging, Counterparty Credit Risk Exposures and Impact on Capital Requirement
Authors: Vikranth Lokeshwar Dhandapani, Shashi Jain
Date: 2024-02-27 00:00:00
Tasks: Management

In this paper, we present an artificial neural network framework for portfolio compression of a large portfolio of European options with varying maturities (target portfolio) by a significantly smaller portfolio of European options with shorter or same maturity (compressed portfolio), which also represents a self-replicating static hedge portfolio of the target portfolio. For the proposed machine learning architecture, which is consummately interpretable by choice of design, we also define the algorithm to learn model parameters by providing a parameter initialisation technique and leveraging the optimisation methodology proposed in Lokeshwar and Jain (2024), which was initially introduced to price Bermudan options. We demonstrate the convergence of errors and the iterative evolution of neural network parameters over the course of optimization process, using selected target portfolio samples for illustration. We demonstrate through numerical examples that the Exposure distributions and Exposure profiles (Expected Exposure and Potential Future Exposure) of the target portfolio and compressed portfolio align closely across future risk horizons under risk-neutral and real-world scenarios. Additionally, we benchmark the target portfolio's Financial Greeks (Delta, Gamma, and Vega) against the compressed portfolio at future time horizons across different market scenarios generated by Monte-Carlo simulations. Finally, we compare the regulatory capital requirement under the standardised approach for counterparty credit risk of the target portfolio against the compressed portfolio and highlight that the capital requirement for the compact portfolio substantially reduces.
Manager Characteristics and SMEs' Restructuring Decisions: In-Court vs. Out-of-Court Restructuring
Authors: Rachid Achbah
Date: 2024-02-28 00:00:00

This study aims to empirically investigate the impact of managers' characteristics on their choice between in-court and out-of-court restructuring. Based on the theory of upper echelons, we tested the preferences of 342 managers of financially distressed French firms regarding restructuring decisions. The overall findings of this study provide empirical support for the upper echelons theory. Specifically, managers with a long tenure and those with a high level of education are less likely to restructure before the court and are more likely to restructure privately. The findings also indicate that managers' age and gender do not significantly affect their choice between in-court and out-of-court restructuring. This study contributes to the literature on bankruptcy and corporate restructuring by turning the focus from firm characteristics to manager characteristics to explain restructuring decisions.
Quanto Option Pricing on a Multivariate Levy Process Model with a Generative Artificial Intelligence
Authors: Young Shin Kim, Hyun-Gyoon Kim
Date: 2024-02-27 00:00:00

In this study, we discuss a machine learning technique to price exotic options with two underlying assets based on a non-Gaussian Levy process model. We introduce a new multivariate Levy process model named the generalized normal tempered stable (gNTS) process, which is defined by time-changed multivariate Brownian motion. Since the gNTS process does not provide a simple analytic formula for the probability density function (PDF), we use the conditional real-valued non-volume preserving (CRealNVP) model, which is a type of flow-based generative network. Then, we discuss the no-arbitrage pricing on the gNTS model for pricing the quanto option, whose underlying assets consist of a foreign index and foreign exchange rate. We present the training of the CRealNVP model to learn the PDF of the gNTS process using a training set generated by Monte Carlo simulation. Next, we estimate the parameters of the gNTS model with the trained CRealNVP model using the empirical data observed in the market. Finally, we provide a method to find an equivalent martingale measure on the gNTS model and to price the quanto option using the CRealNVP model with the risk-neutral parameters of the gNTS model.
Set-valued Star-Shaped Risk Measures
Authors: Bingchu Nie, Dejian Tian, Long Jiang
Date: 2024-02-28 00:00:00

In this paper, we introduce a new class of set-valued risk measures, named set-valued star-shaped risk measures. Motivated by the results of scalar monetary and star-shaped risk measures, this paper investigates the representation theorems in the set-valued framework. It is demonstrated that set-valued risk measures can be represented as the union of a family of set-valued convex risk measures, and set-valued normalized star-shaped risk measures can be represented as the union of a family of set-valued normalized convex risk measures. The link between set-valued risk measures and set-valued star-shaped risk measures is also established.
Adversarial Example Soups: Improving Transferability and Stealthiness for Free
Authors: Bo Yang, Hengwei Zhang, Jindong Wang, Yulong Yang, Chenhao Lin, Chao Shen, Zhengyu Zhao
Date: 2024-02-27 00:00:00

Transferable adversarial examples cause practical security risks since they can mislead a target model without knowing its internal knowledge. A conventional recipe for maximizing transferability is to keep only the optimal adversarial example from all those obtained in the optimization pipeline. In this paper, for the first time, we question this convention and demonstrate that those discarded, sub-optimal adversarial examples can be reused to boost transferability. Specifically, we propose ``Adversarial Example Soups'' (AES), with AES-tune for averaging discarded adversarial examples in hyperparameter tuning and AES-rand for stability testing. In addition, our AES is inspired by ``model soups'', which averages weights of multiple fine-tuned models for improved accuracy without increasing inference time. Extensive experiments validate the global effectiveness of our AES, boosting 10 state-of-the-art transfer attacks and their combinations by up to 13% against 10 diverse (defensive) target models. We also show the possibility of generalizing AES to other types, e.g., directly averaging multiple in-the-wild adversarial examples that yield comparable success. A promising byproduct of AES is the improved stealthiness of adversarial examples since the perturbation variances are naturally reduced.
Predicting machine failures from multivariate time series: an industrial case study
Authors: Nicolò Oreste Pinciroli Vago, Francesca Forbicini, Piero Fraternali
Date: 2024-02-27 00:00:00
Tasks: Binary Classification, Prediction, Time Series

Non-neural Machine Learning (ML) and Deep Learning (DL) models are often used to predict system failures in the context of industrial maintenance. However, only a few researches jointly assess the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three data sets concerning the operation of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. The problem is formulated as a binary classification task that assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared using multivariate telemetry time series. The results indicate that, in the considered scenarios, the dimension of the prediction windows plays a crucial role and highlight the effectiveness of DL approaches at classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches at classifying similar and repetitive patterns preceding a failure.
Stepwise Self-Consistent Mathematical Reasoning with Large Language Models
Authors: Zilong Zhao, Yao Rong, Dongyang Guo, Emek Gözlüklü, Emir Gülboy, Enkelejda Kasneci
Date: 2024-02-24 00:00:00
Tasks: Math, Mathematical Reasoning

Using Large Language Models for complex mathematical reasoning is difficult, primarily due to the complexity of multi-step reasoning. The main challenges of this process include (1) selecting critical intermediate results to advance the procedure, and (2) limited exploration of potential solutions. To address these issues, we introduce a novel algorithm, namely Stepwise Self-Consistent Chain-of-Thought (SSC-CoT). SSC-CoT employs a strategy of selecting intermediate steps based on the intersection of various reasoning chains. Additionally, SSC-CoT enables the model to discover critical intermediate steps by querying a knowledge graph comprising relevant domain knowledge. To validate SSC-CoT, we present a new dataset, TriMaster100, tailored for complex trigonometry problems. This dataset contains 100 questions, with each solution broken down into scored intermediate steps, facilitating a comprehensive evaluation of the mathematical reasoning process. On TriMaster100, SSC-CoT triples the effectiveness of the state-of-the-art methods. Furthermore, we benchmark SSC-CoT on the widely recognized complex mathematical question dataset, MATH level 5, and it surpasses the second-best method by 7.2% in accuracy. Code and the TriMaster100 dataset can be found at: https://github.com/zhao-zilong/ssc-cot.
BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra
Authors: Parker Glenn, Parag Pravin Dakle, Liang Wang, Preethi Raghavan
Date: 2024-02-27 00:00:00
Tasks: Question Answering

Many existing end-to-end systems for hybrid question answering tasks can often be boiled down to a "prompt-and-pray" paradigm, where the user has limited control and insight into the intermediate reasoning steps used to achieve the final result. Additionally, due to the context size limitation of many transformer-based LLMs, it is often not reasonable to expect that the full structured and unstructured context will fit into a given prompt in a zero-shot setting, let alone a few-shot setting. We introduce BlendSQL, a superset of SQLite to act as a unified dialect for orchestrating reasoning across both unstructured and structured data. For hybrid question answering tasks involving multi-hop reasoning, we encode the full decomposed reasoning roadmap into a single interpretable BlendSQL query. Notably, we show that BlendSQL can scale to massive datasets and improve the performance of end-to-end systems while using 35% fewer tokens. Our code is available and installable as a package at https://github.com/parkervg/blendsql.
Acquiring Linguistic Knowledge from Multimodal Input
Authors: Theodor Amariucai, Alex Warstadt
Date: 2024-02-27 00:00:00
Tasks: Language Modelling

In contrast to children, language models (LMs) exhibit considerably inferior data efficiency when acquiring language. In this submission to the BabyLM Challenge (Warstadt et al., 2023), we test the hypothesis that this data efficiency gap is partly caused by a lack of multimodal input and grounding in the learning environment of typical language models. Although previous work looking into this question found that multimodal training can even harm language-only performance, we speculate that these findings can be attributed to catastrophic forgetting of complex language due to fine-tuning on captions data. To test our hypothesis, we perform an ablation study on FLAVA (Singh et al., 2022), a multimodal vision-and-language model, independently varying the volume of text and vision input to quantify how much text data (if any) can be offset by vision at different data scales. We aim to limit catastrophic forgetting through a multitask pretraining regime that includes unimodal text-only tasks and data sampled from WiT, the relatively diverse Wikipedia-based dataset (Srinivasan et al., 2021). Our results are largely negative: Multimodal pretraining does not harm our models' language performance but does not consistently help either. That said, our conclusions are limited by our having been able to conduct only a small number of runs. While we must leave open the possibility that multimodal input explains some of the gap in data efficiency between LMs and humans, positive evidence for this hypothesis will require better architectures and techniques for multimodal training.
SparseLLM: Towards Global Pruning for Pre-trained Language Models
Authors: Guangji Bai, Yijiang Li, Chen Ling, Kibaek Kim, Liang Zhao
Date: 2024-02-28 00:00:00
Tasks: Computational Efficiency, Problem Decomposition

The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands. Pruning has emerged as a pivotal compression strategy, introducing sparsity to enhance both memory and computational efficiency. Yet, traditional global pruning is impractical for LLMs due to scalability issues, while local pruning, despite its efficiency, leads to suboptimal solutions. Addressing these challenges, we propose SparseLLM, a novel framework that redefines the global pruning process into manageable, coordinated subproblems, allowing for resource-efficient optimization with global optimality. SparseLLM's approach, which conceptualizes LLMs as a chain of modular functions and leverages auxiliary variables for problem decomposition, not only facilitates a pragmatic application on LLMs but also demonstrates significant performance improvements, particularly in high-sparsity regimes where it surpasses current state-of-the-art methods.
Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance Gaps
Authors: Giuseppe Attanasio, Beatrice Savoldi, Dennis Fucci, Dirk Hovy
Date: 2024-02-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Current automatic speech recognition (ASR) models are designed to be used across many languages and tasks without substantial changes. However, this broad language coverage hides performance gaps within languages, for example, across genders. Our study systematically evaluates the performance of two widely used multilingual ASR models on three datasets, encompassing 19 languages from eight language families and two speaking conditions. Our findings reveal clear gender disparities, with the advantaged group varying across languages and models. Surprisingly, those gaps are not explained by acoustic or lexical properties. However, probing internal model states reveals a correlation with gendered performance gap. That is, the easier it is to distinguish speaker gender in a language using probes, the more the gap reduces, favoring female speakers. Our results show that gender disparities persist even in state-of-the-art models. Our findings have implications for the improvement of multilingual ASR systems, underscoring the importance of accessibility to training data and nuanced evaluation to predict and mitigate gender gaps. We release all code and artifacts at https://github.com/g8a9/multilingual-asr-gender-gap.
Collaborative decoding of critical tokens for boosting factuality of large language models
Authors: Lifeng Jin, Baolin Peng, Linfeng Song, Haitao Mi, Ye Tian, Dong Yu
Date: 2024-02-28 00:00:00
Tasks: Hallucination, Instruction Following

The most common training pipeline for large language models includes pretraining, finetuning and aligning phases, with their respective resulting models, such as the pretrained model and the finetuned model. Finetuned and aligned models show improved abilities of instruction following and safe generation, however their abilities to stay factual about the world are impacted by the finetuning process. Furthermore, the common practice of using sampling during generation also increases chances of hallucination. In this work, we introduce a collaborative decoding framework to harness the high factuality within pretrained models through the concept of critical tokens. We first design a critical token classifier to decide which model to use for the next token, and subsequently generates the next token using different decoding strategies. Experiments with different models and datasets show that our decoding framework is able to reduce model hallucination significantly, showcasing the importance of the collaborative decoding framework.
Wilcoxon Nonparametric CFAR Scheme for Ship Detection in SAR Image
Authors: Xiangwei Meng
Date: 2024-01-11 00:00:00

The parametric constant false alarm rate (CFAR) detection algorithms which are based on various statistical distributions, such as Gaussian, Gamma, Weibull, log-normal, G0 distribution, alpha-stable distribution, etc, are most widely used to detect the ship targets in SAR image at present. However, the clutter background in SAR images is complicated and variable. When the actual clutter background deviates from the assumed statistical distribution, the performance of the parametric CFAR detector will deteriorate. In addition to the parametric CFAR schemes, there is another class of nonparametric CFAR detectors which can maintain a constant false alarm rate for the target detection without the assumption of a known clutter distribution. In this work, the Wilcoxon nonparametric CFAR scheme for ship detection in SAR image is proposed and analyzed, and a closed form of the false alarm rate for the Wilcoxon nonparametric detector to determine the decision threshold is presented. By comparison with several typical parametric CFAR schemes on Radarsat-2, ICEYE-X6 and Gaofen-3 SAR images, the robustness of the Wilcoxon nonparametric detector to maintain a good false alarm performance in different detection backgrounds is revealed, and its detection performance for the weak ship in rough sea surface is improved to some extent. Moreover, the Wilcoxon nonparametric detector can suppress the false alarms resulting from the sidelobes at some degree and its detection speed is fast.
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution
Authors: Hongjun Wang, Jiyuan Chen, Yinqiang Zheng, Tieyong Zeng
Date: 2024-02-29 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Super-Resolution, Super-Resolution

Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. %Despite the substantial advancement% While most existing work assumes a simple and fixed degradation model (e.g., bicubic downsampling), the research of Blind SR seeks to improve model generalization ability with unknown degradation. Recently, Kong et al pioneer the investigation of a more suitable training strategy for Blind SR using Dropout. Although such method indeed brings substantial generalization improvements via mitigating overfitting, we argue that Dropout simultaneously introduces undesirable side-effect that compromises model's capacity to faithfully reconstruct fine details. We show both the theoretical and experimental analyses in our paper, and furthermore, we present another easy yet effective training strategy that enhances the generalization ability of the model by simply modulating its first and second-order features statistics. Experimental results have shown that our method could serve as a model-agnostic regularization and outperforms Dropout on seven benchmark datasets including both synthetic and real-world scenarios.
FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds
Authors: Hadi Tabealhojeh, Soumava Kumar Roy, Peyman Adibi, Hossein Karshenas
Date: 2024-02-28 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Meta-learning problem is usually formulated as a bi-level optimization in which the task-specific and the meta-parameters are updated in the inner and outer loops of optimization, respectively. However, performing the optimization in the Riemannian space, where the parameters and meta-parameters are located on Riemannian manifolds is computationally intensive. Unlike the Euclidean methods, the Riemannian backpropagation needs computing the second-order derivatives that include backward computations through the Riemannian operators such as retraction and orthogonal projection. This paper introduces a Hessian-free approach that uses a first-order approximation of derivatives on the Stiefel manifold. Our method significantly reduces the computational load and memory footprint. We show how using a Stiefel fully-connected layer that enforces orthogonality constraint on the parameters of the last classification layer as the head of the backbone network, strengthens the representation reuse of the gradient-based meta-learning methods. Our experimental results across various few-shot learning datasets, demonstrate the superiority of our proposed method compared to the state-of-the-art methods, especially MAML, its Euclidean counterpart.
Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses
Authors: Jianxin Lei, Chengcai Xu, Langqing Shi, Junhui Hou, Ping Zhou
Date: 2024-02-29 00:00:00
Tasks: Super-Resolution

In this paper, we design a beam splitter-based hybrid light field imaging prototype to record 4D light field image and high-resolution 2D image simultaneously, and make a hybrid light field dataset. The 2D image could be considered as the high-resolution ground truth corresponding to the low-resolution central sub-aperture image of 4D light field image. Subsequently, we propose an unsupervised learning-based super-resolution framework with the hybrid light field dataset, which adaptively settles the light field spatial super-resolution problem with a complex degradation model. Specifically, we design two loss functions based on pre-trained models that enable the super-resolution network to learn the detailed features and light field parallax structure with only one ground truth. Extensive experiments demonstrate the same superiority of our approach with supervised learning-based state-of-the-art ones. To our knowledge, it is the first end-to-end unsupervised learning-based spatial super-resolution approach in light field imaging research, whose input is available from our beam splitter-based hybrid light field system. The hardware and software together may help promote the application of light field super-resolution to a great extent.