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Sequential Latent Spaces for Modeling the Intention During Diverse Image CaptioningAug 22 2019Diverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent ... More
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision ProcessesAug 22 2019Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. We consider for the first time the semiparametric efficiency limits ... More
Transfer Learning for Relation Extraction via Relation-Gated Adversarial LearningAug 22 2019Relation extraction aims to extract relational facts from sentences. Previous models mainly rely on manually labeled datasets, seed instances or human-crafted patterns, and distant supervision. However, the human annotation is expensive, while human-crafted ... More
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health ForumsAug 22 2019Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. The changing nature of ... More
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithmsAug 22 2019One of the challenging questions in time series forecasting is how to find the best algorithm. In recent years, a recommender system scheme has been developed for time series analysis using a meta-learning approach. This system selects the best forecasting ... More
Iterative Hard Thresholding for Low CP-rank Tensor ModelsAug 22 2019Recovery of low-rank matrices from a small number of linear measurements is now well-known to be possible under various model assumptions on the measurements. Such results demonstrate robustness and are backed with provable theoretical guarantees. However, ... More
Data Context Adaptation for Accurate Recommendation with Additional InformationAug 22 2019Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities? Many previous studies proved that utilizing the additional information with rating data ... More
A new measure of modularity density for community detectionAug 22 2019Using an intuitive concept of what constitutes a meaningful community, a novel metric is formulated for detecting non-overlapping communities in undirected, weighted heterogeneous networks. This metric, modularity density, is shown to be superior to the ... More
Efficient Cross-Validation of Echo State NetworksAug 22 2019Echo State Networks (ESNs) are known for their fast and precise one-shot learning of time series. But they often need good hyper-parameter tuning for best performance. For this good validation is key, but usually, a single validation split is used. In ... More
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum OptimizationAug 22 2019This paper studies the lower bound complexity for the optimization problem whose objective function is the average of $n$ individual smooth convex functions. We consider the algorithm which gets access to gradient and proximal oracle for each individual ... More
Practical Risk Measures in Reinforcement LearningAug 22 2019Practical application of Reinforcement Learning (RL) often involves risk considerations. We study a generalized approximation scheme for risk measures, based on Monte-Carlo simulations, where the risk measures need not necessarily be \emph{coherent}. ... More
A General Data Renewal Model for Prediction Algorithms in Industrial Data AnalyticsAug 22 2019In industrial data analytics, one of the fundamental problems is to utilize the temporal correlation of the industrial data to make timely predictions in the production process, such as fault prediction and yield prediction. However, the traditional prediction ... More
The compositionality of neural networks: integrating symbolism and connectionismAug 22 2019Despite a multitude of empirical studies, little consensus exists on whether neural networks are able to generalise compositionally, a controversy that, in part, stems from a lack of agreement about what it means for a neural model to be compositional. ... More
LoRAS: An oversampling approach for imbalanced datasetsAug 22 2019The Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets.It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the ... More
An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated LearningAug 22 2019Federated learning is a distributed learning method to train a shared model by aggregating the locally-computed gradient updates. In federated learning, bandwidth and privacy are two main concerns of gradient updates transmission. This paper proposes ... More
The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum EntropyAug 22 2019Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. ... More
LEAP nets for power grid perturbationsAug 22 2019We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. We call our architeture LEAP net, ... More
Learning stochastic differential equations using RNN with log signature featuresAug 22 2019This paper contributes to the challenge of learning a function on streamed multimodal data through evaluation. The core of the result of our paper is the combination of two quite different approaches to this problem. One comes from the mathematically ... More
Block Randomized Optimization for Adaptive Hypergraph LearningAug 22 2019The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizations ... More
Adaptive Configuration Oracle for Online Portfolio Selection MethodsAug 22 2019Financial markets are complex environments that produce enormous amounts of noisy and non-stationary data. One fundamental problem is online portfolio selection, the goal of which is to exploit this data to sequentially select portfolios of assets to ... More
NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local OperationsAug 22 2019Road extraction from very high resolution satellite images is one of the most important topics in the field of remote sensing. For the road segmentation problem, spatial properties of the data can usually be captured using Convolutional Neural Networks. ... More
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste InspectionAug 22 2019Surface mount technology (SMT) is a process for producing printed circuit boards. Solder paste printer (SPP), package mounter, and solder reflow oven are used for SMT. The board on which the solder paste is deposited from the SPP is monitored by solder ... More
Finite Precision Stochastic Optimisation -- Accounting for the BiasAug 22 2019We consider first order stochastic optimization where the oracle must quantize each subgradient estimate to $r$ bits. We treat two oracle models: the first where the Euclidean norm of the oracle output is almost surely bounded and the second where it ... More
Semi-supervised Adversarial Active Learning on Attributed GraphsAug 22 2019Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label sparsity issues with the conventional independent and identically distributed ... More
A Neural Network for Semi-Supervised Learning on ManifoldsAug 21 2019Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold. However, an explicit graph representation is problematic for neural networks operating in the online setting. Here, we propose a feed-forward ... More
Transferability and Hardness of Supervised Classification TasksAug 21 2019We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does not require or assume trained models. Instead, we estimate these values ... More
BRIDGE: Byzantine-resilient Decentralized Gradient DescentAug 21 2019Decentralized optimization techniques are increasingly being used to learn machine learning models from data distributed over multiple locations without gathering the data at any one location. Unfortunately, methods that are designed for faultless networks ... More
Dynamic Scheduling of MPI-based Distributed Deep Learning Training JobsAug 21 2019There is a general trend towards solving problems suited to deep learning with more complex deep learning architectures trained on larger training sets. This requires longer compute times and greater data parallelization or model parallelization. Both ... More
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance EstimatesAug 21 2019We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution. We ... More
Testing Robustness Against Unforeseen AdversariesAug 21 2019Considerable work on adversarial defense has studied robustness to a fixed, known family of adversarial distortions, most frequently L_p-bounded distortions. In reality, the specific form of attack will rarely be known and adversaries are free to employ ... More
A tree-based radial basis function method for noisy parallel surrogate optimizationAug 21 2019Parallel surrogate optimization algorithms have proven to be efficient methods for solving expensive noisy optimization problems. In this work we develop a new parallel surrogate optimization algorithm (ProSRS), using a novel tree-based "zoom strategy" ... More
QCNN: Quantile Convolutional Neural NetworkAug 21 2019A dilated causal one-dimensional convolutional neural network architecture is proposed for quantile regression. The model can forecast any arbitrary quantile, and it can be trained jointly on multiple similar time series. An application to Value at Risk ... More
Estimation of perceptual scales using ordinal embeddingAug 21 2019In this paper, we address the problem of measuring and analysing sensation, the subjective magnitude of one's experience. We do this in the context of the method of triads: the sensation of the stimulus is evaluated via relative judgments of the form: ... More
Representation Disentanglement for Multi-task Learning with application to Fetal UltrasoundAug 21 2019One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the ... More
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy ProtectionAug 21 2019In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. Theoretically, we prove that a differentially private learning algorithm used for training the GAN ... More
Federated Learning: Challenges, Methods, and Future DirectionsAug 21 2019Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges ... More
Exploring Offline Policy Evaluation for the Continuous-Armed Bandit ProblemAug 21 2019The (contextual) multi-armed bandit problem (MAB) provides a formalization of sequential decision-making which has many applications. However, validly evaluating MAB policies is challenging; we either resort to simulations which inherently include debatable ... More
Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial predictionAug 21 2019Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is ... More
Minimum Description Length RevisitedAug 21 2019This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in statistics, machine learning and pattern recognition. While MDL was originally ... More
Decentralized Federated Learning: A Segmented Gossip ApproachAug 21 2019The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data. Conventional federated learning architecture, inherited ... More
Data-driven model reduction, Wiener projections, and the Mori-Zwanzig formalismAug 21 2019First-principles models of complex dynamic phenomena often have many degrees of freedom, only a small fraction of which may be scientifically relevant or observable. Reduced models distill such phenomena to their essence by modeling only relevant variables, ... More
Hebbian Graph EmbeddingsAug 21 2019Representation learning has recently been successfully used to create vector representations of entities in language learning, recommender systems and in similarity learning. Graph embeddings exploit the locality structure of a graph and generate embeddings ... More
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial AttacksAug 21 2019Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by adding maliciously crafted perturbations to the benign inputs. As a growing ... More
Saccader: Improving Accuracy of Hard Attention Models for VisionAug 20 2019Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, they are often regarded as black boxes. Because they compute a nonlinear function of the entire input image, their decisions ... More
AdaCliP: Adaptive Clipping for Private SGDAug 20 2019Privacy preserving machine learning algorithms are crucial for learning models over user data to protect sensitive information. Motivated by this, differentially private stochastic gradient descent (SGD) algorithms for training machine learning models ... More
How to gamble with non-stationary $\mathcal{X}$-armed bandits and have no regretsAug 20 2019In $\mathcal{X}$-armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem ... More
How to gamble with non-stationary $\mathcal{X}$-armed bandits and have no regretsAug 20 2019Aug 22 2019In $\mathcal{X}$-armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem ... More
Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient DescentAug 20 2019Stochastic Gradient Descent (SGD) methods are prominent for training machine learning and deep learning models. The performance of these techniques depends on their hyperparameter tuning over time and varies for different models and problems. Manual adjustment ... More
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentationAug 20 2019Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. ... More
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel AccessAug 20 2019To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt ... More
Developing Creative AI to Generate Sculptural ObjectsAug 20 2019We explore the intersection of human and machine creativity by generating sculptural objects through machine learning. This research raises questions about both the technical details of automatic art generation and the interaction between AI and people, ... More
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher ProcessesAug 20 2019The developments of Rademacher complexity and PAC-Bayesian theory have been largely independent. One exception is the PAC-Bayes theorem of Kakade, Sridharan, and Tewari (2008), which is established via Rademacher complexity theory by viewing Gibbs classifiers ... More
Robust Graph Neural Network Against Poisoning Attacks via Transfer LearningAug 20 2019Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable modifications on graph topology or nodal features can significantly reduce ... More
Image Synthesis From Reconfigurable Layout and StyleAug 20 2019Despite remarkable recent progress on both unconditional and conditional image synthesis, it remains a long-standing problem to learn generative models that are capable of synthesizing realistic and sharp images from reconfigurable spatial layout (i.e., ... More
Hotel Recommendation SystemAug 20 2019One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these situations, we ... More
Hotel Recommendation SystemAug 20 2019Aug 21 2019One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these situations, we ... More
Sensor-Based Estimation of Dim Light Melatonin Onset (DLMO) Using Features of Two Time ScalesAug 20 2019Circadian rhythms govern most essential biological processes in the human body; they influence multiple biological activities including sleep, performance, mood, skin temperature, hormone production, and nutrient absorption. The dim light melatonin onset ... More
On Analog Gradient Descent Learning over Multiple Access Fading ChannelsAug 20 2019We consider a distributed learning problem over multiple access channel (MAC) using a large wireless network. The computation is made by the network edge and is based on received data from a large number of distributed nodes which transmit over a noisy ... More
TabNet: Attentive Interpretable Tabular LearningAug 20 2019We propose a novel high-performance interpretable deep tabular data learning network, TabNet. TabNet utilizes a sequential attention mechanism to choose which features to reason from at each decision step and then aggregates the processed information ... More
Towards Effective Device-Aware Federated LearningAug 20 2019With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security. To address the above issues, ... More
Sarcasm Detection using Hybrid Neural NetworkAug 20 2019Sarcasm Detection has enjoyed great interest from the research community, however the task of predicting sarcasm in a text remains an elusive problem for machines. Past studies mostly make use of twitter datasets collected using hashtag based supervision ... More
Expected path length on random manifoldsAug 20 2019Manifold learning seeks a low dimensional representation that faithfully captures the essence of data. Current methods can successfully learn such representations, but do not provide a meaningful set of operations that are associated with the representation. ... More
Hierarchical Bayesian Personalized Recommendation: A Case Study and BeyondAug 20 2019Items in modern recommender systems are often organized in hierarchical structures. These hierarchical structures and the data within them provide valuable information for building personalized recommendation systems. In this paper, we propose a general ... More
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networksAug 20 2019Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented ... More
Investigation of wind pressures on tall building under interference effects using machine learning techniquesAug 20 2019Interference effects of tall buildings have attracted numerous studies due to the boom of clusters of tall buildings in megacities. To fully understand the interference effects of buildings, it often requires a substantial amount of wind tunnel tests. ... More
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True ErrorAug 20 2019As deep learning applications are becoming more and more pervasive in robotics, the question of evaluating the reliability of inferences becomes a central question in the robotics community. This domain, known as predictive uncertainty, has come under ... More
Density estimation in representation space to predict model uncertaintyAug 20 2019Deep learning models frequently make incorrect predictions with high confidence when presented with test examples that are not well represented in their training dataset. We propose a novel and straightforward approach to estimate prediction uncertainty ... More
A Noise-Robust Fast Sparse Bayesian Learning ModelAug 20 2019This paper utilizes the hierarchical model structure from the Bayesian Lasso in the Sparse Bayesian Learning process to develop a new type of probabilistic supervised learning approach. This approach has several performance advantages, such as being fast, ... More
Counterfactual Distribution Regression for Structured InferenceAug 20 2019We consider problems in which a system receives external \emph{perturbations} from time to time. For instance, the system can be a train network in which particular lines are repeatedly disrupted without warning, having an effect on passenger behavior. ... More
Compliance Change Tracking in Business Process ServicesAug 20 2019Regulatory compliance is an organization's adherence to laws, regulations, guidelines and specifications relevant to its business. Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes ... More
Similarity Learning for Authorship Verification in Social MediaAug 20 2019Authorship verification tries to answer the question if two documents with unknown authors were written by the same author or not. A range of successful technical approaches has been proposed for this task, many of which are based on traditional linguistic ... More
A Review of Changepoint Detection ModelsAug 20 2019The objective of the change-point detection is to discover the abrupt property changes lying behind the time-series data. In this paper, we firstly summarize the definition and in-depth implication of the changepoint detection. The next stage is to elaborate ... More
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic DefensesAug 20 2019Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on model generalizability and new threats such as prediction-evasive misclassification ... More
Graph Neural Networks with High-order Feature InteractionsAug 19 2019Network representation learning, a fundamental research problem which aims at learning low-dimension node representations on graph-structured data, has been extensively studied in the research community. By generalizing the power of neural networks on ... More
Fuzzy C-Means Clustering and Sonification of HRV FeaturesAug 19 2019Linear and non-linear measures of heart rate variability (HRV) are widely investigated as non-invasive indicators of health. Stress has a profound impact on heart rate, and different meditation techniques have been found to modulate heartbeat rhythm. ... More
Real-time Person Re-identification at the Edge: A Mixed Precision ApproachAug 19 2019A critical part of multi-person multi-camera tracking is person re-identification (re-ID) algorithm, which recognizes and retains identities of all detected unknown people throughout the video stream. Many re-ID algorithms today exemplify state of the ... More
Alliances and Conflict, or Conflict and Alliances? Appraising the Causal Effect of Alliances on ConflictAug 19 2019The deterrent effect of military alliances is well documented and widely accepted. However, such work has typically assumed that alliances are exogenous. This is problematic as alliances may simultaneously influence the probability of conflict and be ... More
Semi-Implicit Graph Variational Auto-EncodersAug 19 2019Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a hierarchical variational framework to enable neighboring node sharing for better ... More
Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction EstimationAug 19 2019An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction ... More
Twitter Sentiment on Affordable Care Act using Score EmbeddingAug 19 2019In this paper we introduce score embedding, a neural network based model to learn interpretable vector representations for words. Score embedding is a supervised method that takes advantage of the labeled training data and the neural network architecture ... More
CUDA optimized Neural Network predicts blood glucose control from quantified joint mobility and anthropometricsAug 19 2019Neural network training entails heavy computation with obvious bottlenecks. The Compute Unified Device Architecture (CUDA) programming model allows us to accelerate computation by passing the processing workload from the CPU to the graphics processing ... More
Semi-supervised Sequence Modeling for Elastic Impedance InversionAug 19 2019Recent applications of machine learning algorithms in the seismic domain have shown great potential in different areas such as seismic inversion and interpretation. However, such algorithms rarely enforce geophysical constraints - the lack of which might ... More
Evaluating Hierarchies through A Partially Observable Markov Decision Processes MethodologyAug 19 2019Hierarchical clustering has been shown to be valuable in many scenarios, e.g. catalogues, biology research, image processing, and so on. Despite its usefulness to many situations, there is no agreed methodology on how to properly evaluate the hierarchies ... More
Topic Augmented Generator for Abstractive SummarizationAug 19 2019Steady progress has been made in abstractive summarization with attention-based sequence-to-sequence learning models. In this paper, we propose a new decoder where the output summary is generated by conditioning on both the input text and the latent topics ... More
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex OptimizationAug 19 2019Recent years have seen increased interest in performance guarantees of gradient descent algorithms for non-convex optimization. A number of works have uncovered that gradient noise plays a critical role in the ability of gradient descent recursions to ... More
Learning Fair Classifiers in Online Stochastic SettingsAug 19 2019In many real life situations, including job and loan applications, gatekeepers must make justified, real-time decisions about a person's fitness for a particular opportunity. People on both sides of such decisions have understandable concerns about their ... More
On Regularization Properties of Artificial Datasets for Deep LearningAug 19 2019The paper discusses regularization properties of artificial data for deep learning. Artificial datasets allow to train neural networks in the case of a real data shortage. It is demonstrated that the artificial data generation process, described as injecting ... More
Gradient Boosting Machine: A SurveyAug 19 2019In this survey, we discuss several different types of gradient boosting algorithms and illustrate their mathematical frameworks in detail: 1. introduction of gradient boosting leads to 2. objective function optimization, 3. loss function estimations, ... More
Consistent Community Detection in Continuous-Time Networks of Relational EventsAug 19 2019In many application settings involving networks, such as messages between users of an on-line social network or transactions between traders in financial markets, the observed data are in the form of relational events with timestamps, which form a continuous-time ... More
A new asymmetric $ε$-insensitive pinball loss function based support vector quantile regression modelAug 19 2019In this paper, we propose a novel asymmetric $\epsilon$-insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate the $\epsilon$-insensitive zone approach in it but, they fail to extend ... More
Probability Estimation with Truncated Inverse Binomial SamplingAug 19 2019In this paper, we develop a general theory of truncated inverse binomial sampling. In this theory, the fixed-size sampling and inverse binomial sampling are accommodated as special cases. In particular, the classical Chernoff-Hoeffding bound is an immediate ... More
Gradient Methods for Solving Stackelberg GamesAug 19 2019Stackelberg Games are gaining importance in the last years due to the raise of Adversarial Machine Learning (AML). Within this context, a new paradigm must be faced: in classical game theory, intervening agents were humans whose decisions are generally ... More
Efficient Discovery of Expressive Multi-label Rules using Relaxed PruningAug 19 2019Being able to model correlations between labels is considered crucial in multi-label classification. Rule-based models enable to expose such dependencies, e.g., implications, subsumptions, or exclusions, in an interpretable and human-comprehensible manner. ... More
Across-Stack Profiling and Characterization of Machine Learning Models on GPUsAug 19 2019The world sees a proliferation of machine learning/deep learning (ML) models and their wide adoption in different application domains recently. This has made the profiling and characterization of ML models an increasingly pressing task for both hardware ... More
SIRUS: making random forests interpretableAug 19 2019State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism. This lack of interpretability is a strong ... More
The efficacy of various machine learning models for multi-class classification of RNA-seq expression dataAug 19 2019Late diagnosis and high costs are key factors that negatively impact the care of cancer patients worldwide. Although the availability of biological markers for the diagnosis of cancer type is increasing, costs and reliability of tests currently present ... More
PAC-Bayes with BackpropAug 19 2019Aug 21 2019We explore a method to train probabilistic neural networks by minimizing risk upper bounds, specifically, PAC-Bayes bounds. Thus randomization is not just part of a proof strategy, but part of the learning algorithm itself. We derive two training objectives, ... More
PAC-Bayes with BackpropAug 19 2019We explore a method to train probabilistic neural networks by minimizing risk upper bounds, specifically, PAC-Bayes bounds. Thus randomization is not just part of a proof strategy, but part of the learning algorithm itself. We derive two training objectives, ... More
Quantum algorithms for Second-Order Cone Programming and Support Vector MachinesAug 19 2019Second order cone programs (SOCPs) are a class of structured convex optimization problems that generalize linear programs. We present a quantum algorithm for second order cone programs (SOCPs) based on a quantum variant of the interior point method. Our ... More
Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible SparsityAug 19 2019Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. Among various clustering approaches, the family of K-Means algorithms gains popularity due to simplicity and efficiency. However, most of existing ... More