pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Merge Conflicts in Collaborative Software Development
Authors: Moein Owhadi-Kareshk, Sarah Nadi, Julia Rubin
Date: 2019-07-14 00:00:00

Background. During collaborative software development, developers often use branches to add features or fix bugs. When merging changes from two branches, conflicts may occur if the changes are inconsistent. Developers need to resolve these conflicts before completing the merge, which is an error-prone and time-consuming process. Early detection of merge conflicts, which warns developers about resolving conflicts before they become large and complicated, is among the ways of dealing with this problem. Existing techniques do this by continuously pulling and merging all combinations of branches in the background to notify developers as soon as a conflict occurs, which is a computationally expensive process. One potential way for reducing this cost is to use a machine-learning based conflict predictor that filters out the merge scenarios that are not likely to have conflicts, ie safe merge scenarios. Aims. In this paper, we assess if conflict prediction is feasible. Method. We design a classifier for predicting merge conflicts, based on 9 light-weight Git feature sets. To evaluate our predictor, we perform a large-scale study on 267, 657 merge scenarios from 744 GitHub repositories in seven programming languages. Results. Our results show that we achieve high f1-scores, varying from 0.95 to 0.97 for different programming languages, when predicting safe merge scenarios. The f1-score is between 0.57 and 0.68 for the conflicting merge scenarios. Conclusions. Predicting merge conflicts is feasible in practice, especially in the context of predicting safe merge scenarios as a pre-filtering step for speculative merging.
Counterfactual Reasoning for Fair Clinical Risk Prediction
Authors: Stephen Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah
Date: 2019-07-14 00:00:00
Tasks: Attribute, counterfactual, Counterfactual Inference, Counterfactual Reasoning, Decision Making, Fairness, Prediction

The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for historically underrepresented and mistreated groups, due to biases implicitly embedded in observational data in electronic health records. To address this problem in the context of clinical risk prediction models, we develop an augmented counterfactual fairness criteria to extend the group fairness criteria of equalized odds to an individual level. We do so by requiring that the same prediction be made for a patient, and a counterfactual patient resulting from changing a sensitive attribute, if the factual and counterfactual outcomes do not differ. We investigate the extent to which the augmented counterfactual fairness criteria may be applied to develop fair models for prolonged inpatient length of stay and mortality with observational electronic health records data. As the fairness criteria is ill-defined without knowledge of the data generating process, we use a variational autoencoder to perform counterfactual inference in the context of an assumed causal graph. While our technique provides a means to trade off maintenance of fairness with reduction in predictive performance in the context of a learned generative model, further work is needed to assess the generality of this approach.
Improving classification performance by feature space transformations and model selection
Authors: Jose Ortiz-Bejar, Eric S. Tellez, Mario Graff
Date: 2019-07-14 00:00:00
Tasks: Classification, General Classification, Model Selection, Prototype Selection

Improving the performance of classifiers is the realm of feature mapping, prototype selection, and kernel function transformations; these techniques aim for reducing the complexity, and also, improving the accuracy of models. In particular, our objective is to combine them to transform data's shape into another more convenient distribution; such that some simple algorithms, such as Na\"ive Bayes or k-Nearest Neighbors, can produce competitive classifiers. In this paper, we introduce a family of classifiers based on feature mapping and kernel functions, orchestrated by a model selection scheme that excels in performance. We provide an extensive experimental comparison of our methods with sixteen popular classifiers on more than thirty benchmarks supporting our claims. In addition to their competitive performance, our statistical tests also found that our methods are different among them, supporting our claim of a compelling family of classifiers.
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
Authors: Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu
Date: 2019-07-14 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Binary Classification, Computational Efficiency, Weakly-supervised Learning

We consider the weakly supervised binary classification problem where the labels are randomly flipped with probability $1- {\alpha}$. Although there exist numerous algorithms for this problem, it remains theoretically unexplored how the statistical accuracies and computational efficiency of these algorithms depend on the degree of supervision, which is quantified by ${\alpha}$. In this paper, we characterize the effect of ${\alpha}$ by establishing the information-theoretic and computational boundaries, namely, the minimax-optimal statistical accuracy that can be achieved by all algorithms, and polynomial-time algorithms under an oracle computational model. For small ${\alpha}$, our result shows a gap between these two boundaries, which represents the computational price of achieving the information-theoretic boundary due to the lack of supervision. Interestingly, we also show that this gap narrows as ${\alpha}$ increases. In other words, having more supervision, i.e., more correct labels, not only improves the optimal statistical accuracy as expected, but also enhances the computational efficiency for achieving such accuracy.
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
Authors: Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka
Date: 2019-07-14 00:00:00
Tasks: Bayesian Inference, Probabilistic Programming, Time Series, Time Series Analysis

We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that capture key properties of a broad class of data generating processes, using Bayesian inference to synthesize probabilistic programs in these modeling languages given observed data. We provide a precise formulation of Bayesian synthesis for automatic data modeling that identifies sufficient conditions for the resulting synthesis procedure to be sound. We also derive a general class of synthesis algorithms for domain-specific languages specified by probabilistic context-free grammars and establish the soundness of our approach for these languages. We apply the techniques to automatically synthesize probabilistic programs for time series data and multivariate tabular data. We show how to analyze the structure of the synthesized programs to compute, for key qualitative properties of interest, the probability that the underlying data generating process exhibits each of these properties. Second, we translate probabilistic programs in the domain-specific language into probabilistic programs in Venture, a general-purpose probabilistic programming system. The translated Venture programs are then executed to obtain predictions of new time series data and new multivariate data records. Experimental results show that our techniques can accurately infer qualitative structure in multiple real-world data sets and outperform standard data analysis methods in forecasting and predicting new data.
State Estimation in Visual Inertial Autonomous Helicopter Landing Using Optimisation on Manifold
Authors: Thinh Hoang Dinh, Hieu Le Thi Hong, Tri Ngo Dinh
Date: 2019-07-14 00:00:00
Tasks: Position, State Estimation

Autonomous helicopter landing is a challenging task that requires precise information about the aircraft states regarding the helicopters position, attitude, as well as position of the helipad. To this end, we propose a solution that fuses data from an Inertial Measurement Unit (IMU) and a monocular camera which is capable of detecting helipads position in the image plane. The algorithm utilises manifold based nonlinear optimisation over preintegrated IMU measurements and reprojection error in temporally uniformly distributed keyframes, exhibiting good performance in terms of accuracy and being computationally feasible. Our contributions of this paper are the formal address of the landmarks Jacobian expressions and the adaptation of equality constrained Gauss-Newton method to this specific problem. Numerical simulations on MATLAB/Simulink confirm the validity of given claims.
On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
Authors: Zhuoran Yang, Yongxin Chen, Mingyi Hong, Zhaoran Wang
Date: 2019-07-14 00:00:00
Tasks: Bilevel Optimization, Reinforcement Learning

Despite the empirical success of the actor-critic algorithm, its theoretical understanding lags behind. In a broader context, actor-critic can be viewed as an online alternating update algorithm for bilevel optimization, whose convergence is known to be fragile. To understand the instability of actor-critic, we focus on its application to linear quadratic regulators, a simple yet fundamental setting of reinforcement learning. We establish a nonasymptotic convergence analysis of actor-critic in this setting. In particular, we prove that actor-critic finds a globally optimal pair of actor (policy) and critic (action-value function) at a linear rate of convergence. Our analysis may serve as a preliminary step towards a complete theoretical understanding of bilevel optimization with nonconvex subproblems, which is NP-hard in the worst case and is often solved using heuristics.
Lexical Simplification with Pretrained Encoders
Authors: Jipeng Qiang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu
Date: 2019-07-14 00:00:00
Tasks: Language Modelling, Lexical Simplification, Sentence

Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple LS approach that makes use of the Bidirectional Encoder Representations from Transformers (BERT) which can consider both the given sentence and the complex word during generating candidate substitutions for the complex word. Specifically, we mask the complex word of the original sentence for feeding into the BERT to predict the masked token. The predicted results will be used as candidate substitutions. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement compared with these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 12 Accuracy points on three well-known benchmarks.
Task Selection Policies for Multitask Learning
Authors: John Glover, Chris Hokamp
Date: 2019-07-14 00:00:00
Tasks: counterfactual, Natural Language Understanding, Off-policy evaluation

One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task. We refer to any formalized approach to addressing this problem (learned or otherwise) as a task selection policy. In this work we provide an empirical evaluation of the performance of some common task selection policies in a synthetic bandit-style setting, as well as on the GLUE benchmark for natural language understanding. We connect task selection policy learning to existing work on automated curriculum learning and off-policy evaluation, and suggest a method based on counterfactual estimation that leads to improved model performance in our experimental settings.
Simple Automatic Post-editing for Arabic-Japanese Machine Translation
Authors: Ella Noll, Mai Oudah, Nizar Habash
Date: 2019-07-14 00:00:00
Tasks: Articles, Automatic Post-Editing, Machine Translation, Translation

A common bottleneck for developing machine translation (MT) systems for some language pairs is the lack of direct parallel translation data sets, in general and in certain domains. Alternative solutions such as zero-shot models or pivoting techniques are successful in getting a strong baseline, but are often below the more supported language-pair systems. In this paper, we focus on Arabic-Japanese machine translation, a less studied language pair; and we work with a unique parallel corpus of Arabic news articles that were manually translated to Japanese. We use this parallel corpus to adapt a state-of-the-art domain/genre agnostic neural MT system via a simple automatic post-editing technique. Our results and detailed analysis suggest that this approach is quite viable for less supported language pairs in specific domains.
Unsupervised Automatic Building Extraction Using Active Contour Model on Unregistered Optical Imagery and Airborne LiDAR Data
Authors: Thanh Huy Nguyen, Sylvie Daniel, Didier Gueriot, Christophe Sintes, Jean-Marc Le Caillec
Date: 2019-07-14 00:00:00

Automatic extraction of buildings in urban scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly with the emergence of LiDAR systems since mid-1990s. However, in reality, this task is still very challenging due to the complexity of building size and shapes, as well as its surrounding environment. Active contour model, colloquially called snake model, which has been extensively used in many applications in computer vision and image processing, is also applied to extract buildings from aerial/satellite imagery. Motivated by the limitations of existing snake models addressing to the building extraction, this paper presents an unsupervised and fully automatic snake model to extract buildings using optical imagery and an unregistered airborne LiDAR dataset, without manual initial points or training data. The proposed method is shown to be capable of extracting buildings with varying color from complex environments, and yielding high overall accuracy.
Automatic Repair and Type Binding of Undeclared Variables using Neural Networks
Authors: Venkatesh Theru Mohan, Ali Jannesari
Date: 2019-07-14 00:00:00
Tasks: Machine Translation, Translation, Vocal Bursts Type Prediction

Deep learning had been used in program analysis for the prediction of hidden software defects using software defect datasets, security vulnerabilities using generative adversarial networks as well as identifying syntax errors by learning a trained neural machine translation on program codes. However, all these approaches either require defect datasets or bug-free source codes that are executable for training the deep learning model. Our neural network model is neither trained with any defect datasets nor bug-free programming source codes, instead it is trained using structural semantic details of Abstract Syntax Tree (AST) where each node represents a construct appearing in the source code. This model is implemented to fix one of the most common semantic errors, such as undeclared variable errors as well as infer their type information before program compilation. By this approach, the model has achieved in correctly locating and identifying 81% of the programs on prutor dataset of 1059 programs with only undeclared variable errors and also inferring their types correctly in 80% of the programs.
On the Role of Time in Learning
Authors: Alessandro Betti, Marco Gori
Date: 2019-07-14 00:00:00

By and large the process of learning concepts that are embedded in time is regarded as quite a mature research topic. Hidden Markov models, recurrent neural networks are, amongst others, successful approaches to learning from temporal data. In this paper, we claim that the dominant approach minimizing appropriate risk functions defined over time by classic stochastic gradient might miss the deep interpretation of time given in other fields like physics. We show that a recent reformulation of learning according to the principle of Least Cognitive Action is better suited whenever time is involved in learning. The principle gives rise to a learning process that is driven by differential equations, that can somehow descrive the process within the same framework as other laws of nature.
A Divide-and-Conquer Approach towards Understanding Deep Networks
Authors: Weilin Fu, Katharina Breininger, Roman Schaffert, Nishant Ravikumar, Andreas Maier
Date: 2019-07-14 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Retinal Vessel Segmentation, Semantic Segmentation

Deep neural networks have achieved tremendous success in various fields including medical image segmentation. However, they have long been criticized for being a black-box, in that interpretation, understanding and correcting architectures is difficult as there is no general theory for deep neural network design. Previously, precision learning was proposed to fuse deep architectures and traditional approaches. Deep networks constructed in this way benefit from the original known operator, have fewer parameters, and improved interpretability. However, they do not yield state-of-the-art performance in all applications. In this paper, we propose to analyze deep networks using known operators, by adopting a divide-and-conquer strategy to replace network components, whilst retaining its performance. The task of retinal vessel segmentation is investigated for this purpose. We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators. The results indicate that a combination of a trainable guided filter and a trainable version of the Frangi filter yields a performance at the level of U-Net (AUC 0.974 vs. 0.972) with a tremendous reduction in parameters (111,536 vs. 9,575). In addition, the trained layers can be mapped back into their original algorithmic interpretation and analyzed using standard tools of signal processing.
Towards Generation of Visual Attention Map for Source Code
Authors: Takeshi D. Itoh, Takatomi Kubo, Kiyoka Ikeda, Yuki Maruno, Yoshiharu Ikutani, Hideaki Hata, Kenichi Matsumoto, Kazushi Ikeda
Date: 2019-07-14 00:00:00

Program comprehension is a dominant process in software development and maintenance. Experts are considered to comprehend the source code efficiently by directing their gaze, or attention, to important components in it. However, reflecting the importance of components is still a remaining issue in gaze behavior analysis for source code comprehension. Here we show a conceptual framework to compare the quantified importance of source code components with the gaze behavior of programmers. We use "attention" in attention models (e.g., code2vec) as the importance indices for source code components and evaluate programmers' gaze locations based on the quantified importance. In this report, we introduce the idea of our gaze behavior analysis using the attention map, and the results of a preliminary experiment.
The FAST Algorithm for Submodular Maximization
Authors: Adam Breuer, Eric Balkanski, Yaron Singer
Date: 2019-07-14 00:00:00
Proceeding: ICML 2020 1

In this paper we describe a new algorithm called Fast Adaptive Sequencing Technique (FAST) for maximizing a monotone submodular function under a cardinality constraint $k$ whose approximation ratio is arbitrarily close to $1-1/e$, is $O(\log(n) \log^2(\log k))$ adaptive, and uses a total of $O(n \log\log(k))$ queries. Recent algorithms have comparable guarantees in terms of asymptotic worst case analysis, but their actual number of rounds and query complexity depend on very large constants and polynomials in terms of precision and confidence, making them impractical for large data sets. Our main contribution is a design that is extremely efficient both in terms of its non-asymptotic worst case query complexity and number of rounds, and in terms of its practical runtime. We show that this algorithm outperforms any algorithm for submodular maximization we are aware of, including hyper-optimized parallel versions of state-of-the-art serial algorithms, by running experiments on large data sets. These experiments show FAST is orders of magnitude faster than the state-of-the-art.
Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation
Authors: Marcin Junczys-Dowmunt
Date: 2019-07-14 00:00:00
Proceeding: WS 2019 8
Tasks: Data Augmentation, Machine Translation, Sentence, Translation

This paper describes the Microsoft Translator submissions to the WMT19 news translation shared task for English-German. Our main focus is document-level neural machine translation with deep transformer models. We start with strong sentence-level baselines, trained on large-scale data created via data-filtering and noisy back-translation and find that back-translation seems to mainly help with translationese input. We explore fine-tuning techniques, deeper models and different ensembling strategies to counter these effects. Using document boundaries present in the authentic and synthetic parallel data, we create sequences of up to 1000 subword segments and train transformer translation models. We experiment with data augmentation techniques for the smaller authentic data with document-boundaries and for larger authentic data without boundaries. We further explore multi-task training for the incorporation of document-level source language monolingual data via the BERT-objective on the encoder and two-pass decoding for combinations of sentence-level and document-level systems. Based on preliminary human evaluation results, evaluators strongly prefer the document-level systems over our comparable sentence-level system. The document-level systems also seem to score higher than the human references in source-based direct assessment.
FoodX-251: A Dataset for Fine-grained Food Classification
Authors: Parneet Kaur, Karan Sikka, Weijun Wang, Serge Belongie, Ajay Divakaran
Date: 2019-07-14 00:00:00
Tasks: Classification, Fine-Grained Visual Categorization, General Classification

Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models. Solving this problem will require advances in both computer vision models as well as datasets for evaluating these models. In this paper we focus on the second aspect and introduce FoodX-251, a dataset of 251 fine-grained food categories with 158k images collected from the web. We use 118k images as a training set and provide human verified labels for 40k images that can be used for validation and testing. In this work, we outline the procedure of creating this dataset and provide relevant baselines with deep learning models. The FoodX-251 dataset has been used for organizing iFood-2019 challenge in the Fine-Grained Visual Categorization workshop (FGVC6 at CVPR 2019) and is available for download.