pwc-archive/papers-with-abstracts huggingface dataset:
Assessing Regulatory Risk in Personal Financial Advice Documents: a Pilot Study
Authors: Wanita Sherchan, Simon Harris, Sue Ann Chen, Nebula Alam, Khoi-Nguyen Tran, Adam J. Makarucha, Christopher J. Butler
Date: 2019-10-11 00:00:00

Assessing regulatory compliance of personal financial advice is currently a complex manual process. In Australia, only 5%- 15% of advice documents are audited annually and 75% of these are found to be non-compliant(ASI 2018b). This paper describes a pilot with an Australian government regulation agency where Artificial Intelligence (AI) models based on techniques such natural language processing (NLP), machine learning and deep learning were developed to methodically characterise the regulatory risk status of personal financial advice documents. The solution provides traffic light rating of advice documents for various risk factors enabling comprehensive coverage of documents in the review and allowing rapid identification of documents that are at high risk of non-compliance with government regulations. This pilot serves as a case study of public-private partnership in developing AI systems for government and public sector.
Mammographic density: Comparison of visual assessment with fully automatic calculation on a multivendor dataset
Authors: Daniela Sacchetto, Lia Morra, Silvano Agliozzo, Daniela Bernardi, Tomas Bjorklund, Beniamino Brancato, Patrizia Bravetti, Luca A. Carbonaro, Loredana Correale, Carmen Fantò, Elisabetta Favettini, Laura Martincich, Luisella Milanesio, Sara Mombelloni, Francesco Monetti, Doralba Morrone, Marco Pellegrini, Barbara Pesce, Antonella Petrillo, Gianni Saguatti, Carmen Stevanin, Rubina M. Trimboli, Paola Tuttobene, Marvi Valentini, Vincenzo Marra, Alfonso Frigerio, Alberto Bert, Francesco Sardanelli
Date: 2018-11-13 00:00:00
Tasks: Binary Classification, Classification, Diagnostic, General Classification

Objectives: To compare breast density (BD) assessment provided by an automated BD evaluator (ABDE) with that provided by a panel of experienced breast radiologists, on a multivendor dataset. Methods: Twenty-one radiologists assessed 613 screening/diagnostic digital mammograms from 9 centers and 6 different vendors, using the BI-RADS a, b, c, and d density classification. The same mammograms were also evaluated by an ABDE providing the ratio between fibroglandular and total breast area on a continuous scale and, automatically, the BI-RADS score. Panel majority report (PMR) was used as reference standard. Agreement (k) and accuracy (proportion of cases correctly classified) were calculated for binary (BI-RADS a-b versus c-d) and 4-class classification. Results: While the agreement of individual radiologists with PMR ranged from k=0.483 to k=0.885, the ABDE correctly classified 563/613 mammograms (92%). A substantial agreement for binary classification was found for individual reader pairs (k=0.620, standard deviation [SD]=0.140), individual versus PMR (k=0.736, SD=0.117), and individual versus ABDE (k=0.674, SD=0.095). Agreement between ABDE and PMR was almost perfect (k=0.831). Conclusions: The ABDE showed an almost perfect agreement with a 21-radiologist panel in binary BD classification on a multivendor dataset, earning a chance as a reproducible alternative to visual evaluation.
A Semi-Automated Approach for Information Extraction, Classification and Analysis of Unstructured Data
Authors: Alberto Purpura, Marco Calaresu
Date: 2019-10-20 00:00:00
Tasks: Data Visualization, General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

In this paper, we show how Quantitative Narrative Analysis and simple Natural Language Processing techniques apply to the extraction and categorization of data in a sample case study of the Diary of the former President of the Italian Republic (PoR), Giorgio Napolitano. The Diary contains a record of all his institutional meetings. This information, if properly handled, allows for an analysis of how the PoR used his so-called soft-powers to influence the Italian political system during his first mandate. In this paper, we propose a way to use simple, yet very effective, Natural Language Processing techniques - such as Regular Expressions and Named Entity Recognition - to extract information from the Diary. Then, we propose an innovative way to organize the extracted data relying on the methodological framework of Quantitative Narrative Analysis. Finally, we show how to analyze the structured data under different levels of detail using PC-ACE (Program for Computer-Assisted Coding of Events), a software developed specifically for this task and for data visualization.
Trouble with the Curve: Predicting Future MLB Players Using Scouting Reports
Authors: Jacob Danovitch
Date: 2019-10-21 00:00:00
Tasks: Articles

In baseball, a scouting report profiles a player's characteristics and traits, usually intended for use in player valuation. This work presents a first-of-its-kind dataset of almost 10,000 scouting reports for minor league, international, and draft prospects. Compiled from articles posted to MLB.com and Fangraphs.com, each report consists of a written description of the player, numerical grades for several skills, and unique IDs to reference their profiles on popular resources like MLB.com, FanGraphs, and Baseball-Reference. With this dataset, we employ several deep neural networks to predict if minor league players will make the MLB given their scouting report. We open-source this data to share with the community, and present a web application demonstrating language variations in the reports of successful and unsuccessful prospects.
BUT System Description to VoxCeleb Speaker Recognition Challenge 2019
Authors: Hossein Zeinali, Shuai Wang, Anna Silnova, Pavel Matějka, Oldřich Plchot
Date: 2019-10-16 00:00:00
Tasks: Speaker Recognition

In this report, we describe the submission of Brno University of Technology (BUT) team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2019. We also provide a brief analysis of different systems on VoxCeleb-1 test sets. Submitted systems for both Fixed and Open conditions are a fusion of 4 Convolutional Neural Network (CNN) topologies. The first and second networks have ResNet34 topology and use two-dimensional CNNs. The last two networks are one-dimensional CNN and are based on the x-vector extraction topology. Some of the networks are fine-tuned using additive margin angular softmax. Kaldi FBanks and Kaldi PLPs were used as features. The difference between Fixed and Open systems lies in the used training data and fusion strategy. The best systems for Fixed and Open conditions achieved 1.42% and 1.26% ERR on the challenge evaluation set respectively.
Semantic Modelling with Long-Short-Term Memory for Information Retrieval
Authors: H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, R. Ward
Date: 2014-12-20 00:00:00
Tasks: Information Retrieval, Retrieval

In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods that use bag of words representation for queries and documents, we treat them as a sequence of words and use long short term memory (LSTM) to capture contextual dependencies. To the best of our knowledge, this is the first time that LSTM is applied to information retrieval tasks. Unlike training traditional LSTMs, the training strategy is different due to the special nature of information retrieval problem. Experimental evaluation on an IR task derived from the Bing web search demonstrates the ability of the proposed method in addressing both lexical mismatch and long-term context modelling issues, thereby, significantly outperforming existing state of the art methods for web document retrieval task.
Ezhil: A Tamil Programming Language
Authors: Muthiah Annamalai
Date: 2009-07-28 00:00:00

Ezhil is a Tamil language based interpreted procedural programming language. Tamil keywords and grammar are chosen to make the native Tamil speaker write programs in the Ezhil system. Ezhil allows easy representation of computer program closer to the Tamil language logical constructs equivalent to the conditional, branch and loop statements in modern English based programming languages. Ezhil is a compact programming language aimed towards Tamil speaking novice computer users. Grammar for Ezhil and a few example programs are reported here, from the initial proof-of-concept implementation using the Python programming language1. To the best of our knowledge, Ezhil language is the first freely available Tamil programming language.
Improved Differentially Private Decentralized Source Separation for fMRI Data
Authors: Hafiz Imtiaz, Jafar Mohammadi, Rogers Silva, Bradley Baker, Sergey M. Plis, Anand D. Sarwate, Vince Calhoun
Date: 2019-10-28 00:00:00
Tasks: blind source separation

Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. In order to leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at a single site. In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting. Conventional approaches to decentralized differentially private algorithms may introduce too much noise due to the typically small sample sizes at each site. We propose a novel protocol that uses correlated noise to remedy this problem. We show that our algorithm outperforms existing approaches on synthetic and real neuroimaging datasets and demonstrate that it can sometimes reach the same level of utility as the corresponding non-private algorithm. This indicates that it is possible to have meaningful utility while preserving privacy.
Harnessing the power of Topological Data Analysis to detect change points in time series
Authors: Umar Islambekov, Monisha Yuvaraj, Yulia R. Gel
Date: 2019-10-28 00:00:00
Tasks: Change Point Detection, Time Series, Time Series Analysis, Topological Data Analysis

We introduce a novel geometry-oriented methodology, based on the emerging tools of topological data analysis, into the change point detection framework. The key rationale is that change points are likely to be associated with changes in geometry behind the data generating process. While the applications of topological data analysis to change point detection are potentially very broad, in this paper we primarily focus on integrating topological concepts with the existing nonparametric methods for change point detection. In particular, the proposed new geometry-oriented approach aims to enhance detection accuracy of distributional regime shift locations. Our simulation studies suggest that integration of topological data analysis with some existing algorithms for change point detection leads to consistently more accurate detection results. We illustrate our new methodology in application to the two closely related environmental time series datasets -ice phenology of the Lake Baikal and the North Atlantic Oscillation indices, in a research query for a possible association between their estimated regime shift locations.
Fundamental Limits of Deep Graph Convolutional Networks
Authors: Abram Magner, Mayank Baranwal, Alfred O. Hero III
Date: 2019-10-28 00:00:00
Tasks: Graph Classification, Graph Representation Learning, Representation Learning

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate the capabilities and limitations of GCNs, we investigate their power, as a function of their number of layers, to distinguish between different random graph models (corresponding to different class-conditional distributions in a classification problem) on the basis of the embeddings of their sample graphs. In particular, the graph models that we consider arise from graphons, which are the most general possible parameterizations of infinite exchangeable graph models and which are the central objects of study in the theory of dense graph limits. We give a precise characterization of the set of pairs of graphons that are indistinguishable by a GCN with nonlinear activation functions coming from a certain broad class if its depth is at least logarithmic in the size of the sample graph. This characterization is in terms of a degree profile closeness property. Outside this class, a very simple GCN architecture suffices for distinguishability. We then exhibit a concrete, infinite class of graphons arising from stochastic block models that are well-separated in terms of cut distance and are indistinguishable by a GCN. These results theoretically match empirical observations of several prior works. To prove our results, we exploit a connection to random walks on graphs. Finally, we give empirical results on synthetic and real graph classification datasets, indicating that indistinguishable graph distributions arise in practice.
Ensemble Quantile Classifier
Authors: Yuanhao Lai, Ian McLeod
Date: 2019-10-28 00:00:00
Tasks: Text Categorization

Both the median-based classifier and the quantile-based classifier are useful for discriminating high-dimensional data with heavy-tailed or skewed inputs. But these methods are restricted as they assign equal weight to each variable in an unregularized way. The ensemble quantile classifier is a more flexible regularized classifier that provides better performance with high-dimensional data, asymmetric data or when there are many irrelevant extraneous inputs. The improved performance is demonstrated by a simulation study as well as an application to text categorization. It is proven that the estimated parameters of the ensemble quantile classifier consistently estimate the minimal population loss under suitable general model assumptions. It is also shown that the ensemble quantile classifier is Bayes optimal under suitable assumptions with asymmetric Laplace distribution inputs.
Poisson-Randomized Gamma Dynamical Systems
Authors: Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna Wallach
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Inductive Bias

This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and burstiness. The PRGDS is based on a new motif in Bayesian latent variable modeling, an alternating chain of discrete Poisson and continuous gamma latent states that is analytically convenient and computationally tractable. This motif yields closed-form complete conditionals for all variables by way of the Bessel distribution and a novel discrete distribution that we call the shifted confluent hypergeometric distribution. We draw connections to closely related models and compare the PRGDS to these models in studies of real-world count data sets of text, international events, and neural spike trains. We find that a sparse variant of the PRGDS, which allows the continuous gamma latent states to take values of exactly zero, often obtains better predictive performance than other models and is uniquely capable of inferring latent structures that are highly localized in time.
Bayesian Optimization with Unknown Search Space
Authors: Huong Ha, Santu Rana, Sunil Gupta, Thanh Nguyen, Hung Tran-The, Svetha Venkatesh
Date: 2019-10-29 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Bayesian Optimization

Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Bayesian optimization. We devise a strategy to guarantee that in iterative expansions of the search space, our method can find a point whose function value within epsilon of the objective function maximum. Without the need to specify any parameters, our algorithm automatically triggers a minimal expansion required iteratively. We derive analytic expressions for when to trigger the expansion and by how much to expand. We also provide theoretical analysis to show that our method achieves epsilon-accuracy after a finite number of iterations. We demonstrate our method on both benchmark test functions and machine learning hyper-parameter tuning tasks and demonstrate that our method outperforms baselines.
Minimal Variance Sampling in Stochastic Gradient Boosting
Authors: Bulat Ibragimov, Gleb Gusev
Date: 2019-10-29 00:00:00
Proceeding: NeurIPS 2019 12

Stochastic Gradient Boosting (SGB) is a widely used approach to regularization of boosting models based on decision trees. It was shown that, in many cases, random sampling at each iteration can lead to better generalization performance of the model and can also decrease the learning time. Different sampling approaches were proposed, where probabilities are not uniform, and it is not currently clear which approach is the most effective. In this paper, we formulate the problem of randomization in SGB in terms of optimization of sampling probabilities to maximize the estimation accuracy of split scoring used to train decision trees. This optimization problem has a closed-form nearly optimal solution, and it leads to a new sampling technique, which we call Minimal Variance Sampling (MVS). The method both decreases the number of examples needed for each iteration of boosting and increases the quality of the model significantly as compared to the state-of-the art sampling methods. The superiority of the algorithm was confirmed by introducing MVS as a new default option for subsampling in CatBoost, a gradient boosting library achieving state-of-the-art quality on various machine learning tasks.
Neural Density Estimation and Likelihood-free Inference
Authors: George Papamakarios
Date: 2019-10-29 00:00:00
Tasks: BIG-bench Machine Learning, Density Estimation

I consider two problems in machine learning and statistics: the problem of estimating the joint probability density of a collection of random variables, known as density estimation, and the problem of inferring model parameters when their likelihood is intractable, known as likelihood-free inference. The contribution of the thesis is a set of new methods for addressing these problems that are based on recent advances in neural networks and deep learning.
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Authors: Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
Date: 2019-10-29 00:00:00
Proceeding: ICML 2020 1
Tasks: Probabilistic Programming

Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models. They further attempt to automate the process of drawing inferences from these models, but doing this successfully is severely hampered by the wide range of non--standard models they can express. As a result, although one can specify complex models in a universal PPS, the provided inference engines often fall far short of what is required. In particular, we show that they produce surprisingly unsatisfactory performance for models where the support varies between executions, often doing no better than importance sampling from the prior. To address this, we introduce a new inference framework: Divide, Conquer, and Combine, which remains efficient for such models, and show how it can be implemented as an automated and generic PPS inference engine. We empirically demonstrate substantial performance improvements over existing approaches on three examples.
How Much Can We See? A Note on Quantifying Explainability of Machine Learning Models
Authors: Gero Szepannek
Date: 2019-10-29 00:00:00
Tasks: BIG-bench Machine Learning

One of the most popular approaches to understanding feature effects of modern black box machine learning models are partial dependence plots (PDP). These plots are easy to understand but only able to visualize low order dependencies. The paper is about the question 'How much can we see?': A framework is developed to quantify the explainability of arbitrary machine learning models, i.e. up to what degree the visualization as given by a PDP is able to explain the predictions of the model. The result allows for a judgement whether an attempt to explain a black box model is sufficient or not.
Learning Sparse Distributions using Iterative Hard Thresholding
Authors: Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
Date: 2019-10-29 00:00:00
Proceeding: NeurIPS 2019 12

Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such as sparse inference. In this work, we consider IHT as a solution to the problem of learning sparse discrete distributions. We study the hardness of using IHT on the space of measures. As a practical alternative, we propose a greedy approximate projection which simultaneously captures appropriate notions of sparsity in distributions, while satisfying the simplex constraint, and investigate the convergence behavior of the resulting procedure in various settings. Our results show, both in theory and practice, that IHT can achieve state of the art results for learning sparse distributions.
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
Authors: Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Date: 2019-10-29 00:00:00
Tasks: LEMMA

Stein's method (Stein, 1973; 1981) is a powerful tool for statistical applications and has significantly impacted machine learning. Stein's lemma plays an essential role in Stein's method. Previous applications of Stein's lemma either required strong technical assumptions or were limited to Gaussian distributions with restricted covariance structures. In this work, we extend Stein's lemma to exponential-family mixture distributions, including Gaussian distributions with full covariance structures. Our generalization enables us to establish a connection between Stein's lemma and the reparameterization trick to derive gradients of expectations of a large class of functions under weak assumptions. Using this connection, we can derive many new reparameterizable gradient identities that go beyond the reach of existing works. For example, we give gradient identities when the expectation is taken with respect to Student's t-distribution, skew Gaussian, exponentially modified Gaussian, and normal inverse Gaussian.
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing
Authors: Diyan Teng, Rashmi Kulkarni, Justin McGloin
Date: 2019-10-29 00:00:00
Tasks: Activity Recognition, Weakly-supervised Learning

Always-on sensing of mobile device user's contextual information is critical to many intelligent use cases nowadays such as healthcare, drive assistance, voice UI. State-of-the-art approaches for predicting user context have proved the value to leverage multiple sensing modalities for better accuracy. However, those context inference algorithms that run on application processor nowadays tend to drain heavy amount of power, making them not suitable for an always-on implementation. We claim that not every sensing modality is suitable to be activated all the time and it remains challenging to build an inference engine using power friendly sensing modalities. Meanwhile, due to the diverse population, we find it challenging to learn a context inference model that generalizes well, with limited training data, especially when only using always-on low power sensors. In this work, we propose an approach to leverage the opportunistically-on counterparts in device to improve the always-on prediction model, leading to a personalized solution. We model this problem using a weakly supervised learning framework and provide both theoretical and experimental results to validate our design. The proposed framework achieves satisfying result in the IMU based activity recognition application we considered.