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Title: PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting Abstract: The complex spatial-temporal correlations in transportation networks make the traffic forecasting problem challenging. Since transportation system inherently possesses graph structures, much research efforts have be... |
Title: Out of Distribution Data Detection Using Dropout Bayesian Neural Networks Abstract: We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how previous attempts to leverage the randomized embeddi... |
Title: KINet: Keypoint Interaction Networks for Unsupervised Forward Modeling Abstract: Object-centric representation is an essential abstraction for physical reasoning and forward prediction. Most existing approaches learn this representation through extensive supervision (e.g., object class and bounding box) although... |
Title: On Variance Estimation of Random Forests Abstract: Ensemble methods, such as random forests, are popular in applications due to their high predictive accuracy. Existing literature views a random forest prediction as an infinite-order incomplete U-statistic to quantify its uncertainty. However, these methods focu... |
Title: DARL1N: Distributed multi-Agent Reinforcement Learning with One-hop Neighbors Abstract: Most existing multi-agent reinforcement learning (MARL) methods are limited in the scale of problems they can handle. Particularly, with the increase of the number of agents, their training costs grow exponentially. In this p... |
Title: Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT) Abstract: Revealing the hidden patterns shaping the urban environment is essential to understand its dynamics and to make cities smarter. Recent studies have demonstrated that learning the representations of ... |
Title: Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction Abstract: Graph neural networks (GNNs) have drawn significant research attention recently, mostly under the setting of semi-supervised learning. When task-agnostic representations are preferred or supervision is simply unavailable, the auto-encoder f... |
Title: On the Implicit Bias Towards Minimal Depth of Deep Neural Networks Abstract: Recent results in the literature suggest that the penultimate layer representations of neural networks that are trained for classification exhibit a clustering property called neural collapse (NC). We study the implicit bias of stochast... |
Title: Adaptivity and Confounding in Multi-Armed Bandit Experiments Abstract: We explore a new model of bandit experiments where a potentially nonstationary sequence of contexts influences arms' performance. Context-unaware algorithms risk confounding while those that perform correct inference face information delays. ... |
Title: Critical Checkpoints for Evaluating Defence Models Against Adversarial Attack and Robustness Abstract: From past couple of years there is a cycle of researchers proposing a defence model for adversaries in machine learning which is arguably defensible to most of the existing attacks in restricted condition (they... |
Title: Microplankton life histories revealed by holographic microscopy and deep learning Abstract: The marine microbial food web plays a central role in the global carbon cycle. Our mechanistic understanding of the ocean, however, is biased towards its larger constituents, while rates and biomass fluxes in the microbia... |
Title: Tackling benign nonconvexity with smoothing and stochastic gradients Abstract: Non-convex optimization problems are ubiquitous in machine learning, especially in Deep Learning. While such complex problems can often be successfully optimized in practice by using stochastic gradient descent (SGD), theoretical anal... |
Title: Interpolation and Regularization for Causal Learning Abstract: We study the problem of learning causal models from observational data through the lens of interpolation and its counterpart -- regularization. A large volume of recent theoretical, as well as empirical work, suggests that, in highly complex model cl... |
Title: Can Interpretable Reinforcement Learning Manage Assets Your Way? Abstract: Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers' needs and preferences. Whereas tr... |
Title: A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics Abstract: Parameter estimation in empirical fields is usually undertaken using parametric models, and such models readily facilitate statistical inference. Unfortunately, they are unlikely to be... |
Title: How to Manage Tiny Machine Learning at Scale: An Industrial Perspective Abstract: Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in real-time. To manage TinyML in the industry, where mas... |
Title: Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes Abstract: Computing the volume of a polytope in high dimensions is computationally challenging but has wide applications. Current state-of-the-art algorithms to compute such volumes rely on efficie... |
Title: Quantifying the Effects of Data Augmentation Abstract: We provide results that exactly quantify how data augmentation affects the convergence rate and variance of estimates. They lead to some unexpected findings: Contrary to common intuition, data augmentation may increase rather than decrease uncertainty of est... |
Title: Modelling the semantics of text in complex document layouts using graph transformer networks Abstract: Representing structured text from complex documents typically calls for different machine learning techniques, such as language models for paragraphs and convolutional neural networks (CNNs) for table extractio... |
Title: Generalizing Aggregation Functions in GNNs:High-Capacity GNNs via Nonlinear Neighborhood Aggregators Abstract: Graph neural networks (GNNs) have achieved great success in many graph learning tasks. The main aspect powering existing GNNs is the multi-layer network architecture to learn the nonlinear graph represe... |
Title: Gaussian Mixture Convolution Networks Abstract: This paper proposes a novel method for deep learning based on the analytical convolution of multidimensional Gaussian mixtures. In contrast to tensors, these do not suffer from the curse of dimensionality and allow for a compact representation, as data is only stor... |
Title: PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks Abstract: Federated learning is gaining popularity as a distributed machine learning method that can be used to deploy AI-dependent IoT applications while protecting client data privacy and security. Due to the differences of clients... |
Title: Linearization and Identification of Multiple-Attractors Dynamical System through Laplacian Eigenmaps Abstract: Dynamical Systems (DS) are fundamental to the modeling and understanding of time evolving phenomena, and find application in physics, biology and control. As determining an analytical description of the... |
Title: Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network Abstract: Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse archit... |
Title: Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images Abstract: High-dimensional imaging is becoming increasingly relevant in many fields from astronomy and cultural heritage to systems biology. Visual exploration of such high-dimensional data is commonly facilitated by dime... |
Title: Churn modeling of life insurance policies via statistical and machine learning methods -- Analysis of important features Abstract: Life assurance companies typically possess a wealth of data covering multiple systems and databases. These data are often used for analyzing the past and for describing the present. ... |
Title: Testing the boundaries: Normalizing Flows for higher dimensional data sets Abstract: Normalizing Flows (NFs) are emerging as a powerful class of generative models, as they not only allow for efficient sampling, but also deliver, by construction, density estimation. They are of great potential usage in High Energ... |
Title: A Review on Methods and Applications in Multimodal Deep Learning Abstract: Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years. The goal of multimodal deep learning (MMDL) is to create models that can process and link information using various modalities... |
Title: An Integrated Optimization and Machine Learning Models to Predict the Admission Status of Emergency Patients Abstract: This work proposes a framework for optimizing machine learning algorithms. The practicality of the framework is illustrated using an important case study from the healthcare domain, which is pre... |
Title: Deep-Learning Architectures for Multi-Pitch Estimation: Towards Reliable Evaluation Abstract: Extracting pitch information from music recordings is a challenging but important problem in music signal processing. Frame-wise transcription or multi-pitch estimation aims for detecting the simultaneous activity of pi... |
Title: Molecule Generation for Drug Design: a Graph Learning Perspective Abstract: Machine learning has revolutionized many fields, and graph learning is recently receiving increasing attention. From the application perspective, one of the emerging and attractive areas is aiding the design and discovery of molecules, e... |
Title: Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures Abstract: Kernel design for Multi-output Gaussian Processes (MOGP) has received increased attention recently. In particular, the Multi-Output Spectral Mixture kernel (MOSM) arXiv:1709.01298 approach has been praised as a general mod... |
Title: Transfer and Marginalize: Explaining Away Label Noise with Privileged Information Abstract: Supervised learning datasets often have privileged information, in the form of features which are available at training time but are not available at test time e.g. the ID of the annotator that provided the label. We argu... |
Title: Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automunge Abstract: Injecting gaussian noise into training features is well known to have regularization properties. This paper considers noise injections to numeric or categoric tabular features as passed to inference, which trans... |
Title: Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI Abstract: High-resolution medical images are beneficial for analysis but their acquisition may not always be feasible. Alternatively, high-resolution images can be created from low-resolution acquisitions using conventional ... |
Title: Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control Abstract: Fault-tolerant flight control faces challenges, as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample eff... |
Title: Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition? Abstract: Humans express their emotions via facial expressions, voice intonation and word choices. To infer the nature of the underlying emotion, recognition models may use a single modality, such as vision, audio, and text, or ... |
Title: Deep Movement Primitives: toward Breast Cancer Examination Robot Abstract: Breast cancer is the most common type of cancer worldwide. A robotic system performing autonomous breast palpation can make a significant impact on the related health sector worldwide. However, robot programming for breast palpating with ... |
Title: Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules Abstract: Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants. However, human road users usually do not adhere to these rules strictly, resulting in varying degrees of rule co... |
Title: Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks Abstract: Recent efforts in deep learning show a considerable advancement in redesigning deep learning models for low-resource and edge devices. The performance optimization of deep learning models are conducted either manually or thro... |
Title: Geometric Regularization from Overparameterization explains Double Descent and other findings Abstract: The volume of the distribution of possible weight configurations associated with a loss value may be the source of implicit regularization from overparameterization due to the phenomenon of contracting volume ... |
Title: (2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering Abstract: Spatio-temporal scene-graph approaches to video-based reasoning tasks, such as video question-answering (QA), typically construct such graphs for every video frame. These approaches often ignore the fact that videos are essentially sequ... |
Title: FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural Network Abstract: In recent years, deep learning approaches for partial differential equations have received much attention due to their mesh-freeness and other desirable properties. However, most of the works so far concentrat... |
Title: Surf or sleep? Understanding the influence of bedtime patterns on campus Abstract: Poor sleep habits may cause serious problems of mind and body, and it is a commonly observed issue for college students due to study workload as well as peer and social influence. Understanding its impact and identifying students ... |
Title: Amenable Sparse Network Investigator Abstract: As the optimization problem of pruning a neural network is nonconvex and the strategies are only guaranteed to find local solutions, a good initialization becomes paramount. To this end, we present the Amenable Sparse Network Investigator ASNI algorithm that learns ... |
Title: Towards a Numerical Proof of Turbulence Closure Abstract: The development of turbulence closure models, parametrizing the influence of small non-resolved scales on the dynamics of large resolved ones, is an outstanding theoretical challenge with vast applicative relevance. We present a closure, based on deep rec... |
Title: System Safety and Artificial Intelligence Abstract: This chapter formulates seven lessons for preventing harm in artificial intelligence (AI) systems based on insights from the field of system safety for software-based automation in safety-critical domains. New applications of AI across societal domains and publ... |
Title: tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices Abstract: Advances in low-power electronics and machine learning techniques lead to many novel wearable IoT devices. These devices have limited battery capacity and computational power. Thus, energy harves... |
Title: Exploring Adversarially Robust Training for Unsupervised Domain Adaptation Abstract: Unsupervised Domain Adaptation (UDA) methods aim to transfer knowledge from a labeled source domain to an unlabeled target domain. UDA has been extensively studied in the computer vision literature. Deep networks have been shown... |
Title: Masked prediction tasks: a parameter identifiability view Abstract: The vast majority of work in self-supervised learning, both theoretical and empirical (though mostly the latter), have largely focused on recovering good features for downstream tasks, with the definition of "good" often being intricately tied t... |
Title: Learning Predictions for Algorithms with Predictions Abstract: A burgeoning paradigm in algorithm design is the field of algorithms with predictions, in which algorithms are designed to take advantage of a possibly-imperfect prediction of some aspect of the problem. While much work has focused on using predictio... |
Title: Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder Abstract: In this paper, we present an unsupervised probabilistic model and associated estimation algorithm for multi-object tracking (MOT) based on a dynamical variational autoencoder (DVAE), called DVAE-UMOT. The DVAE is a latent-va... |
Title: DataMUX: Data Multiplexing for Neural Networks Abstract: In this paper, we introduce data multiplexing (DataMUX), a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representation. DataMUX demonstrates that neural networks are capable of generating accu... |
Title: Signal Decomposition Using Masked Proximal Operators Abstract: We consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We propose a simple and general framework in which the components are de... |
Title: Learning Physics-Informed Neural Networks without Stacked Back-propagation Abstract: Physics-Informed Neural Network (PINN) has become a commonly used machine learning approach to solve partial differential equations (PDE). But, facing high-dimensional second-order PDE problems, PINN will suffer from severe scal... |
Title: Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast Abstract: Deep learning has been a prevalence in computational chemistry and widely implemented in molecule property predictions. Recently, self-supervised learning (SSL), especially contrastive learning (CL)... |
Title: Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions Abstract: The mercury constitutive model predicting the strain and stress in the target vessel plays a central role in improving the lifetime prediction and future target designs of the m... |
Title: ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! Abstract: We introduce \algname{ProxSkip} -- a surprisingly simple and provably efficient method for minimizing the sum of a smooth ($f$) and an expensive nonsmooth proximable ($\psi$) function. The canonical approach to so... |
Title: Machine Learning Models in Stock Market Prediction Abstract: The paper focuses on predicting the Nifty 50 Index by using 8 Supervised Machine Learning Models. The techniques used for empirical study are Adaptive Boost (AdaBoost), k-Nearest Neighbors (kNN), Linear Regression (LR), Artificial Neural Network (ANN),... |
Title: Mixture-of-Experts with Expert Choice Routing Abstract: Sparsely-activated Mixture-of-experts (MoE) models allow the number of parameters to greatly increase while keeping the amount of computation for a given token or a given sample unchanged. However, a poor expert routing strategy (e.g. one resulting in load ... |
Title: Explaining, Evaluating and Enhancing Neural Networks' Learned Representations Abstract: Most efforts in interpretability in deep learning have focused on (1) extracting explanations of a specific downstream task in relation to the input features and (2) imposing constraints on the model, often at the expense of ... |
Title: Black-box Node Injection Attack for Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to vital fields that require high security standard such as product recommendation and traffic forecasting. Under such scenarios, exploiting G... |
Title: Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates Abstract: Differential privacy (DP) techniques can be applied to the federated learning model to statistically guarantee data privacy against inference attacks to communication among the learning agents. While ensuring strong ... |
Title: Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games Abstract: Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized training and policy sharing. Centralized training eliminates the issue of non-stationarity MARL yet induces large communication costs, ... |
Title: Learning Representations Robust to Group Shifts and Adversarial Examples Abstract: Despite the high performance achieved by deep neural networks on various tasks, extensive studies have demonstrated that small tweaks in the input could fail the model predictions. This issue of deep neural networks has led to a n... |
Title: Interactive Visual Pattern Search on Graph Data via Graph Representation Learning Abstract: Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in images. Identifying subgraph patterns in gr... |
Title: Towards Enabling Dynamic Convolution Neural Network Inference for Edge Intelligence Abstract: Deep learning applications have achieved great success in numerous real-world applications. Deep learning models, especially Convolution Neural Networks (CNN) are often prototyped using FPGA because it offers high power... |
Title: Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data Abstract: Panel data involving longitudinal measurements of the same set of participants taken over multiple time points is common in studies to understand childhood development and disease modeling. Deep hybrid models... |
Title: Attacks, Defenses, And Tools: A Framework To Facilitate Robust AI/ML Systems Abstract: Software systems are increasingly relying on Artificial Intelligence (AI) and Machine Learning (ML) components. The emerging popularity of AI techniques in various application domains attracts malicious actors and adversaries.... |
Title: Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation? Abstract: The backpropagation of error algorithm used to train deep neural networks has been fundamental to the successes of deep learning. However, it requires sequential backward updates and non-local computations, which make it chall... |
Title: Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents Abstract: Automated attack discovery techniques, such as attacker synthesis or model-based fuzzing, provide powerful ways to ensure network protocols operate correctly and securely. Such techniques, in general, r... |
Title: FedEmbed: Personalized Private Federated Learning Abstract: Federated learning enables the deployment of machine learning to problems for which centralized data collection is impractical. Adding differential privacy guarantees bounds on privacy while data are contributed to a global model. Adding personalization... |
Title: Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks Abstract: Uncertainty estimation in deep models is essential in many real-world applications and has benefited from developments over the last several years. Recent evidence suggests that existing solutions depen... |
Title: Reciprocity in Machine Learning Abstract: Machine learning is pervasive. It powers recommender systems such as Spotify, Instagram and YouTube, and health-care systems via models that predict sleep patterns, or the risk of disease. Individuals contribute data to these models and benefit from them. Are these contr... |
Title: TransDreamer: Reinforcement Learning with Transformer World Models Abstract: The Dreamer agent provides various benefits of Model-Based Reinforcement Learning (MBRL) such as sample efficiency, reusable knowledge, and safe planning. However, its world model and policy networks inherit the limitations of recurrent... |
Title: Missing Data Infill with Automunge Abstract: Missing data is a fundamental obstacle in the practice of data science. This paper surveys a few conventions for imputation as available in the Automunge open source python library platform for tabular data preprocessing, including "ML infill" in which auto ML models ... |
Title: Suitability of Different Metric Choices for Concept Drift Detection Abstract: The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment. Many unsupervised... |
Title: Shaping Advice in Deep Reinforcement Learning Abstract: Reinforcement learning involves agents interacting with an environment to complete tasks. When rewards provided by the environment are sparse, agents may not receive immediate feedback on the quality of actions that they take, thereby affecting learning of ... |
Title: Numeric Encoding Options with Automunge Abstract: Mainstream practice in machine learning with tabular data may take for granted that any feature engineering beyond scaling for numeric sets is superfluous in context of deep neural networks. This paper will offer arguments for potential benefits of extended encod... |
Title: Gradient Estimation with Discrete Stein Operators Abstract: Gradient estimation -- approximating the gradient of an expectation with respect to the parameters of a distribution -- is central to the solution of many machine learning problems. However, when the distribution is discrete, most common gradient estima... |
Title: Parsed Categoric Encodings with Automunge Abstract: The Automunge open source python library platform for tabular data pre-processing automates feature engineering data transformations of numerical encoding and missing data infill to received tidy data on bases fit to properties of columns in a designated train ... |
Title: From Quantum Graph Computing to Quantum Graph Learning: A Survey Abstract: Quantum computing (QC) is a new computational paradigm whose foundations relate to quantum physics. Notable progress has been made, driving the birth of a series of quantum-based algorithms that take advantage of quantum computational pow... |
Title: Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion Abstract: With the increasingly fierce market competition, offering a free trial has become a potent stimuli strategy to promote products and attract users. By providing users with opportunities to experience goods without charge, a fr... |
Title: PETCI: A Parallel English Translation Dataset of Chinese Idioms Abstract: Idioms are an important language phenomenon in Chinese, but idiom translation is notoriously hard. Current machine translation models perform poorly on idiom translation, while idioms are sparse in many translation datasets. We present PET... |
Title: Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training Abstract: Robust Reinforcement Learning (RL) focuses on improving performances under model errors or adversarial attacks, which facilitates the real-life deployment of RL agents. Robust Adversarial Reinforcement L... |
Title: Learning a Shield from Catastrophic Action Effects: Never Repeat the Same Mistake Abstract: Agents that operate in an unknown environment are bound to make mistakes while learning, including, at least occasionally, some that lead to catastrophic consequences. When humans make catastrophic mistakes, they are expe... |
Title: Deep Learning for Hate Speech Detection: A Comparative Study Abstract: Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. ... |
Title: Distributed Out-of-Memory NMF of Dense and Sparse Data on CPU/GPU Architectures with Automatic Model Selection for Exascale Data Abstract: The need for efficient and scalable big-data analytics methods is more essential than ever due to the exploding size and complexity of globally emerging datasets. Nonnegative... |
Title: The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness Abstract: Computer scientists are trained to create abstractions that simplify and generalize. However, a premature abstraction that omits crucial contextual details creates the risk of epistemic trespass... |
Title: Improving the Level of Autism Discrimination through GraphRNN Link Prediction Abstract: Dataset is the key of deep learning in Autism disease research. However, due to the few quantity and heterogeneity of samples in current dataset, for example ABIDE (Autism Brain Imaging Data Exchange), the recognition researc... |
Title: Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network Abstract: The ability to perceive object slip via tactile feedback enables humans to accomplish complex manipulation tasks including maintaining a stable grasp. Despite the utility of tactile information for many ... |
Title: Bit-wise Training of Neural Network Weights Abstract: We introduce an algorithm where the individual bits representing the weights of a neural network are learned. This method allows training weights with integer values on arbitrary bit-depths and naturally uncovers sparse networks, without additional constraint... |
Title: Image-to-Graph Transformers for Chemical Structure Recognition Abstract: For several decades, chemical knowledge has been published in written text, and there have been many attempts to make it accessible, for example, by transforming such natural language text to a structured format. Although the discovered che... |
Title: Polytopic Matrix Factorization: Determinant Maximization Based Criterion and Identifiability Abstract: We introduce Polytopic Matrix Factorization (PMF) as a novel data decomposition approach. In this new framework, we model input data as unknown linear transformations of some latent vectors drawn from a polytop... |
Title: Echofilter: A Deep Learning Segmentation Model Improves the Automation, Standardization, and Timeliness for Post-Processing Echosounder Data in Tidal Energy Streams Abstract: Understanding the abundance and distribution of fish in tidal energy streams is important for assessing the risk presented by the introduc... |
Title: The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication Abstract: We study the stochastic multi-player multi-armed bandit problem. In this problem, $m$ players cooperate to maximize their total reward from $K > m$ arms. However the players cannot communicate... |
Title: Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things Abstract: Internet of Medical Things (IoMT) represents an application of the Internet of Things, where health professionals perform remote analysis of physiological data collected using sensors that are associated with pa... |
Title: Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks Abstract: We propose a network architecture capable of reliably estimating uncertainty of regression based predictions without sacrificing accuracy. The current state-of-the-art uncertainty algorithms either fall short of... |
Title: Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning Abstract: Off-policy evaluation and learning (OPE/L) use offline observational data to make better decisions, which is crucial in applications where experimentation is necessarily limited. OPE/L is nonetheless sensitive to discrepancies bet... |
Title: Truncated Diffusion Probabilistic Models Abstract: Employing a forward Markov diffusion chain to gradually map the data to a noise distribution, diffusion probabilistic models learn how to generate the data by inferring a reverse Markov diffusion chain to invert the forward diffusion process. To achieve competit... |
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