pwc-archive/papers-with-abstracts huggingface dataset:
Option Pricing and CVA Calculations using the Monte Carlo-Tree (MC-Tree) Method
Authors: Yen Thuan Trinh, Bernard Hanzon
Date: 2022-02-01 00:00:00

The binomial tree method and the Monte Carlo (MC) method are popular methods for solving option pricing problems. However in both methods there is a trade-off between accuracy and speed of computation, both of which are important in applications. We introduce a new method, the MC-Tree method, that combines the MC method with the binomial tree method. It employs a mixing distribution on the tree parameters, which are restricted to give prescribed mean and variance. For the family of mixing densities proposed here, the corresponding compound densities of the tree outcomes at final time are obtained. Ideally the compound density would be (after a logarithmic transformation of the asset prices) Gaussian. Using the fact that in general, when mean and variance are prescribed, the maximum entropy distribution is Gaussian, we look for mixing densities for which the corresponding compound density has high entropy level. The compound densities that we obtain are not exactly Gaussian, but have entropy values close to the maximum possible Gaussian entropy. Furthermore we introduce techniques to correct for the deviation from the ideal Gaussian pricing measure. One of these (distribution correction technique) ensures that expectations calculated with the method are taken with respect to the desired Gaussian measure. The other one (bias-correction technique) ensures that the probability distributions used are risk-neutral in each of the trees. Apart from option pricing, we apply our techniques to develop an algorithm for calculation of the Credit Valuation Adjustment (CVA) to the price of an American option. Numerical examples of the workings of the MC-Tree approach are provided, which show good performance in terms of accuracy and computational speed.
2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log-returns: out-of-sample comparison of conditional EVT models
Authors: Matthew F. Tomlinson, David Greenwood, Marcin Mucha-Kruczynski
Date: 2022-02-02 00:00:00
Tasks: Time Series, Time Series Analysis

Conditional extreme value theory (EVT) methods promise enhanced forecasting of the extreme tail events that often dominate systemic risk. We present an improved two-tailed peaks-over-threshold (2T-POT) Hawkes model that is adapted for conditional quantile forecasting in both the left and right tails of a univariate time series. This is applied to the daily log-returns of six large cap indices. We also take the unique step of fitting the model at multiple exceedance thresholds (from the 1.25% to 25.00% mirrored quantiles). Quantitatively similar asymmetries in Hawkes parameters are found across all six indices, adding further empirical support to a temporal leverage effect in financial price time series in which the impact of losses is not only larger but also more immediate. Out-of-sample backtests find that our 2T-POT Hawkes model is more reliably accurate than the GARCH-EVT model when forecasting (mirrored) value-at-risk and expected shortfall at the 5% coverage level and below. This suggests that asymmetric Hawkes-type arrival dynamics are a better approximation of the true data generating process for extreme daily log-returns than GARCH-type conditional volatility; our 2T-POT Hawkes model therefore presents a better performing alternative for financial risk modelling.
Ontology-Based Production Simulation with OntologySim
Authors: Marvin Carl May, Lars Kiefer, Andreas Kuhnle, Gisela Lanza
Date: 2022-02-03 00:00:00
Proceeding: MDPI 2022 2
Tasks: Manufacturing simulation

Imagine the possibility to save a simulation at any time, modify or analyze it, and restart again with exactly the same state. The conceptualization and its concrete manifestation in the implementation OntologySim is demonstrated in this paper. The presented approach of a fully ontology-based simulation can solve current challenges in modeling and simulation in production science. Due to the individualization and customization of products and the resulting increase in complexity of production, a need for flexibly adaptable simulations arises. This need is exemplified in the trend towards Digital Twins and Digital Shadows. Their application to production systems, against the background of an ever increasing speed of change in such systems, is arduous. Moreover, missing understandability and human interpretability of current approaches hinders successful, goal oriented applications. The OntologySim can help solving this challenge by providing the ability to generate truly cyber physical systems, both interlocked with reality and providing a simulation framework. In a nutshell, this paper presents a discrete-event-based open-source simulation using multi-agency and ontology.
Keywords: ontology; production simulation; multi-agent; digital twin
Linked Data Triples Enhance Document Relevance Classification
Authors: Dinesh Nagumothu, Peter W. Eklund, Bahadorreza Ofoghi, Mohamed Reda Bouadjenek
Date: 2021-07-20 00:00:00
Proceeding: Applied Sciences 2021 7
Tasks: Classification, Deep Learning, Document Text Classification, Information Retrieval, Retrieval

Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news-feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% and show significant improvement over state-of-the art models, like BERT. The findings are tested and empirically validated on an experimental dataset and on two standardized pre-classified news sources, namely the Reuters and 20 News groups datasets.
Generative Flow Networks for Discrete Probabilistic Modeling
Authors: Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio
Date: 2022-02-03 00:00:00

We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data. Building upon the theory of generative flow networks (GFlowNets), we model the generation process by a stochastic data construction policy and thus amortize expensive MCMC exploration into a fixed number of actions sampled from a GFlowNet. We show how GFlowNets can approximately perform large-block Gibbs sampling to mix between modes. We propose a framework to jointly train a GFlowNet with an energy function, so that the GFlowNet learns to sample from the energy distribution, while the energy learns with an approximate MLE objective with negative samples from the GFlowNet. We demonstrate EB-GFN's effectiveness on various probabilistic modeling tasks. Code is publicly available at https://github.com/zdhNarsil/EB_GFN.
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Authors: Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, Himabindu Lakkaraju
Date: 2022-02-03 00:00:00
Tasks: BIG-bench Machine Learning

As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of whether and when the explanations output by these methods disagree with each other, and how such disagreements are resolved in practice. However, there is little to no research that provides answers to these critical questions. In this work, we formalize and study the disagreement problem in explainable machine learning. More specifically, we define the notion of disagreement between explanations, analyze how often such disagreements occur in practice, and how practitioners resolve these disagreements. We first conduct interviews with data scientists to understand what constitutes disagreement between explanations generated by different methods for the same model prediction, and introduce a novel quantitative framework to formalize this understanding. We then leverage this framework to carry out a rigorous empirical analysis with four real-world datasets, six state-of-the-art post hoc explanation methods, and six different predictive models, to measure the extent of disagreement between the explanations generated by various popular explanation methods. In addition, we carry out an online user study with data scientists to understand how they resolve the aforementioned disagreements. Our results indicate that (1) state-of-the-art explanation methods often disagree in terms of the explanations they output, and (2) machine learning practitioners often employ ad hoc heuristics when resolving such disagreements. These findings suggest that practitioners may be relying on misleading explanations when making consequential decisions. They also underscore the importance of developing principled frameworks for effectively evaluating and comparing explanations output by various explanation techniques.
JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension
Authors: ByungHoon So, Kyuhong Byun, Kyungwon Kang, Seongjin Cho
Date: 2022-02-03 00:00:00
Tasks: Articles, Machine Reading Comprehension, Question Answering, Reading Comprehension

Question Answering (QA) is a task in which a machine understands a given document and a question to find an answer. Despite impressive progress in the NLP area, QA is still a challenging problem, especially for non-English languages due to the lack of annotated datasets. In this paper, we present the Japanese Question Answering Dataset, JaQuAD, which is annotated by humans. JaQuAD consists of 39,696 extractive question-answer pairs on Japanese Wikipedia articles. We finetuned a baseline model which achieves 78.92% for F1 score and 63.38% for EM on test set. The dataset and our experiments are available at https://github.com/SkelterLabsInc/JaQuAD.
The Impact of Vaccination on the Infection rate and the Severity of Covid-19
Authors: MD. Rayhan, M. Abdullah-Al-Wadud, M. Helal Uddin Ahmed
Date: 2022-02-03 00:00:00

This study aims to statistically assess the effectiveness of vaccination against SARS-CoV-2. It is indispensable to investigate the relationship between Covid-19 deadliness and vaccination in order to study the impact of vaccine in real-world. We studied rates of infection and death due to Covid-19 in different countries with respect to their levels of vaccination. People who received the required dose of vaccination were considered as fully vaccinated in this study. Based on the percentage of fully vaccinated population, countries were categorized into several groups. Though a high-level study on the vaccine effectiveness may not provide much insight for individual level differences, a global analysis is imperative to infer the influence of vaccination as a controlling measure of the pandemic.
Near-Optimal Learning of Extensive-Form Games with Imperfect Information
Authors: Yu Bai, Chi Jin, Song Mei, Tiancheng Yu
Date: 2022-02-03 00:00:00
Tasks: counterfactual, Form, Open-Ended Question Answering

This paper resolves the open question of designing near-optimal algorithms for learning imperfect-information extensive-form games from bandit feedback. We present the first line of algorithms that require only $\widetilde{\mathcal{O}}((XA+YB)/\varepsilon^2)$ episodes of play to find an $\varepsilon$-approximate Nash equilibrium in two-player zero-sum games, where $X,Y$ are the number of information sets and $A,B$ are the number of actions for the two players. This improves upon the best known sample complexity of $\widetilde{\mathcal{O}}((X^2A+Y^2B)/\varepsilon^2)$ by a factor of $\widetilde{\mathcal{O}}(\max\{X, Y\})$, and matches the information-theoretic lower bound up to logarithmic factors. We achieve this sample complexity by two new algorithms: Balanced Online Mirror Descent, and Balanced Counterfactual Regret Minimization. Both algorithms rely on novel approaches of integrating \emph{balanced exploration policies} into their classical counterparts. We also extend our results to learning Coarse Correlated Equilibria in multi-player general-sum games.
How to Leverage Unlabeled Data in Offline Reinforcement Learning
Authors: Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine
Date: 2022-02-03 00:00:00
Tasks: Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Offline reinforcement learning (RL) can learn control policies from static datasets but, like standard RL methods, it requires reward annotations for every transition. In many cases, labeling large datasets with rewards may be costly, especially if those rewards must be provided by human labelers, while collecting diverse unlabeled data might be comparatively inexpensive. How can we best leverage such unlabeled data in offline RL? One natural solution is to learn a reward function from the labeled data and use it to label the unlabeled data. In this paper, we find that, perhaps surprisingly, a much simpler method that simply applies zero rewards to unlabeled data leads to effective data sharing both in theory and in practice, without learning any reward model at all. While this approach might seem strange (and incorrect) at first, we provide extensive theoretical and empirical analysis that illustrates how it trades off reward bias, sample complexity and distributional shift, often leading to good results. We characterize conditions under which this simple strategy is effective, and further show that extending it with a simple reweighting approach can further alleviate the bias introduced by using incorrect reward labels. Our empirical evaluation confirms these findings in simulated robotic locomotion, navigation, and manipulation settings.
Separating Rule Discovery and Global Solution Composition in a Learning Classifier System
Authors: Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, Jörg Hähner
Date: 2022-02-03 00:00:00
Tasks: Decision Making

While utilization of digital agents to support crucial decision making is increasing, trust in suggestions made by these agents is hard to achieve. However, it is essential to profit from their application, resulting in a need for explanations for both the decision making process and the model. For many systems, such as common black-box models, achieving at least some explainability requires complex post-processing, while other systems profit from being, to a reasonable extent, inherently interpretable. We propose a rule-based learning system specifically conceptualised and, thus, especially suited for these scenarios. Its models are inherently transparent and easily interpretable by design. One key innovation of our system is that the rules' conditions and which rules compose a problem's solution are evolved separately. We utilise independent rule fitnesses which allows users to specifically tailor their model structure to fit the given requirements for explainability.
Proportional Fairness in Federated Learning
Authors: Guojun Zhang, Saber Malekmohammadi, Xi Chen, YaoLiang Yu
Date: 2022-02-03 00:00:00
Tasks: Fairness, Federated Learning

With the increasingly broad deployment of federated learning (FL) systems in the real world, it is critical but challenging to ensure fairness in FL, i.e. reasonably satisfactory performances for each of the numerous diverse clients. In this work, we introduce and study a new fairness notion in FL, called proportional fairness (PF), which is based on the relative change of each client's performance. From its connection with the bargaining games, we propose PropFair, a novel and easy-to-implement algorithm for finding proportionally fair solutions in FL and study its convergence properties. Through extensive experiments on vision and language datasets, we demonstrate that PropFair can approximately find PF solutions, and it achieves a good balance between the average performances of all clients and of the worst 10% clients. Our code is available at \url{https://github.com/huawei-noah/Federated-Learning/tree/main/FairFL}.
Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints
Authors: Anay Mehrotra, Bary S. R. Pradelski, Nisheeth K. Vishnoi
Date: 2022-02-03 00:00:00

In selection processes such as hiring, promotion, and college admissions, implicit bias toward socially-salient attributes such as race, gender, or sexual orientation of candidates is known to produce persistent inequality and reduce aggregate utility for the decision maker. Interventions such as the Rooney Rule and its generalizations, which require the decision maker to select at least a specified number of individuals from each affected group, have been proposed to mitigate the adverse effects of implicit bias in selection. Recent works have established that such lower-bound constraints can be very effective in improving aggregate utility in the case when each individual belongs to at most one affected group. However, in several settings, individuals may belong to multiple affected groups and, consequently, face more extreme implicit bias due to this intersectionality. We consider independently drawn utilities and show that, in the intersectional case, the aforementioned non-intersectional constraints can only recover part of the total utility achievable in the absence of implicit bias. On the other hand, we show that if one includes appropriate lower-bound constraints on the intersections, almost all the utility achievable in the absence of implicit bias can be recovered. Thus, intersectional constraints can offer a significant advantage over a reductionist dimension-by-dimension non-intersectional approach to reducing inequality.
On the Complexity of Winner Determination and Strategic Control in Conditional Approval Voting
Authors: Evangelos Markakis, Georgios Papasotiropoulos
Date: 2022-02-03 00:00:00
Tasks: Computational Efficiency

We focus on a generalization of the classic Minisum approval voting rule, introduced by Barrot and Lang (2016), and referred to as Conditional Minisum (CMS), for multi-issue elections with preferential dependencies. Under this rule, voters are allowed to declare dependencies between different issues, but the price we have to pay for this higher level of expressiveness is that we end up with a computationally hard rule. Motivated by this, we first focus on finding special cases that admit efficient algorithms for CMS. Our main result in this direction is that we identify the condition of bounded treewidth (of an appropriate graph, emerging from the provided ballots) as the necessary and sufficient condition for exact polynomial algorithms, under common complexity assumptions. We then move to the design of approximation algorithms. For the (still hard) case of binary issues, we identify natural restrictions on the voters' ballots, under which we provide the first multiplicative approximation algorithms for the problem. The restrictions involve upper bounds on the number of dependencies an issue can have on the others and on the number of alternatives per issue that a voter can approve. Finally, we also investigate the complexity of problems related to the strategic control of conditional approval elections by adding or deleting either voters or alternatives and we show that in most variants of these problems, CMS is computationally resistant against control. Overall, we conclude that CMS can be viewed as a solution that achieves a satisfactory tradeoff between expressiveness and computational efficiency, when we have a limited number of dependencies among issues, while at the same time exhibiting sufficient resistance to control.
A Survey of Methods for Automated Algorithm Configuration
Authors: Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
Date: 2022-02-03 00:00:00
Tasks: Survey

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
Authors: Valerio De Caro, Saira Bano, Achilles Machumilane, Alberto Gotta, Pietro Cassará, Antonio Carta, Rudy Semola, Christos Sardianos, Christos Chronis, Iraklis Varlamis, Konstantinos Tserpes, Vincenzo Lomonaco, Claudio Gallicchio, Davide Bacciu
Date: 2022-02-03 00:00:00
Tasks: Autonomous Driving, reinforcement-learning, Reinforcement Learning (RL)

This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving simulation software, CARLA, which allows testing the approach's feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning.
Graph Coloring with Physics-Inspired Graph Neural Networks
Authors: Martin J. A. Schuetz, J. Kyle Brubaker, Zhihuai Zhu, Helmut G. Katzgraber
Date: 2022-02-03 00:00:00
Tasks: Clustering, Community Detection, Node Classification, Scheduling

We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multi-class node classification problem and utilize an unsupervised training strategy based on the statistical physics Potts model. Generalizations to other multi-class problems such as community detection, data clustering, and the minimum clique cover problem are straightforward. We provide numerical benchmark results and illustrate our approach with an end-to-end application for a real-world scheduling use case within a comprehensive encode-process-decode framework. Our optimization approach performs on par or outperforms existing solvers, with the ability to scale to problems with millions of variables.