Paper Group ANR 1086
Factored Bandits. Learning to Address Health Inequality in the United States with a Bayesian Decision Network. Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation. Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning. Upgrading from …
Factored Bandits
| Title | Factored Bandits |
| Authors | Julian Zimmert, Yevgeny Seldin |
| Abstract | We introduce the factored bandits model, which is a framework for learning with limited (bandit) feedback, where actions can be decomposed into a Cartesian product of atomic actions. Factored bandits incorporate rank-1 bandits as a special case, but significantly relax the assumptions on the form of the reward function. We provide an anytime algorithm for stochastic factored bandits and up to constants matching upper and lower regret bounds for the problem. Furthermore, we show that with a slight modification the proposed algorithm can be applied to utility based dueling bandits. We obtain an improvement in the additive terms of the regret bound compared to state of the art algorithms (the additive terms are dominating up to time horizons which are exponential in the number of arms). |
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| Published | 2018-07-04 |
| URL | http://arxiv.org/abs/1807.01488v2 |
| http://arxiv.org/pdf/1807.01488v2.pdf | |
| PWC | https://paperswithcode.com/paper/factored-bandits |
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Learning to Address Health Inequality in the United States with a Bayesian Decision Network
| Title | Learning to Address Health Inequality in the United States with a Bayesian Decision Network |
| Authors | Tavpritesh Sethi, Anant Mittal, Shubham Maheshwari, Samarth Chugh |
| Abstract | Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. There is a growing concern about a further widening of healthcare inequality caused by Artificial Intelligence (AI) due to differential access to AI-driven services. Hence, it is imperative to explore and exploit the potential of AI for illuminating biases and enabling transparent policy decisions for positive social and health impact. In this work, we reveal actionable interventions for decreasing the longevity-gap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features. We learn an ensemble-averaged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the impact of diversity, preventive-care quality and stable-families within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our reported findings and to explore the impact of ones beyond reported in this work. |
| Tasks | |
| Published | 2018-09-18 |
| URL | http://arxiv.org/abs/1809.09215v2 |
| http://arxiv.org/pdf/1809.09215v2.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-address-health-inequality-in-the |
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Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation
| Title | Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation |
| Authors | Anwar Irmatov, Elnura Irmatova |
| Abstract | In 2018, at the World Economic Forum in Davos it was presented a new countries’ economic performance metric named the Inclusive Development Index (IDI) composed of 12 indicators. The new metric implies that countries might need to realize structural reforms for improving both economic expansion and social inclusion performance. That is why, it is vital for the IDI calculation method to have strong statistical and mathematical basis, so that results are accurate and transparent for public purposes. In the current work, we propose a novel approach for the IDI estimation - the Ranking Relative Principal Component Attributes Network Model (REL-PCANet). The model is based on RELARM and RankNet principles and combines elements of PCA, techniques applied in image recognition and learning to rank mechanisms. Also, we define a new approach for estimation of target probabilities matrix to reflect dynamic changes in countries’ inclusive development. Empirical study proved that REL-PCANet ensures reliable and robust scores and rankings, thus is recommended for practical implementation. |
| Tasks | Learning-To-Rank |
| Published | 2018-04-16 |
| URL | http://arxiv.org/abs/1804.06219v1 |
| http://arxiv.org/pdf/1804.06219v1.pdf | |
| PWC | https://paperswithcode.com/paper/application-of-the-ranking-relative-principal |
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Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning
| Title | Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning |
| Authors | Wouter van Loon, Marjolein Fokkema, Botond Szabo, Mark de Rooij |
| Abstract | In biomedical research many different types of patient data can be collected, including various types of omics data and medical imaging modalities. Applying multi-view learning to these different sources of information can increase the accuracy of medical classification models compared with single-view procedures. However, the collection of biomedical data can be expensive and taxing on patients, so that superfluous data collection should be avoided. It is therefore necessary to develop multi-view learning methods which can accurately identify the views most important for prediction. In recent years, several biomedical studies have used an approach known as multi-view stacking (MVS), where a model is trained on each view separately and the resulting predictions are combined through stacking. In these studies, MVS has been shown to increase classification accuracy. However, the MVS framework can also be used for selecting a subset of important views. To study the view selection potential of MVS, we develop a special case called stacked penalized logistic regression (StaPLR). Compared with existing view-selection methods, StaPLR can make use of faster optimization algorithms and is easily parallelized. We show that nonnegativity constraints on the parameters of the function which combines the views are important for preventing unimportant views from entering the model. We investigate the performance of StaPLR through simulations, and consider two real data examples. We compare the performance of StaPLR with an existing view selection method called the group lasso and observe that, in terms of view selection, StaPLR has a consistently lower false positive rate. |
| Tasks | MULTI-VIEW LEARNING |
| Published | 2018-11-06 |
| URL | http://arxiv.org/abs/1811.02316v2 |
| http://arxiv.org/pdf/1811.02316v2.pdf | |
| PWC | https://paperswithcode.com/paper/stacked-penalized-logistic-regression-for |
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Upgrading from Gaussian Processes to Student’s-T Processes
| Title | Upgrading from Gaussian Processes to Student’s-T Processes |
| Authors | Brendan D. Tracey, David H. Wolpert |
| Abstract | Gaussian process priors are commonly used in aerospace design for performing Bayesian optimization. Nonetheless, Gaussian processes suffer two significant drawbacks: outliers are a priori assumed unlikely, and the posterior variance conditioned on observed data depends only on the locations of those data, not the associated sample values. Student’s-T processes are a generalization of Gaussian processes, founded on the Student’s-T distribution instead of the Gaussian distribution. Student’s-T processes maintain the primary advantages of Gaussian processes (kernel function, analytic update rule) with additional benefits beyond Gaussian processes. The Student’s-T distribution has higher Kurtosis than a Gaussian distribution and so outliers are much more likely, and the posterior variance increases or decreases depending on the variance of observed data sample values. Here, we describe Student’s-T processes, and discuss their advantages in the context of aerospace optimization. We show how to construct a Student’s-T process using a kernel function and how to update the process given new samples. We provide a clear derivation of optimization-relevant quantities such as expected improvement, and contrast with the related computations for Gaussian processes. Finally, we compare the performance of Student’s-T processes against Gaussian process on canonical test problems in Bayesian optimization, and apply the Student’s-T process to the optimization of an aerostructural design problem. |
| Tasks | Gaussian Processes |
| Published | 2018-01-18 |
| URL | http://arxiv.org/abs/1801.06147v1 |
| http://arxiv.org/pdf/1801.06147v1.pdf | |
| PWC | https://paperswithcode.com/paper/upgrading-from-gaussian-processes-to-students |
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Robust cross-domain disfluency detection with pattern match networks
| Title | Robust cross-domain disfluency detection with pattern match networks |
| Authors | Vicky Zayats, Mari Ostendorf |
| Abstract | In this paper we introduce a novel pattern match neural network architecture that uses neighbor similarity scores as features, eliminating the need for feature engineering in a disfluency detection task. We evaluate the approach in disfluency detection for four different speech genres, showing that the approach is as effective as hand-engineered pattern match features when used on in-domain data and achieves superior performance in cross-domain scenarios. |
| Tasks | Feature Engineering |
| Published | 2018-11-17 |
| URL | http://arxiv.org/abs/1811.07236v1 |
| http://arxiv.org/pdf/1811.07236v1.pdf | |
| PWC | https://paperswithcode.com/paper/robust-cross-domain-disfluency-detection-with |
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Stochastic Gradient Descent for Stochastic Doubly-Nonconvex Composite Optimization
| Title | Stochastic Gradient Descent for Stochastic Doubly-Nonconvex Composite Optimization |
| Authors | Takayuki Kawashima, Hironori Fujisawa |
| Abstract | The stochastic gradient descent has been widely used for solving composite optimization problems in big data analyses. Many algorithms and convergence properties have been developed. The composite functions were convex primarily and gradually nonconvex composite functions have been adopted to obtain more desirable properties. The convergence properties have been investigated, but only when either of composite functions is nonconvex. There is no convergence property when both composite functions are nonconvex, which is named the \textit{doubly-nonconvex} case.To overcome this difficulty, we assume a simple and weak condition that the penalty function is \textit{quasiconvex} and then we obtain convergence properties for the stochastic doubly-nonconvex composite optimization problem.The convergence rate obtained here is of the same order as the existing work.We deeply analyze the convergence rate with the constant step size and mini-batch size and give the optimal convergence rate with appropriate sizes, which is superior to the existing work. Experimental results illustrate that our method is superior to existing methods. |
| Tasks | |
| Published | 2018-05-21 |
| URL | https://arxiv.org/abs/1805.07960v2 |
| https://arxiv.org/pdf/1805.07960v2.pdf | |
| PWC | https://paperswithcode.com/paper/stochastic-gradient-descent-for-stochastic |
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Analysis on Gradient Propagation in Batch Normalized Residual Networks
| Title | Analysis on Gradient Propagation in Batch Normalized Residual Networks |
| Authors | Abhishek Panigrahi, Yueru Chen, C. -C. Jay Kuo |
| Abstract | We conduct mathematical analysis on the effect of batch normalization (BN) on gradient backpropogation in residual network training, which is believed to play a critical role in addressing the gradient vanishing/explosion problem, in this work. By analyzing the mean and variance behavior of the input and the gradient in the forward and backward passes through the BN and residual branches, respectively, we show that they work together to confine the gradient variance to a certain range across residual blocks in backpropagation. As a result, the gradient vanishing/explosion problem is avoided. We also show the relative importance of batch normalization w.r.t. the residual branches in residual networks. |
| Tasks | |
| Published | 2018-12-02 |
| URL | http://arxiv.org/abs/1812.00342v1 |
| http://arxiv.org/pdf/1812.00342v1.pdf | |
| PWC | https://paperswithcode.com/paper/analysis-on-gradient-propagation-in-batch |
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Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning
| Title | Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning |
| Authors | Tommy Tram, Anton Jansson, Robin Grönberg, Mohammad Ali, Jonas Sjöberg |
| Abstract | This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed along its pre-defined trajectory to pass the crossing efficiently. Deep Q-learning is used on simulated traffic with different predefined driver behaviors and intentions. The results show a policy that is able to cross the intersection avoiding collision with other vehicles 98% of the time, while at the same time not being too passive. Moreover, inferring information over time is important to distinguish between different intentions and is shown by comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a Deep Q-learning at 1.75%. |
| Tasks | Q-Learning |
| Published | 2018-10-24 |
| URL | http://arxiv.org/abs/1810.10469v1 |
| http://arxiv.org/pdf/1810.10469v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-negotiating-behavior-between-cars-in |
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Active Fairness in Algorithmic Decision Making
| Title | Active Fairness in Algorithmic Decision Making |
| Authors | Alejandro Noriega-Campero, Michiel A. Bakker, Bernardo Garcia-Bulle, Alex Pentland |
| Abstract | Society increasingly relies on machine learning models for automated decision making. Yet, efficiency gains from automation have come paired with concern for algorithmic discrimination that can systematize inequality. Recent work has proposed optimal post-processing methods that randomize classification decisions for a fraction of individuals, in order to achieve fairness measures related to parity in errors and calibration. These methods, however, have raised concern due to the information inefficiency, intra-group unfairness, and Pareto sub-optimality they entail. The present work proposes an alternative active framework for fair classification, where, in deployment, a decision-maker adaptively acquires information according to the needs of different groups or individuals, towards balancing disparities in classification performance. We propose two such methods, where information collection is adapted to group- and individual-level needs respectively. We show on real-world datasets that these can achieve: 1) calibration and single error parity (e.g., equal opportunity); and 2) parity in both false positive and false negative rates (i.e., equal odds). Moreover, we show that by leveraging their additional degree of freedom, active approaches can substantially outperform randomization-based classifiers previously considered optimal, while avoiding limitations such as intra-group unfairness. |
| Tasks | Calibration, Decision Making |
| Published | 2018-09-28 |
| URL | http://arxiv.org/abs/1810.00031v2 |
| http://arxiv.org/pdf/1810.00031v2.pdf | |
| PWC | https://paperswithcode.com/paper/active-fairness-in-algorithmic-decision |
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TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces
| Title | TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces |
| Authors | Yash Patel, Lluis Gomez, Raul Gomez, Marçal Rusiñol, Dimosthenis Karatzas, C. V. Jawahar |
| Abstract | The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community. In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN. Our experiments demonstrate state-of-the-art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or naturally-supervised approaches. |
| Tasks | Image Classification, Object Detection |
| Published | 2018-07-04 |
| URL | http://arxiv.org/abs/1807.02110v1 |
| http://arxiv.org/pdf/1807.02110v1.pdf | |
| PWC | https://paperswithcode.com/paper/texttopicnet-self-supervised-learning-of |
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Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration
| Title | Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration |
| Authors | Xiaoxiao Wang, Xueying Guo, Jie Chuai, Zhitang Chen, Xin Liu |
| Abstract | Cellular network configuration plays a critical role in network performance. In current practice, network configuration depends heavily on field experience of engineers and often remains static for a long period of time. This practice is far from optimal. To address this limitation, online-learning-based approaches have great potentials to automate and optimize network configuration. Learning-based approaches face the challenges of learning a highly complex function for each base station and balancing the fundamental exploration-exploitation tradeoff while minimizing the exploration cost. Fortunately, in cellular networks, base stations (BSs) often have similarities even though they are not identical. To leverage such similarities, we propose kernel-based multi-BS contextual bandit algorithm based on multi-task learning. In the algorithm, we leverage the similarity among different BSs defined by conditional kernel embedding. We present theoretical analysis of the proposed algorithm in terms of regret and multi-task-learning efficiency. We evaluate the effectiveness of our algorithm based on a simulator built by real traces. |
| Tasks | Multi-Armed Bandits, Multi-Task Learning |
| Published | 2018-11-27 |
| URL | https://arxiv.org/abs/1811.10902v2 |
| https://arxiv.org/pdf/1811.10902v2.pdf | |
| PWC | https://paperswithcode.com/paper/kernel-based-multi-task-contextual-bandits-in |
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An Automatic Patch-based Approach for HER-2 Scoring in Immunohistochemical Breast Cancer Images Using Color Features
| Title | An Automatic Patch-based Approach for HER-2 Scoring in Immunohistochemical Breast Cancer Images Using Color Features |
| Authors | Caroline Q. Cordeiro, Sergio O. Ioshii, Jeovane H. Alves, Lucas F. Oliveira |
| Abstract | Breast cancer (BC) is the most common cancer among women world-wide, approximately 20-25% of BCs are HER-2 positive. Analysis of HER-2 is fundamental to defining the appropriate therapy for patients with breast cancer. Inter-pathologist variability in the test results can affect diagnostic accuracy. The present study intends to propose an automatic scoring HER-2 algorithm. Based on color features, the technique is fully-automated and avoids segmentation, showing a concordance higher than 90% with a pathologist in the experiments realized. |
| Tasks | |
| Published | 2018-05-14 |
| URL | http://arxiv.org/abs/1805.05392v1 |
| http://arxiv.org/pdf/1805.05392v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-automatic-patch-based-approach-for-her-2 |
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A Greedy Search Tree Heuristic for Symbolic Regression
| Title | A Greedy Search Tree Heuristic for Symbolic Regression |
| Authors | Fabricio Olivetti de Franca |
| Abstract | Symbolic Regression tries to find a mathematical expression that describes the relationship of a set of explanatory variables to a measured variable. The main objective is to find a model that minimizes the error and, optionally, that also minimizes the expression size. A smaller expression can be seen as an interpretable model considered a reliable decision model. This is often performed with Genetic Programming which represents their solution as expression trees. The shortcoming of this algorithm lies on this representation that defines a rugged search space and contains expressions of any size and difficulty. These pose as a challenge to find the optimal solution under computational constraints. This paper introduces a new data structure, called Interaction-Transformation (IT), that constrains the search space in order to exclude a region of larger and more complicated expressions. In order to test this data structure, it was also introduced an heuristic called SymTree. The obtained results show evidence that SymTree are capable of obtaining the optimal solution whenever the target function is within the search space of the IT data structure and competitive results when it is not. Overall, the algorithm found a good compromise between accuracy and simplicity for all the generated models. |
| Tasks | |
| Published | 2018-01-04 |
| URL | http://arxiv.org/abs/1801.01807v1 |
| http://arxiv.org/pdf/1801.01807v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-greedy-search-tree-heuristic-for-symbolic |
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The limit of artificial intelligence: Can machines be rational?
| Title | The limit of artificial intelligence: Can machines be rational? |
| Authors | Tshilidzi Marwala |
| Abstract | This paper studies the question on whether machines can be rational. It observes the existing reasons why humans are not rational which is due to imperfect and limited information, limited and inconsistent processing power through the brain and the inability to optimize decisions and achieve maximum utility. It studies whether these limitations of humans are transferred to the limitations of machines. The conclusion reached is that even though machines are not rational advances in technological developments make these machines more rational. It also concludes that machines can be more rational than humans. |
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| Published | 2018-12-16 |
| URL | http://arxiv.org/abs/1812.06510v1 |
| http://arxiv.org/pdf/1812.06510v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-limit-of-artificial-intelligence-can |
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