Paper Group ANR 1483
Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using DeepReinforcement Learning. Community Detection in the Sparse Hypergraph Stochastic Block Model. SAVEHR: Self Attention Vector Representations for EHR based Personalized Chronic Disease Onset Prediction and Interpretability. Efficient Drone Mobility Support …
Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using DeepReinforcement Learning
| Title | Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using DeepReinforcement Learning |
| Authors | Zining Liu, Chong Long, Xiaolu Lu, Zehong Hu, Jie Zhang, Yafang Wang |
| Abstract | Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers’ questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers’ requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers’ satisfaction. To achieve the optimal tradeoff between resources and customers’ satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning based routing method-double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning based framework is superior to the existing production system. Moreover, we also show our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the trade-off where both channel resources and customers’ satisfaction are optimal. |
| Tasks | Chatbot, Q-Learning |
| Published | 2019-11-24 |
| URL | https://arxiv.org/abs/1911.10521v1 |
| https://arxiv.org/pdf/1911.10521v1.pdf | |
| PWC | https://paperswithcode.com/paper/which-channel-to-ask-my-question-personalized |
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Community Detection in the Sparse Hypergraph Stochastic Block Model
| Title | Community Detection in the Sparse Hypergraph Stochastic Block Model |
| Authors | Soumik Pal, Yizhe Zhu |
| Abstract | We consider the community detection problem in sparse random hypergraphs. Angelini et al. (2015) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block model (HSBM). We solve the positive part of the conjecture for the case of two blocks: above the threshold, there is a spectral algorithm which asymptotically almost surely constructs a partition of the hypergraph correlated with the true partition. Our method is a generalization to random hypergraphs of the method developed by Massouli'{e} (2014) for sparse random graphs. |
| Tasks | Community Detection |
| Published | 2019-04-11 |
| URL | https://arxiv.org/abs/1904.05981v2 |
| https://arxiv.org/pdf/1904.05981v2.pdf | |
| PWC | https://paperswithcode.com/paper/community-detection-in-the-sparse-hypergraph |
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SAVEHR: Self Attention Vector Representations for EHR based Personalized Chronic Disease Onset Prediction and Interpretability
| Title | SAVEHR: Self Attention Vector Representations for EHR based Personalized Chronic Disease Onset Prediction and Interpretability |
| Authors | Sunil Mallya, Marc Overhage, Sravan Bodapati, Navneet Srivastava, Sahika Genc |
| Abstract | Chronic disease progression is emerging as an important area of investment for healthcare providers. As the quantity and richness of available clinical data continue to increase along with advances in machine learning, there is great potential to advance our approaches to caring for patients. An ideal approach to this problem should generate good performance on at least three axes namely, a) perform across many clinical conditions without requiring deep clinical expertise or extensive data scientist effort, b) generalization across populations, and c) be explainable (model interpretability). We present SAVEHR, a self-attention based architecture on heterogeneous structured EHR data that achieves $>$ 0.51 AUC-PR and $>$ 0.87 AUC-ROC gains on predicting the onset of four clinical conditions (CHF, Kidney Failure, Diabetes and COPD) 15-months in advance, and transfers with high performance onto a new population. We demonstrate that SAVEHR model performs superior to ten baselines on all three axes stated formerly. |
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| Published | 2019-11-13 |
| URL | https://arxiv.org/abs/1911.05370v1 |
| https://arxiv.org/pdf/1911.05370v1.pdf | |
| PWC | https://paperswithcode.com/paper/savehr-self-attention-vector-representations |
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Efficient Drone Mobility Support Using Reinforcement Learning
| Title | Efficient Drone Mobility Support Using Reinforcement Learning |
| Authors | Yun Chen, Xingqin Lin, Talha Khan, Mohammad Mozaffari |
| Abstract | Flying drones can be used in a wide range of applications and services from surveillance to package delivery. To ensure robust control and safety of drone operations, cellular networks need to provide reliable wireless connectivity to drone user equipments (UEs). To date, existing mobile networks have been primarily designed and optimized for serving ground UEs, thus making the mobility support in the sky challenging. In this paper, a novel handover (HO) mechanism is developed for a cellular-connected drone system to ensure robust wireless connectivity and mobility support for drone-UEs. By leveraging tools from reinforcement learning, HO decisions are dynamically optimized using a Q-learning algorithm to provide an efficient mobility support in the sky. The results show that the proposed approach can significantly reduce (e.g., by 80%) the number of HOs, while maintaining connectivity, compared to the baseline HO scheme in which the drone always connects to the strongest cell. |
| Tasks | Q-Learning |
| Published | 2019-11-21 |
| URL | https://arxiv.org/abs/1911.09715v1 |
| https://arxiv.org/pdf/1911.09715v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-drone-mobility-support-using |
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Active Learning for Segmentation Based on Bayesian Sample Queries
| Title | Active Learning for Segmentation Based on Bayesian Sample Queries |
| Authors | Firat Ozdemir, Zixuan Peng, Philipp Fuernstahl, Christine Tanner, Orcun Goksel |
| Abstract | Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but for this purpose large numbers of manual annotations are needed in the first place, which necessitate prohibitive levels of resources that are often unavailable. In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning. In this paper, we propose a novel method that combines representativeness with uncertainty in order to estimate ideal samples to be annotated, iteratively from a given dataset. Our novel representativeness metric is based on Bayesian sampling, by using information-maximizing autoencoders. We conduct experiments on a shoulder magnetic resonance imaging (MRI) dataset for the segmentation of four musculoskeletal tissue classes. Quantitative results show that the annotation of representative samples selected by our proposed querying method yields an improved segmentation performance at each active learning iteration, compared to a baseline method that also employs uncertainty and representativeness metrics. For instance, with only 10% of the dataset annotated, our method reaches within 5% of Dice score expected from the upper bound scenario of all the dataset given as annotated (an impractical scenario due to resource constraints), and this gap drops down to a mere 2% when less than a fifth of the dataset samples are annotated. Such active learning approach to selecting samples to annotate enables an optimal use of the expert clinician time, being often the bottleneck in realizing machine learning solutions in medicine. |
| Tasks | Active Learning |
| Published | 2019-12-22 |
| URL | https://arxiv.org/abs/1912.10493v1 |
| https://arxiv.org/pdf/1912.10493v1.pdf | |
| PWC | https://paperswithcode.com/paper/active-learning-for-segmentation-based-on |
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Fairness-Aware Neural Réyni Minimization for Continuous Features
| Title | Fairness-Aware Neural Réyni Minimization for Continuous Features |
| Authors | Vincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki |
| Abstract | The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. While plenty of fair algorithms have been proposed recently to tackle this challenge for discrete variables, only a few ideas exist for continuous ones. The objective in this paper is to ensure some independence level between the outputs of regression models and any given continuous sensitive variables. For this purpose, we use the Hirschfeld-Gebelein-R'enyi (HGR) maximal correlation coefficient as a fairness metric. We propose two approaches to minimize the HGR coefficient. First, by reducing an upper bound of the HGR with a neural network estimation of the $\chi^{2}$ divergence. Second, by minimizing the HGR directly with an adversarial neural network architecture. The idea is to predict the output Y while minimizing the ability of an adversarial neural network to find the estimated transformations which are required to predict the HGR coefficient. We empirically assess and compare our approaches and demonstrate significant improvements on previously presented work in the field. |
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| Published | 2019-11-12 |
| URL | https://arxiv.org/abs/1911.04929v1 |
| https://arxiv.org/pdf/1911.04929v1.pdf | |
| PWC | https://paperswithcode.com/paper/fairness-aware-neural-reyni-minimization-for |
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On the Optimality of Sparse Model-Based Planning for Markov Decision Processes
| Title | On the Optimality of Sparse Model-Based Planning for Markov Decision Processes |
| Authors | Alekh Agarwal, Sham Kakade, Lin F. Yang |
| Abstract | This work considers the sample complexity of obtaining an $\epsilon$-optimal policy in a discounted Markov Decision Process (MDP), given only access to a generative model. In this model, the learner accesses the underlying transition model via a sampling oracle that provides a sample of the next state, when given any state-action pair as input. In this work, we study the effectiveness of the most natural plug-in approach to model-based planning: we build the maximum likelihood estimate of the transition model in the MDP from observations and then find an optimal policy in this empirical MDP. We ask arguably the most basic and unresolved question in model-based planning: is the na"ive “plug-in” approach, non-asymptotically, minimax optimal in the quality of the policy it finds, given a fixed sample size? With access to a generative model, we resolve this question in the strongest possible sense: our main result shows that \emph{any} high accuracy solution in the plug-in model constructed with $N$ samples, provides an $\epsilon$-optimal policy in the true underlying MDP. In comparison, all prior (non-asymptotically) minimax optimal results use model-free approaches, such as the Variance Reduced Q-value iteration algorithm (Sidford et al 2018), while the best known model-based results (e.g. Azar et al 2013) require larger sample sample sizes in their dependence on the planning horizon or the state space. Notably, we show that the model-based approach allows the use of \emph{any} efficient planning algorithm in the empirical MDP, which simplifies the algorithm design as this approach does not tie the algorithm to the sampling procedure. The core of our analysis is a novel “absorbing MDP” construction to address the statistical dependency issues that arise in the analysis of model-based planning approaches, a construction which may be helpful more generally. |
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| Published | 2019-06-10 |
| URL | https://arxiv.org/abs/1906.03804v2 |
| https://arxiv.org/pdf/1906.03804v2.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-optimality-of-sparse-model-based |
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Assessing the Quality of Scientific Papers
| Title | Assessing the Quality of Scientific Papers |
| Authors | Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach |
| Abstract | A multitude of factors are responsible for the overall quality of scientific papers, including readability, linguistic quality, fluency,semantic complexity, and of course domain-specific technical factors. These factors vary from one field of study to another. In this paper, we propose a measure and method for assessing the overall quality of the scientific papers in a particular field of study. We evaluate our method in the computer science domain, but it can be applied to other technical and scientific fields.Our method is based on the corpus linguistics technique. This technique enables the extraction of required information and knowledge associated with a specific domain. For this purpose, we have created a large corpus, consisting of papers from very high impact conferences. First, we analyze this corpus in order to extract rich domain-specific terminology and knowledge. Then we use the acquired knowledge to estimate the quality of scientific papers by applying our proposed measure. We examine our measure on high and low scientific impact test corpora. Our results show a significant difference in the measure scores of the high and low impact test corpora. Second, we develop a classifier based on our proposed measure and compare it to the baseline classifier. Our results show that the classifier based on our measure over-performed the baseline classifier. Based on the presented results the proposed measure and the technique can be used for automated assessment of scientific papers. |
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| Published | 2019-08-12 |
| URL | https://arxiv.org/abs/1908.04200v1 |
| https://arxiv.org/pdf/1908.04200v1.pdf | |
| PWC | https://paperswithcode.com/paper/assessing-the-quality-of-scientific-papers |
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Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression
| Title | Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression |
| Authors | Daniele Ravi, Daniel C. Alexander, Neil P. Oxtoby |
| Abstract | Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This capability is valuable for secondary prevention clinical trials where outcomes and screening criteria involve neuroimaging. Traditional computational methods are limited by imposing a parametric model for atrophy and are extremely resource-demanding. Recent advances in deep learning have yielded data-driven models for longitudinal studies (e.g., face ageing) that are capable of generating synthetic images in real-time. Similar solutions can be used to model trajectories of atrophy in the brain, although new challenges need to be addressed to ensure accurate disease progression modelling. Here we propose Degenerative Adversarial NeuroImage Net (DaniNet) — a new deep learning approach that learns to emulate the effect of neurodegeneration on MRI by simulating atrophy as a function of ages, and disease progression. DaniNet uses an underlying set of Support Vector Regressors (SVRs) trained to capture the patterns of regional intensity changes that accompany disease progression. DaniNet produces whole output images, consisting of 2D-MRI slices that are constrained to match regional predictions from the SVRs. DaniNet is also able to maintain the unique brain morphology of individuals. Adversarial training ensures realistic brain images and smooth temporal progression. We train our model using 9652 T1-weighted (longitudinal) MRI extracted from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We perform quantitative and qualitative evaluations on a separate test set of 1283 images (also from ADNI) demonstrating the ability of DaniNet to produce accurate and convincing synthetic images that emulate disease progression. |
| Tasks | Predicting Patient Outcomes |
| Published | 2019-07-05 |
| URL | https://arxiv.org/abs/1907.02787v2 |
| https://arxiv.org/pdf/1907.02787v2.pdf | |
| PWC | https://paperswithcode.com/paper/degenerative-adversarial-neuroimage-nets |
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Real-world Mapping of Gaze Fixations Using Instance Segmentation for Road Construction Safety Applications
| Title | Real-world Mapping of Gaze Fixations Using Instance Segmentation for Road Construction Safety Applications |
| Authors | Idris Jeelani, Khashayar Asadi, Hariharan Ramshankar, Kevin Han, Alex Albert |
| Abstract | Research studies have shown that a large proportion of hazards remain unrecognized, which expose construction workers to unanticipated safety risks. Recent studies have also found that a strong correlation exists between viewing patterns of workers, captured using eye-tracking devices, and their hazard recognition performance. Therefore, it is important to analyze the viewing patterns of workers to gain a better understanding of their hazard recognition performance. This paper proposes a method that can automatically map the gaze fixations collected using a wearable eye-tracker to the predefined areas of interests. The proposed method detects these areas or objects (i.e., hazards) of interests through a computer vision-based segmentation technique and transfer learning. The mapped fixation data is then used to analyze the viewing behaviors of workers and compute their attention distribution. The proposed method is implemented on an under construction road as a case study to evaluate the performance of the proposed method. |
| Tasks | Eye Tracking, Instance Segmentation, Semantic Segmentation, Transfer Learning |
| Published | 2019-01-30 |
| URL | http://arxiv.org/abs/1901.11078v2 |
| http://arxiv.org/pdf/1901.11078v2.pdf | |
| PWC | https://paperswithcode.com/paper/real-world-mapping-of-gaze-fixations-using |
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Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data
| Title | Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data |
| Authors | Jackson A. Killian, Bryan Wilder, Amit Sharma, Daksha Shah, Vinod Choudhary, Bistra Dilkina, Milind Tambe |
| Abstract | Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. The data contains nearly 17,000 patients and 2.1M dose records. We lay the groundwork for learning from this real-world data, including a method for avoiding the effects of unobserved interventions in training data used for machine learning. We then construct a deep learning model, demonstrate its interpretability, and show how it can be adapted and trained in different clinical scenarios to better target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with 21% more patients and before 76% more missed doses than current heuristic baselines. For outcome prediction, our model performs 40% better than baseline methods, allowing cities to target more resources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our model can be trained in an end-to-end decision focused learning setting to achieve 15% better solution quality in an example decision problem faced by health workers. |
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| Published | 2019-02-05 |
| URL | https://arxiv.org/abs/1902.01506v3 |
| https://arxiv.org/pdf/1902.01506v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-prescribe-interventions-for |
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Thresholding Graph Bandits with GrAPL
| Title | Thresholding Graph Bandits with GrAPL |
| Authors | Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk |
| Abstract | In this paper, we introduce a new online decision making paradigm that we call Thresholding Graph Bandits. The main goal is to efficiently identify a subset of arms in a multi-armed bandit problem whose means are above a specified threshold. While traditionally in such problems, the arms are assumed to be independent, in our paradigm we further suppose that we have access to the similarity between the arms in the form of a graph, allowing us gain information about the arm means in fewer samples. Such settings play a key role in a wide range of modern decision making problems where rapid decisions need to be made in spite of the large number of options available at each time. We present GrAPL, a novel algorithm for the thresholding graph bandit problem. We demonstrate theoretically that this algorithm is effective in taking advantage of the graph structure when available and the reward function homophily (that strongly connected arms have similar rewards) when favorable. We confirm these theoretical findings via experiments on both synthetic and real data. |
| Tasks | Decision Making |
| Published | 2019-05-22 |
| URL | https://arxiv.org/abs/1905.09190v3 |
| https://arxiv.org/pdf/1905.09190v3.pdf | |
| PWC | https://paperswithcode.com/paper/thresholding-graph-bandits-with-grapl |
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Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration
| Title | Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration |
| Authors | Rohan Paleja, Matthew Gombolay |
| Abstract | For assistive robots and virtual agents to achieve ubiquity, machines will need to anticipate the needs of their human counterparts. The field of Learning from Demonstration (LfD) has sought to enable machines to infer predictive models of human behavior for autonomous robot control. However, humans exhibit heterogeneity in decision-making, which traditional LfD approaches fail to capture. To overcome this challenge, we propose a Bayesian LfD framework to infer an integrated representation of all human task demonstrators by inferring human-specific embeddings, thereby distilling their unique characteristics. We validate our approach is able to outperform state-of-the-art techniques on both synthetic and real-world data sets. |
| Tasks | Decision Making |
| Published | 2019-03-14 |
| URL | http://arxiv.org/abs/1903.06047v1 |
| http://arxiv.org/pdf/1903.06047v1.pdf | |
| PWC | https://paperswithcode.com/paper/inferring-personalized-bayesian-embeddings |
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Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors
| Title | Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors |
| Authors | Mariusz Kubkowski, Jan Mielniczuk |
| Abstract | We consider selection of random predictors for high-dimensional regression problem with binary response for a general loss function. Important special case is when the binary model is semiparametric and the response function is misspecified under parametric model fit. Selection for such a scenario aims at recovering the support of the minimizer of the associated risk with large probability. We propose a two-step selection procedure which consists of screening and ordering predictors by Lasso method and then selecting a subset of predictors which minimizes Generalized Information Criterion on the corresponding nested family of models. We prove consistency of the selection method under conditions which allow for much larger number of predictors than number of observations. For the semiparametric case when distribution of random predictors satisfies linear regression conditions the true and the estimated parameters are collinear and their common support can be consistently identified. |
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| Published | 2019-06-10 |
| URL | https://arxiv.org/abs/1906.04175v1 |
| https://arxiv.org/pdf/1906.04175v1.pdf | |
| PWC | https://paperswithcode.com/paper/selection-consistency-of-lasso-based |
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End-to-end facial and physiological model for Affective Computing and applications
| Title | End-to-end facial and physiological model for Affective Computing and applications |
| Authors | Joaquim Comas, Decky Aspandi, Xavier Binefa |
| Abstract | In recent years, Affective Computing and its applications have become a fast-growing research topic. Furthermore, the rise of Deep Learning has introduced significant improvements in the emotion recognition system compared to classical methods. In this work, we propose a multi-modal emotion recognition model based on deep learning techniques using the combination of peripheral physiological signals and facial expressions. Moreover, we present an improvement to proposed models by introducing latent features extracted from our internal Bio Auto-Encoder (BAE). Both models are trained and evaluated on AMIGOS datasets reporting valence, arousal, and emotion state classification. Finally, to demonstrate a possible medical application in affective computing using deep learning techniques, we applied the proposed method to the assessment of anxiety therapy. To this purpose, a reduced multi-modal database has been collected by recording facial expressions and peripheral signals such as Electrocardiogram (ECG) and Galvanic Skin Response (GSR) of each patient. Valence and arousal estimation was extracted using the proposed model from the beginning until the end of the therapy, with successful evaluation to the different emotional changes in the temporal domain. |
| Tasks | Emotion Recognition |
| Published | 2019-12-10 |
| URL | https://arxiv.org/abs/1912.04711v2 |
| https://arxiv.org/pdf/1912.04711v2.pdf | |
| PWC | https://paperswithcode.com/paper/end-to-end-facial-and-physiological-model-for |
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