cs.AR - 硬件体系结构

    cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.DS - 数据结构与算法 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.MA - 多代理系统 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 eess.IV - 图像与视频处理 math.CO - 组合数学 math.PR - 概率 math.ST - 统计理论 physics.soc-ph - 物理学与社会 q-bio.NC - 神经元与认知 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cs.AR]Polystore++: Accelerated Polystore System for Heterogeneous Workloads
    • [cs.CL]A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
    • [cs.CL]An Analysis of Source-Side Grammatical Errors in NMT
    • [cs.CL]BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
    • [cs.CL]Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
    • [cs.CL]Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor
    • [cs.CL]Incorporating Context and External Knowledge for Pronoun Coreference Resolution
    • [cs.CL]Outline Generation: Understanding the Inherent Content Structure of Documents
    • [cs.CL]Personalizing Dialogue Agents via Meta-Learning
    • [cs.CL]Training language GANs from Scratch
    • [cs.CL]Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
    • [cs.CL]mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
    • [cs.CR]Devil in the Detail: Attack Scenarios in Industrial Applications
    • [cs.CR]Tiresias: Predicting Security Events Through Deep Learning
    • [cs.CV]A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images
    • [cs.CV]A Compressive Sensing Video dataset using Pixel-wise coded exposure
    • [cs.CV]A Real-Time Tiny Detection Model for Stem End and Blossom End of Navel Orange
    • [cs.CV]ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
    • [cs.CV]Beyond Intra-modality Discrepancy: A Comprehensive Survey of Heterogeneous Person Re-identification
    • [cs.CV]Brain-mediated Transfer Learning of Convolutional Neural Networks
    • [cs.CV]DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects
    • [cs.CV]Deep Reason: A Strong Baseline for Real-World Visual Reasoning
    • [cs.CV]Deep Trajectory for Recognition of Human Behaviours
    • [cs.CV]From Here to There: Video Inbetweening Using Direct 3D Convolutions
    • [cs.CV]Generative Flow via Invertible nxn Convolution
    • [cs.CV]Guided Stereo Matching
    • [cs.CV]Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning
    • [cs.CV]Light-Weight RetinaNet for Object Detection
    • [cs.CV]Mask-Guided Portrait Editing with Conditional GANs
    • [cs.CV]Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing
    • [cs.CV]OVSNet : Towards One-Pass Real-Time Video Object Segmentation
    • [cs.CV]PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
    • [cs.CV]Pose-adaptive Hierarchical Attention Network for Facial Expression Recognition
    • [cs.CV]Rank3DGAN: Semantic mesh generation using relative attributes
    • [cs.CV]Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion
    • [cs.CV]Saliency detection based on structural dissimilarity induced by image quality assessment model
    • [cs.CV]Self-Critical Reasoning for Robust Visual Question Answering
    • [cs.CV]Uncertainty Estimation in One-Stage Object Detection
    • [cs.CY]Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions
    • [cs.DC]Atomic Cross-Chain Swaps with Improved Space and Time Complexity
    • [cs.DC]Deploying AI Frameworks on Secure HPC Systems with Containers
    • [cs.DC]Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
    • [cs.DC]Performance-Feedback Autoscaling with Budget Constraints for Cloud-based Workloads of Workflows
    • [cs.DS]A Practical Framework for Solving Center-Based Clustering with Outliers
    • [cs.DS]Hardness of Distributed Optimization
    • [cs.HC]From Search Engines to Search Services: An End-User Driven Approach
    • [cs.IR]Controlling Risk of Web Question Answering
    • [cs.IR]MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching
    • [cs.IR]Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
    • [cs.IT]Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation
    • [cs.IT]Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems
    • [cs.IT]Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
    • [cs.IT]Minimax Rates of Estimating Approximate Differential Privacy
    • [cs.IT]On Recurrent Neural Networks for Sequence-based Processing in Communications
    • [cs.IT]On the Global Minimizers of Real Robust Phase Retrieval with Sparse Noise
    • [cs.IT]On the Performance Analysis of Binary Hypothesis Testing with Byzantine Sensors
    • [cs.IT]Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications
    • [cs.IT]Signature codes for weighted binary adder channel and multimedia fingerprinting
    • [cs.IT]Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
    • [cs.LG]Adaptive Symmetric Reward Noising for Reinforcement Learning
    • [cs.LG]An Explicitly Relational Neural Network Architecture
    • [cs.LG]Approximation Ratios of Graph Neural Networks for Combinatorial Problems
    • [cs.LG]Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
    • [cs.LG]Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
    • [cs.LG]Continual Reinforcement Learning in 3D Non-stationary Environments
    • [cs.LG]Copy this Sentence
    • [cs.LG]Curriculum Loss: Robust Learning and Generalization against Label Corruption
    • [cs.LG]Deep Model Predictive Control with Online Learning for Complex Physical Systems
    • [cs.LG]Deep density ratio estimation for change point detection
    • [cs.LG]Deep-gKnock: nonlinear group-feature selection with deep neural network
    • [cs.LG]Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
    • [cs.LG]Discrete Flows: Invertible Generative Models of Discrete Data
    • [cs.LG]Distributional Policy Optimization: An Alternative Approach for Continuous Control
    • [cs.LG]Doctor of Crosswise: Reducing Over-parametrization in Neural Networks
    • [cs.LG]Efficient Batch Black-box Optimization with Deterministic Regret Bounds
    • [cs.LG]Embedded Meta-Learning: Toward more flexible deep-learning models
    • [cs.LG]Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks
    • [cs.LG]Exploration via Flow-Based Intrinsic Rewards
    • [cs.LG]Federated Forest
    • [cs.LG]Generative Grading: Neural Approximate Parsing for Automated Student Feedback
    • [cs.LG]Generative adversarial network based on chaotic time series
    • [cs.LG]Graph Representations for Higher-Order Logic and Theorem Proving
    • [cs.LG]Gravity-Inspired Graph Autoencoders for Directed Link Prediction
    • [cs.LG]HDI-Forest: Highest Density Interval Regression Forest
    • [cs.LG]Interpreting a Recurrent Neural Network Model for ICU Mortality Using Learned Binary Masks
    • [cs.LG]Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
    • [cs.LG]Learning Cross-Domain Representation with Multi-Graph Neural Network
    • [cs.LG]Learning Low-Rank Approximation for CNNs
    • [cs.LG]Learning Surrogate Losses
    • [cs.LG]Learning to learn by Self-Critique
    • [cs.LG]Likelihood-Free Inference and Generation of Molecular Graphs
    • [cs.LG]Loss Surface Modality of Feed-Forward Neural Network Architectures
    • [cs.LG]Memorized Sparse Backpropagation
    • [cs.LG]Momentum-Based Variance Reduction in Non-Convex SGD
    • [cs.LG]Multi-Kernel Correntropy for Robust Learning
    • [cs.LG]Neural Temporal-Difference Learning Converges to Global Optima
    • [cs.LG]Neuro-Optimization: Learning Objective Functions Using Neural Networks
    • [cs.LG]Not All Features Are Equal: Feature Leveling Deep Neural Networks for Better Interpretation
    • [cs.LG]On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural Networks
    • [cs.LG]Optimizing Shallow Networks for Binary Classification
    • [cs.LG]Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
    • [cs.LG]Partially Encrypted Machine Learning using Functional Encryption
    • [cs.LG]Perturbed Model Validation: A New Framework to Validate Model Relevance
    • [cs.LG]Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering
    • [cs.LG]Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective
    • [cs.LG]Robust Attribution Regularization
    • [cs.LG]SCRAM: Spatially Coherent Randomized Attention Maps
    • [cs.LG]STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
    • [cs.LG]Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks
    • [cs.LG]Statistical embedding for directed graphs
    • [cs.LG]Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks
    • [cs.LG]Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
    • [cs.LG]The advantages of multiple classes for reducing overfitting from test set reuse
    • [cs.LG]Training decision trees as replacement for convolution layers
    • [cs.LG]What Can ResNet Learn Efficiently, Going Beyond Kernels?
    • [cs.LG]X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations
    • [cs.MA]Decentralized Informative Path Planning with Exploration-Exploitation Balance for Swarm Robotic Search
    • [cs.MA]winPIBT: Expanded Prioritized Algorithm for Iterative Multi-agent Path Finding
    • [cs.NE]Instruction-Level Design of Local Optimisers using Push GP
    • [cs.RO]Designing an Inertia Actuator with a Fast Rotating Gyro inside an Egg-shaped Robot
    • [cs.RO]Mechatronic Design of a Dribbling System for RoboCup Small Size Robot
    • [cs.RO]Scene Induced Multi-Modal Trajectory Forecasting via Planning
    • [cs.RO]Visual Model-predictive Localization for Computationally Efficient Autonomous Racing of a 72-gram Drone
    • [cs.SD]Disentangled Feature for Weakly Supervised Multi-class Sound Event Detection
    • [cs.SE]A Customised App to Attract Female Teenagers to Coding
    • [cs.SI]An Integrated Model for User Innovation Knowledge Based on Super-network
    • [cs.SI]Extended Scale-Free Networks
    • [cs.SI]Multifaceted Privacy: How to Express Your Online Persona without Revealing Your Sensitive Attributes
    • [cs.SI]Tempus Volat, Hora Fugit — A Survey of Dynamic Network Models in Discrete and Continuous Time
    • [econ.EM]Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
    • [econ.EM]Semi-Parametric Efficient Policy Learning with Continuous Actions
    • [eess.IV]A Research and Strategy of Remote Sensing Image Denoising Algorithms
    • [eess.IV]Functional Segmentation through Dynamic Mode Decomposition: Automatic Quantification of Kidney Function in DCE-MRI Images
    • [eess.IV]Tissue segmentation with deep 3D networks and spatial priors
    • [math.CO]A concatenation construction for propelinear perfect codes from regular subgroups of GA(r,2)
    • [math.PR]Revisiting Relations between Stochastic Ageing and Dependence for Exchangeable Lifetimes with an Extension for the IFRA/DFRA Property
    • [math.PR]The Skipping Sampler: A new approach to sample from complex conditional densities
    • [math.ST]Asymptotic Behaviour of Discretised Functionals of Long-Range Dependent Functional Data
    • [math.ST]High-Dimensional Functional Factor Models
    • [math.ST]Likelihood ratio tests for many groups in high dimensions
    • [math.ST]Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator
    • [physics.soc-ph]Morphological organization of point-to-point transport in complex networks
    • [q-bio.NC]Damped oscillations of the probability of random events followed by absolute refractory period
    • [stat.AP]Inference of Dynamic Graph Changes for Functional Connectome
    • [stat.AP]The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling
    • [stat.CO]A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler
    • [stat.CO]Divide-and-Conquer Information-Based Optimal Subdata Selection Algorithm
    • [stat.CO]Estimating Convergence of Markov chains with L-Lag Couplings
    • [stat.CO]Monitoring dynamic networks: a simulation-based strategy for comparing monitoring methods and a comparative study
    • [stat.CO]Parallel Coordinate Order for High-Dimensional Data
    • [stat.ME]Adaptive Function-on-Scalar Regression with a Smoothing Elastic Net
    • [stat.ME]Optimal nonparametric change point detection and localization
    • [stat.ML]Concentration bounds for linear Monge mapping estimation and optimal transport domain adaptation
    • [stat.ML]Convergence Guarantees for Adaptive Bayesian Quadrature Methods
    • [stat.ML]Dirac Delta Regression: Conditional Density Estimation with Clinical Trials
    • [stat.ML]OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits
    • [stat.ML]Polynomial Cost of Adaptation for X -Armed Bandits
    • [stat.ML]Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models
    • [stat.ML]Privacy Risks of Securing Machine Learning Models against Adversarial Examples
    • [stat.ML]Sequential Gaussian Processes for Online Learning of Nonstationary Functions
    • [stat.ML]Sliced Gromov-Wasserstein
    • [stat.ML]forgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature extraction

    ·····································

    • [cs.AR]Polystore++: Accelerated Polystore System for Heterogeneous Workloads
    Rekha Singhal, Nathan Zhang, Luigi Nardi, Muhammad Shahbaz, Kunle Olukotun
    http://arxiv.org/abs/1905.10336v1

    • [cs.CL]A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
    Fuli Luo, Peng Li, Jie Zhou, Pengcheng Yang, Baobao Chang, Zhifang Sui, Xu Sun
    http://arxiv.org/abs/1905.10060v1

    • [cs.CL]An Analysis of Source-Side Grammatical Errors in NMT
    Antonios Anastasopoulos
    http://arxiv.org/abs/1905.10024v1

    • [cs.CL]BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
    Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, Kristina Toutanova
    http://arxiv.org/abs/1905.10044v1

    • [cs.CL]Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
    Sungjin Lee, Igor Shalyminov
    http://arxiv.org/abs/1905.10247v1

    • [cs.CL]Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor
    Malvina Nissim, Rik van Noord, Rob van der Goot
    http://arxiv.org/abs/1905.09866v1

    • [cs.CL]Incorporating Context and External Knowledge for Pronoun Coreference Resolution
    Hongming Zhang, Yan Song, Yangqiu Song
    http://arxiv.org/abs/1905.10238v1

    • [cs.CL]Outline Generation: Understanding the Inherent Content Structure of Documents
    Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xueqi Cheng
    http://arxiv.org/abs/1905.10039v1

    • [cs.CL]Personalizing Dialogue Agents via Meta-Learning
    Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu, Pascale Fung
    http://arxiv.org/abs/1905.10033v1

    • [cs.CL]Training language GANs from Scratch
    Cyprien de Masson d’Autume, Mihaela Rosca, Jack Rae, Shakir Mohamed
    http://arxiv.org/abs/1905.09922v1

    • [cs.CL]Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
    Varun Kumar, Alison Smith-Renner, Leah Findlater, Kevin Seppi, Jordan Boyd-Graber
    http://arxiv.org/abs/1905.09864v1

    • [cs.CL]mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
    Dayiheng Liu, Xu Yang, Feng He, Yuanyuan Chen, Jiancheng Lv
    http://arxiv.org/abs/1905.10072v1

    • [cs.CR]Devil in the Detail: Attack Scenarios in Industrial Applications
    Simon D. Duque Anton, Alexander Hafner, Hans Dieter Schotten
    http://arxiv.org/abs/1905.10292v1

    • [cs.CR]Tiresias: Predicting Security Events Through Deep Learning
    Yun Shen, Enrico Mariconti, Pierre-Antoine Vervier, Gianluca Stringhini
    http://arxiv.org/abs/1905.10328v1

    • [cs.CV]A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images
    Junxing Hu, Ling Li, Yijun Lin, Fengge Wu, Junsuo Zhao
    http://arxiv.org/abs/1905.10231v1

    • [cs.CV]A Compressive Sensing Video dataset using Pixel-wise coded exposure
    Sathyaprakash Narayanan, Yeshwanth Beti, Chetan Singh Thakur
    http://arxiv.org/abs/1905.10054v1

    • [cs.CV]A Real-Time Tiny Detection Model for Stem End and Blossom End of Navel Orange
    Xiaoye Sun, Shaoyun Xu, Gongyan Li
    http://arxiv.org/abs/1905.09994v1

    • [cs.CV]ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
    Xinxin Hu, Kailun Yang, Lei Fei, Kaiwei Wang
    http://arxiv.org/abs/1905.10089v1

    • [cs.CV]Beyond Intra-modality Discrepancy: A Comprehensive Survey of Heterogeneous Person Re-identification
    Zheng Wang, Zhixiang Wang, Yang Wu, Jingdong Wang, Shin’ichi Satoh
    http://arxiv.org/abs/1905.10048v1

    • [cs.CV]Brain-mediated Transfer Learning of Convolutional Neural Networks
    Satoshi Nishida, Yusuke Nakano, Antoine Blanc, Shinji Nishimoto
    http://arxiv.org/abs/1905.10037v1

    • [cs.CV]DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects
    Edgar Tretschk, Ayush Tewari, Michael Zollhöfer, Vladislav Golyanik, Christian Theobalt
    http://arxiv.org/abs/1905.10290v1

    • [cs.CV]Deep Reason: A Strong Baseline for Real-World Visual Reasoning
    Chenfei Wu, Yanzhao Zhou, Gen Li, Nan Duan, Duyu Tang, Xiaojie Wang
    http://arxiv.org/abs/1905.10226v1

    • [cs.CV]Deep Trajectory for Recognition of Human Behaviours
    Tauseef Ali, Eissa Jaber Alreshidi
    http://arxiv.org/abs/1905.10357v1

    • [cs.CV]From Here to There: Video Inbetweening Using Direct 3D Convolutions
    Yunpeng Li, Dominik Roblek, Marco Tagliasacchi
    http://arxiv.org/abs/1905.10240v1

    • [cs.CV]Generative Flow via Invertible nxn Convolution
    Thanh-Dat Truong, Khoa Luu, Chi Nhan Duong, Ngan Le, Minh-Triet Tran
    http://arxiv.org/abs/1905.10170v1

    • [cs.CV]Guided Stereo Matching
    Matteo Poggi, Davide Pallotti, Fabio Tosi, Stefano Mattoccia
    http://arxiv.org/abs/1905.10107v1

    • [cs.CV]Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning
    Yongxi Lu, Ziyao Tang, Tara Javidi
    http://arxiv.org/abs/1905.10000v1

    • [cs.CV]Light-Weight RetinaNet for Object Detection
    Yixing Li, Fengbo Ren
    http://arxiv.org/abs/1905.10011v1

    • [cs.CV]Mask-Guided Portrait Editing with Conditional GANs
    Shuyang Gu, Jianmin Bao, Hao Yang, Dong Chen, Fang Wen, Lu Yuan
    http://arxiv.org/abs/1905.10346v1

    • [cs.CV]Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing
    Yang Lu, Xiaohui Liang, Frederick W. B. Li
    http://arxiv.org/abs/1905.10100v1

    • [cs.CV]OVSNet : Towards One-Pass Real-Time Video Object Segmentation
    Peng Sun, Peiwen Lin, Guangliang Cheng, Jianping Shi, Jiawan Zhang, Xi Li
    http://arxiv.org/abs/1905.10064v1

    • [cs.CV]PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
    Junyu Gao, Qi Wang, Xuelong Li
    http://arxiv.org/abs/1905.10085v1

    • [cs.CV]Pose-adaptive Hierarchical Attention Network for Facial Expression Recognition
    Yuanyuan Liu, Jiyao Peng, Jiabei Zeng, Shiguang Shan
    http://arxiv.org/abs/1905.10059v1

    • [cs.CV]Rank3DGAN: Semantic mesh generation using relative attributes
    Yassir Saquil, Qun-Ce Xu, Yong-Liang Yang, Peter Hall
    http://arxiv.org/abs/1905.10257v1

    • [cs.CV]Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion
    Andreas Pfeuffer, Klaus Dietmayer
    http://arxiv.org/abs/1905.10117v1

    • [cs.CV]Saliency detection based on structural dissimilarity induced by image quality assessment model
    Yang Li, Xuanqin Mou
    http://arxiv.org/abs/1905.10150v1

    • [cs.CV]Self-Critical Reasoning for Robust Visual Question Answering
    Jialin Wu, Raymond J. Mooney
    http://arxiv.org/abs/1905.09998v1

    • [cs.CV]Uncertainty Estimation in One-Stage Object Detection
    Florian Kraus, Klaus Dietmayer
    http://arxiv.org/abs/1905.10296v1

    • [cs.CY]Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions
    Michael Mathioudakis, Carlos Castillo, Giorgio Barnabo, Sergio Celis
    http://arxiv.org/abs/1905.09947v1

    • [cs.DC]Atomic Cross-Chain Swaps with Improved Space and Time Complexity
    Soichiro Imoto, Yuichi Sudo, Hirotsugu Kakugawa, Toshimitsu Masuzawa
    http://arxiv.org/abs/1905.09985v1

    • [cs.DC]Deploying AI Frameworks on Secure HPC Systems with Containers
    David Brayford, Sofia Vallecorsa, Atanas Atanasov, Fabio Baruffa, Walter Riviera
    http://arxiv.org/abs/1905.10090v1

    • [cs.DC]Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
    Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, Junshan Zhang
    http://arxiv.org/abs/1905.10083v1

    • [cs.DC]Performance-Feedback Autoscaling with Budget Constraints for Cloud-based Workloads of Workflows
    Alexey Ilyushkin, André Bauer, Alessandro V. Papadopoulos, Ewa Deelman, Alexandru Iosup
    http://arxiv.org/abs/1905.10270v1

    • [cs.DS]A Practical Framework for Solving Center-Based Clustering with Outliers
    Hu Ding, Haikuo Yu
    http://arxiv.org/abs/1905.10143v1

    • [cs.DS]Hardness of Distributed Optimization
    Nir Bachrach, Keren Censor-Hillel, Michal Dory, Yuval Efron, Dean Leitersdorf, Ami Paz
    http://arxiv.org/abs/1905.10284v1

    • [cs.HC]From Search Engines to Search Services: An End-User Driven Approach
    Gabriela Bosetti, Sergio Firmenich, Alejandro Fernandez, Marco Winckler, Gustavo Rossi
    http://arxiv.org/abs/1905.10215v1

    • [cs.IR]Controlling Risk of Web Question Answering
    ixin Su, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xueqi Cheng
    http://arxiv.org/abs/1905.10077v1

    • [cs.IR]MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching
    Jiafeng Guo, Yixing Fan, Xiang Ji, Xueqi Cheng
    http://arxiv.org/abs/1905.10289v1

    • [cs.IR]Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
    Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
    http://arxiv.org/abs/1905.09248v3

    • [cs.IT]Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation
    Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel
    http://arxiv.org/abs/1905.10031v1

    • [cs.IT]Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems
    Samad Ali, Hossein Asgharimoghaddam, Nandana Rajatheva, Walid Saad, Jussi Haapola
    http://arxiv.org/abs/1905.09880v1

    • [cs.IT]Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
    Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
    http://arxiv.org/abs/1905.10028v1

    • [cs.IT]Minimax Rates of Estimating Approximate Differential Privacy
    Xiyang Liu, Sewoong Oh
    http://arxiv.org/abs/1905.10335v1

    • [cs.IT]On Recurrent Neural Networks for Sequence-based Processing in Communications
    Daniel Tandler, Sebastian Dörner, Sebastian Cammerer, Stephan ten Brink
    http://arxiv.org/abs/1905.09983v1

    • [cs.IT]On the Global Minimizers of Real Robust Phase Retrieval with Sparse Noise
    Aleksandr Aravkin, James Burke, Daiwei He
    http://arxiv.org/abs/1905.10358v1

    • [cs.IT]On the Performance Analysis of Binary Hypothesis Testing with Byzantine Sensors
    Yuqing Ni, Kemi Ding, Yong Yang, Ling Shi
    http://arxiv.org/abs/1905.10118v1

    • [cs.IT]Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications
    Hong Shen, Wei Xu, Shulei Gong, Zhenyao He, Chunming Zhao
    http://arxiv.org/abs/1905.10075v1

    • [cs.IT]Signature codes for weighted binary adder channel and multimedia fingerprinting
    Jinping Fan, Yujie Gu, Masahiro Hachimori, Ying Miao
    http://arxiv.org/abs/1905.10180v1

    • [cs.IT]Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
    David G. Clark, Jesse A. Livezey, Kristofer E. Bouchard
    http://arxiv.org/abs/1905.09944v1

    • [cs.LG]Adaptive Symmetric Reward Noising for Reinforcement Learning
    Refael Vivanti, Talya D. Sohlberg-Baris, Shlomo Cohen, Orna Cohen
    http://arxiv.org/abs/1905.10144v1

    • [cs.LG]An Explicitly Relational Neural Network Architecture
    Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David Barrett, Marta Garnelo
    http://arxiv.org/abs/1905.10307v1

    • [cs.LG]Approximation Ratios of Graph Neural Networks for Combinatorial Problems
    Ryoma Sato, Makoto Yamada, Hisashi Kashima
    http://arxiv.org/abs/1905.10261v1

    • [cs.LG]Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
    Iddo Drori, Yamuna Krishnamurthy, Raoni Lourenco, Remi Rampin, Kyunghyun Cho, Claudio Silva, Juliana Freire
    http://arxiv.org/abs/1905.10345v1

    • [cs.LG]Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
    Bo Kang, Darío García García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie
    http://arxiv.org/abs/1905.10086v1

    • [cs.LG]Continual Reinforcement Learning in 3D Non-stationary Environments
    Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello, Davide Maltoni
    http://arxiv.org/abs/1905.10112v1

    • [cs.LG]Copy this Sentence
    Vasileios Lioutas, Andriy Drozdyuk
    http://arxiv.org/abs/1905.09856v1

    • [cs.LG]Curriculum Loss: Robust Learning and Generalization against Label Corruption
    Yueming Lyu, Ivor W. Tsang
    http://arxiv.org/abs/1905.10045v1

    • [cs.LG]Deep Model Predictive Control with Online Learning for Complex Physical Systems
    Katharina Bieker, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, Michael Dellnitz
    http://arxiv.org/abs/1905.10094v1

    • [cs.LG]Deep density ratio estimation for change point detection
    Haidar Khan, Lara Marcuse, Bülent Yener
    http://arxiv.org/abs/1905.09876v1

    • [cs.LG]Deep-gKnock: nonlinear group-feature selection with deep neural network
    Guangyu Zhu, Tingting Zhao
    http://arxiv.org/abs/1905.10013v1

    • [cs.LG]Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
    Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette
    http://arxiv.org/abs/1905.10259v1

    • [cs.LG]Discrete Flows: Invertible Generative Models of Discrete Data
    Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
    http://arxiv.org/abs/1905.10347v1

    • [cs.LG]Distributional Policy Optimization: An Alternative Approach for Continuous Control
    Chen Tessler, Guy Tennenholtz, Shie Mannor
    http://arxiv.org/abs/1905.09855v1

    • [cs.LG]Doctor of Crosswise: Reducing Over-parametrization in Neural Networks
    J. D. Curtó, H. C. Zarza
    http://arxiv.org/abs/1905.10324v1

    • [cs.LG]Efficient Batch Black-box Optimization with Deterministic Regret Bounds
    Yueming Lyu, Yuan Yuan, Ivor W. Tsang
    http://arxiv.org/abs/1905.10041v1

    • [cs.LG]Embedded Meta-Learning: Toward more flexible deep-learning models
    Andrew K. Lampinen, James L. McClelland
    http://arxiv.org/abs/1905.09950v1

    • [cs.LG]Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks
    Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma
    http://arxiv.org/abs/1905.10264v1

    • [cs.LG]Exploration via Flow-Based Intrinsic Rewards
    Hsuan-Kung Yang, Po-Han Chiang, Min-Fong Hong, Chun-Yi Lee
    http://arxiv.org/abs/1905.10071v1

    • [cs.LG]Federated Forest
    Yang Liu, Yingting Liu, Zhijie Liu, Junbo Zhang, Chuishi Meng, Yu Zheng
    http://arxiv.org/abs/1905.10053v1

    • [cs.LG]Generative Grading: Neural Approximate Parsing for Automated Student Feedback
    Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, John Mitchell, Noah Goodman, Chris Piech
    http://arxiv.org/abs/1905.09916v1

    • [cs.LG]Generative adversarial network based on chaotic time series
    Makoto Naruse, Takashi Matsubara, Nicolas Chauvet, Kazutaka Kanno, Tianyu Yang, Atsushi Uchida
    http://arxiv.org/abs/1905.10163v1

    • [cs.LG]Graph Representations for Higher-Order Logic and Theorem Proving
    Aditya Paliwal, Sarah Loos, Markus Rabe, Kshitij Bansal, Christian Szegedy
    http://arxiv.org/abs/1905.10006v1

    • [cs.LG]Gravity-Inspired Graph Autoencoders for Directed Link Prediction
    Guillaume Salha, Stratis Limnios, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis
    http://arxiv.org/abs/1905.09570v2

    • [cs.LG]HDI-Forest: Highest Density Interval Regression Forest
    Lin Zhu, Jiaxin Lu, Yihong Chen
    http://arxiv.org/abs/1905.10101v1

    • [cs.LG]Interpreting a Recurrent Neural Network Model for ICU Mortality Using Learned Binary Masks
    Long V. Ho, Melissa D. Aczon, David Ledbetter, Randall Wetzel
    http://arxiv.org/abs/1905.09865v1

    • [cs.LG]Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
    Xin Huang, Boli Chen, Lin Xiao, Liping Jing
    http://arxiv.org/abs/1905.10070v1

    • [cs.LG]Learning Cross-Domain Representation with Multi-Graph Neural Network
    Yi Ouyang, Bin Guo, Xing Tang, Xiuqiang He, Jian Xiong, Zhiwen Yu
    http://arxiv.org/abs/1905.10095v1

    • [cs.LG]Learning Low-Rank Approximation for CNNs
    Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Gu-Yeon Wei
    http://arxiv.org/abs/1905.10145v1

    • [cs.LG]Learning Surrogate Losses
    Josif Grabocka, Randolf Scholz, Lars Schmidt-Thieme
    http://arxiv.org/abs/1905.10108v1

    • [cs.LG]Learning to learn by Self-Critique
    Antreas Antoniou, Amos Storkey
    http://arxiv.org/abs/1905.10295v1

    • [cs.LG]Likelihood-Free Inference and Generation of Molecular Graphs
    Sebastian Pölsterl, Christian Wachinger
    http://arxiv.org/abs/1905.10310v1

    • [cs.LG]Loss Surface Modality of Feed-Forward Neural Network Architectures
    Anna Sergeevna Bosman, Andries Engelbrecht, Mardé Helbig
    http://arxiv.org/abs/1905.10268v1

    • [cs.LG]Memorized Sparse Backpropagation
    Zhiyuan Zhang, Pengcheng Yang, Xuancheng Ren, Xu Sun
    http://arxiv.org/abs/1905.10194v1

    • [cs.LG]Momentum-Based Variance Reduction in Non-Convex SGD
    Ashok Cutkosky, Francesco Orabona
    http://arxiv.org/abs/1905.10018v1

    • [cs.LG]Multi-Kernel Correntropy for Robust Learning
    Badong Chen, Xin Wang, Zejian yuan, Pengju Ren, Jing Qin
    http://arxiv.org/abs/1905.10115v1

    • [cs.LG]Neural Temporal-Difference Learning Converges to Global Optima
    Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang
    http://arxiv.org/abs/1905.10027v1

    • [cs.LG]Neuro-Optimization: Learning Objective Functions Using Neural Networks
    Younghan Jeon, Minsik Lee, Jin Young Choi
    http://arxiv.org/abs/1905.10079v1

    • [cs.LG]Not All Features Are Equal: Feature Leveling Deep Neural Networks for Better Interpretation
    Yingjing Lu, Runde Yang
    http://arxiv.org/abs/1905.10009v1

    • [cs.LG]On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural Networks
    Bing Yu, Junzhao Zhang, Zhanxing Zhu
    http://arxiv.org/abs/1905.10157v1

    • [cs.LG]Optimizing Shallow Networks for Binary Classification
    Kalliopi Basioti, George V. Moustakides
    http://arxiv.org/abs/1905.10161v1

    • [cs.LG]Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
    Sharan Vaswani, Aaron Mishkin, Issam Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
    http://arxiv.org/abs/1905.09997v1

    • [cs.LG]Partially Encrypted Machine Learning using Functional Encryption
    Theo Ryffel, Edouard Dufour Sans, Romain Gay, Francis Bach, David Pointcheval
    http://arxiv.org/abs/1905.10214v1

    • [cs.LG]Perturbed Model Validation: A New Framework to Validate Model Relevance
    Jie M. Zhang, Earl T. Barr, Benjamin Guedj, Mark Harman, John Shawe-Taylor
    http://arxiv.org/abs/1905.10201v1

    • [cs.LG]Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering
    Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi
    http://arxiv.org/abs/1905.10029v1

    • [cs.LG]Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective
    Changjian Li, Krzysztof Czarnecki
    http://arxiv.org/abs/1905.10016v1

    • [cs.LG]Robust Attribution Regularization
    Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha
    http://arxiv.org/abs/1905.09957v1

    • [cs.LG]SCRAM: Spatially Coherent Randomized Attention Maps
    Dan A. Calian, Peter Roelants, Jacques Cali, Ben Carr, Krishna Dubba, John E. Reid, Dell Zhang
    http://arxiv.org/abs/1905.10308v1

    • [cs.LG]STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
    Lei Bai, Lina Yao, Salil. S Kanhere, Xianzhi Wang, Quan. Z Sheng
    http://arxiv.org/abs/1905.10069v1

    • [cs.LG]Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks
    Dominik Alfke, Martin Stoll
    http://arxiv.org/abs/1905.10224v1

    • [cs.LG]Statistical embedding for directed graphs
    Thorben Funke, Tian Guo, Alen Lancic, Nino Antulov-Fantulin
    http://arxiv.org/abs/1905.10227v1

    • [cs.LG]Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks
    Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, Gu-Yeon Wei
    http://arxiv.org/abs/1905.10138v1

    • [cs.LG]Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
    Boris Muzellec, Marco Cuturi
    http://arxiv.org/abs/1905.10099v1

    • [cs.LG]The advantages of multiple classes for reducing overfitting from test set reuse
    Vitaly Feldman, Roy Frostig, Moritz Hardt
    http://arxiv.org/abs/1905.10360v1

    • [cs.LG]Training decision trees as replacement for convolution layers
    Wolfgang Fuhl, Gjergji Kasneci, Wolfgang Rosenstiel, Enkelejda Kasneci
    http://arxiv.org/abs/1905.10073v1

    • [cs.LG]What Can ResNet Learn Efficiently, Going Beyond Kernels?
    Zeyuan Allen-Zhu, Yuanzhi Li
    http://arxiv.org/abs/1905.10337v1

    • [cs.LG]X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations
    Alberto Marchisio, Beatrice Bussolino, Alessio Colucci, Muhammad Abdullah Hanif, Maurizio Martina, Guido Masera, Muhammad Shafique
    http://arxiv.org/abs/1905.10142v1

    • [cs.MA]Decentralized Informative Path Planning with Exploration-Exploitation Balance for Swarm Robotic Search
    Payam Ghassemi, Souma Chowdhury
    http://arxiv.org/abs/1905.09988v1

    • [cs.MA]winPIBT: Expanded Prioritized Algorithm for Iterative Multi-agent Path Finding
    Keisuke Okumura, Yasumasa Tamura, Xavier Défago
    http://arxiv.org/abs/1905.10149v1

    • [cs.NE]Instruction-Level Design of Local Optimisers using Push GP
    Michael Lones
    http://arxiv.org/abs/1905.10245v1

    • [cs.RO]Designing an Inertia Actuator with a Fast Rotating Gyro inside an Egg-shaped Robot
    Chun-Chi Wang, He-Zhi Liu, Rui-Yuan Lin, Li-Yang Lu, N. Michael Mayer
    http://arxiv.org/abs/1905.10134v1

    • [cs.RO]Mechatronic Design of a Dribbling System for RoboCup Small Size Robot
    Zheyuan Huang, Yunkai Wang, Lingyun Chen, Jiacheng Li, Zexi Chen, Rong Xiong
    http://arxiv.org/abs/1905.09934v1

    • [cs.RO]Scene Induced Multi-Modal Trajectory Forecasting via Planning
    Nachiket Deo, Mohan M. Trivedi
    http://arxiv.org/abs/1905.09949v1

    • [cs.RO]Visual Model-predictive Localization for Computationally Efficient Autonomous Racing of a 72-gram Drone
    Shuo Li, Erik van der Horst, Philipp Duernay, Christophe De Wagter, Guido C. H. E. de Croon
    http://arxiv.org/abs/1905.10110v1

    • [cs.SD]Disentangled Feature for Weakly Supervised Multi-class Sound Event Detection
    Liwei Lin, Xiangdong Wang, Hong Liu, Yueliang Qian
    http://arxiv.org/abs/1905.10091v1

    • [cs.SE]A Customised App to Attract Female Teenagers to Coding
    Bernadette Spieler, Wolfgang Slany
    http://arxiv.org/abs/1905.10065v1

    • [cs.SI]An Integrated Model for User Innovation Knowledge Based on Super-network
    Xiao Liao, Zhihong Li, Yunjiang Xi, Haibo Wang, Kenneth Zantow
    http://arxiv.org/abs/1905.09923v1

    • [cs.SI]Extended Scale-Free Networks
    Arthur Charpentier, Emmanuel Flachaire
    http://arxiv.org/abs/1905.10267v1

    • [cs.SI]Multifaceted Privacy: How to Express Your Online Persona without Revealing Your Sensitive Attributes
    Victor Zakhary, Ishani Gupta, Rey Tang, Amr El Abbadi
    http://arxiv.org/abs/1905.09945v1

    • [cs.SI]Tempus Volat, Hora Fugit — A Survey of Dynamic Network Models in Discrete and Continuous Time
    Cornelius Fritz, Michael Lebacher, Göran Kauermann
    http://arxiv.org/abs/1905.10351v1

    • [econ.EM]Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
    Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis
    http://arxiv.org/abs/1905.10176v1

    • [econ.EM]Semi-Parametric Efficient Policy Learning with Continuous Actions
    Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov
    http://arxiv.org/abs/1905.10116v1

    • [eess.IV]A Research and Strategy of Remote Sensing Image Denoising Algorithms
    Ling Li, Junxing Hu, Fengge Wu, Junsuo Zhao
    http://arxiv.org/abs/1905.10236v1

    • [eess.IV]Functional Segmentation through Dynamic Mode Decomposition: Automatic Quantification of Kidney Function in DCE-MRI Images
    Santosh Tirunagari, Norman Poh, Kevin Wells, Miroslaw Bober, Isky Gorden, David Windridge
    http://arxiv.org/abs/1905.10218v1

    • [eess.IV]Tissue segmentation with deep 3D networks and spatial priors
    Lukas Hirsch, Yu Huang, Lucas C Parra
    http://arxiv.org/abs/1905.10010v1

    • [math.CO]A concatenation construction for propelinear perfect codes from regular subgroups of GA(r,2)
    I. Yu. Mogilnykh, F. I. Solov’eva
    http://arxiv.org/abs/1905.10005v1

    • [math.PR]Revisiting Relations between Stochastic Ageing and Dependence for Exchangeable Lifetimes with an Extension for the IFRA/DFRA Property
    Giovanna Nappo, Fabio L. Spizzichino
    http://arxiv.org/abs/1905.10326v1

    • [math.PR]The Skipping Sampler: A new approach to sample from complex conditional densities
    John Moriarty, Jure Vogrinc, Alessandro Zocca
    http://arxiv.org/abs/1905.09964v1

    • [math.ST]Asymptotic Behaviour of Discretised Functionals of Long-Range Dependent Functional Data
    Tareq Alodat, Andriy Olenko
    http://arxiv.org/abs/1905.10030v1

    • [math.ST]High-Dimensional Functional Factor Models
    Gilles Nisol, Shahin Tavakoli, Marc Hallin
    http://arxiv.org/abs/1905.10325v1

    • [math.ST]Likelihood ratio tests for many groups in high dimensions
    Holger Dette, Nina Dörnemann
    http://arxiv.org/abs/1905.10354v1

    • [math.ST]Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator
    Weixin Cai, Mark van der Laan
    http://arxiv.org/abs/1905.10299v1

    • [physics.soc-ph]Morphological organization of point-to-point transport in complex networks
    Min-Yeong Kang, Geoffroy Berthelot, Liubov Tupikina, Christos Nicolaides, Jean-Francois Colonna, Bernard Sapoval, Denis S. Grebenkov
    http://arxiv.org/abs/1905.10333v1

    • [q-bio.NC]Damped oscillations of the probability of random events followed by absolute refractory period
    A. V. Paraskevov, A. S. Minkin
    http://arxiv.org/abs/1905.10172v1

    • [stat.AP]Inference of Dynamic Graph Changes for Functional Connectome
    Dingjue Ji, Junwei Lu, Yiliang Zhang, Hongyu Zhao, Siyuan Gao
    http://arxiv.org/abs/1905.09993v1

    • [stat.AP]The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling
    Lauren Kennedy, Daniel Simpson, Andrew Gelman
    http://arxiv.org/abs/1905.10341v1

    • [stat.CO]A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler
    Alessandro Varsi, Lykourgos Kekempanos, Jeyarajan Thiyagalingam, Simon Maskell
    http://arxiv.org/abs/1905.10252v1

    • [stat.CO]Divide-and-Conquer Information-Based Optimal Subdata Selection Algorithm
    HaiYing Wang
    http://arxiv.org/abs/1905.09948v1

    • [stat.CO]Estimating Convergence of Markov chains with L-Lag Couplings
    Niloy Biswas, Pierre E. Jacob
    http://arxiv.org/abs/1905.09971v1

    • [stat.CO]Monitoring dynamic networks: a simulation-based strategy for comparing monitoring methods and a comparative study
    Lisha Yu, Inez M. Zwetsloot, Nathaniel T. Stevens, James D. Wilson, Kwok Leung Tsui
    http://arxiv.org/abs/1905.10302v1

    • [stat.CO]Parallel Coordinate Order for High-Dimensional Data
    Shaima Tilouche, Vahid Partovi Nia, Samuel Bassetto
    http://arxiv.org/abs/1905.10035v1

    • [stat.ME]Adaptive Function-on-Scalar Regression with a Smoothing Elastic Net
    Ardalan Mirshani, Matthew Reimherr
    http://arxiv.org/abs/1905.09881v1

    • [stat.ME]Optimal nonparametric change point detection and localization
    Oscar Hernan Madrid Padilla, Yi Yu, Daren Wang, Alessandro Rinaldo
    http://arxiv.org/abs/1905.10019v1

    • [stat.ML]Concentration bounds for linear Monge mapping estimation and optimal transport domain adaptation
    Rémi Flamary, Karim Lounici, André Ferrari
    http://arxiv.org/abs/1905.10155v1

    • [stat.ML]Convergence Guarantees for Adaptive Bayesian Quadrature Methods
    Motonobu Kanagawa, Philipp Hennig
    http://arxiv.org/abs/1905.10271v1

    • [stat.ML]Dirac Delta Regression: Conditional Density Estimation with Clinical Trials
    Eric V. Strobl, Shyam Visweswaran
    http://arxiv.org/abs/1905.10330v1

    • [stat.ML]OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits
    Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett
    http://arxiv.org/abs/1905.10040v1

    • [stat.ML]Polynomial Cost of Adaptation for X -Armed Bandits
    Hédi Hadiji
    http://arxiv.org/abs/1905.10221v1

    • [stat.ML]Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models
    Chiao-Yu Yang, Nhat Ho, Michael I. Jordan
    http://arxiv.org/abs/1905.09959v1

    • [stat.ML]Privacy Risks of Securing Machine Learning Models against Adversarial Examples
    Liwei Song, Reza Shokri, Prateek Mittal
    http://arxiv.org/abs/1905.10291v1

    • [stat.ML]Sequential Gaussian Processes for Online Learning of Nonstationary Functions
    Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt
    http://arxiv.org/abs/1905.10003v1

    • [stat.ML]Sliced Gromov-Wasserstein
    Titouan Vayer, Rémi Flamary, Romain Tavenard, Laetitia Chapel, Nicolas Courty
    http://arxiv.org/abs/1905.10124v1

    • [stat.ML]forgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature extraction
    Yunchuan Kong, Tianwei Yu
    http://arxiv.org/abs/1905.09889v1