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Title: Principle Components Analysis based frameworks for efficient missing data imputation algorithms Abstract: Missing data is a commonly occurring problem in practice, and imputation, i.e., filling the missing entries of the data, is a popular way to deal with this problem. This motivates multiple works on imputatio... |
Title: Batch Normalization Is Blind to the First and Second Derivatives of the Loss Abstract: In this paper, we prove the effects of the BN operation on the back-propagation of the first and second derivatives of the loss. When we do the Taylor series expansion of the loss function, we prove that the BN operation will ... |
Title: Machine Learning Methods for Health-Index Prediction in Coating Chambers Abstract: Coating chambers create thin layers that improve the mechanical and optical surface properties in jewelry production using physical vapor deposition. In such a process, evaporated material condensates on the walls of such chambers... |
Title: On Avoiding Local Minima Using Gradient Descent With Large Learning Rates Abstract: It has been widely observed in training of neural networks that when applying gradient descent (GD), a large step size is essential for obtaining superior models. However, the effect of large step sizes on the success of GD is no... |
Title: Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity Abstract: We study structured convex optimization problems, with additive objective $r:=p + q$, where $r$ is ($\mu$-strongly) convex, $q$ is $L_q$-smooth and convex, and $p$ is $L_p$-smooth, possibly nonconvex. For such a c... |
Title: Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data Abstract: Machine learning in high-stakes domains, such as healthcare, faces two critical challenges: (1) generalizing to diverse data distributions given limited training data while (2) maintaining interpretability. To address... |
Title: Why Adversarial Training of ReLU Networks Is Difficult? Abstract: This paper mathematically derives an analytic solution of the adversarial perturbation on a ReLU network, and theoretically explains the difficulty of adversarial training. Specifically, we formulate the dynamics of the adversarial perturbation ge... |
Title: Domain Constraints in Feature Space: Strengthening Robustness of Android Malware Detection against Realizable Adversarial Examples Abstract: Strengthening the robustness of machine learning-based malware detectors against realistic evasion attacks remains one of the major obstacles for Android malware detection.... |
Title: Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing Abstract: The performance of GNNs degrades as they become deeper due to the over-smoothing. Among all the attempts to prevent over-smoothing, residual connection is one of the promi... |
Title: Generalizing Hierarchical Bayesian Bandits Abstract: A contextual bandit is a popular and practical framework for online learning to act under uncertainty. In many problems, the number of actions is huge and their mean rewards are correlated. In this work, we introduce a general framework for capturing such corr... |
Title: Payday loans -- blessing or growth suppressor? Machine Learning Analysis Abstract: The upsurge of real estate involves a variety of factors that have got influenced by many domains. Indeed, the unrecognized sector that would affect the economy for which regulatory proposals are being drafted to keep this in cont... |
Title: OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs Abstract: This work provides the first theoretical study on the ability of graph Message Passing Neural Networks (gMPNNs) -- such as Graph Neural Networks (GNNs) -- to perform inductive out-of-distribution (OOD) link pr... |
Title: Learning Adaptive Propagation for Knowledge Graph Reasoning Abstract: Due to the success of Graph Neural Networks (GNNs) in learning from graph-structured data, various GNN-based methods have been introduced to learn from knowledge graphs (KGs). In this paper, to reveal the key factors underneath existing GNN-ba... |
Title: Online Agnostic Multiclass Boosting Abstract: Boosting is a fundamental approach in machine learning that enjoys both strong theoretical and practical guarantees. At a high-level, boosting algorithms cleverly aggregate weak learners to generate predictions with arbitrarily high accuracy. In this way, boosting al... |
Title: A k nearest neighbours classifiers ensemble based on extended neighbourhood rule and features subsets Abstract: kNN based ensemble methods minimise the effect of outliers by identifying a set of data points in the given feature space that are nearest to an unseen observation in order to predict its response by u... |
Title: FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction Abstract: Although Machine Learning (ML) can be seen as a promising tool to improve clinical decision-making for supporting the improvement of medication plans, clinical procedures, diagnoses, or medication prescriptions, it remain... |
Title: Meta Representation Learning with Contextual Linear Bandits Abstract: Meta-learning seeks to build algorithms that rapidly learn how to solve new learning problems based on previous experience. In this paper we investigate meta-learning in the setting of stochastic linear bandit tasks. We assume that the tasks s... |
Title: CHALLENGER: Training with Attribution Maps Abstract: We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance. Regularization is key in deep learning, especially when training complex models on relatively small datasets. In order to u... |
Title: Retrieving and Ranking Relevant JavaScript Technologies from Web Repositories Abstract: The selection of software technologies is an important but complex task. We consider developers of JavaScript (JS) applications, for whom the assessment of JS libraries has become difficult and time-consuming due to the growi... |
Title: CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning Abstract: Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering.... |
Title: Improved Algorithms for Bandit with Graph Feedback via Regret Decomposition Abstract: The problem of bandit with graph feedback generalizes both the multi-armed bandit (MAB) problem and the learning with expert advice problem by encoding in a directed graph how the loss vector can be observed in each round of th... |
Title: Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences Abstract: Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied in large-scale appli... |
Title: Embedding Graphs on Grassmann Manifold Abstract: Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction. Given the non-Euclidean structural property of graphs, preserving the original graph data's similarity relationship in... |
Title: SEREN: Knowing When to Explore and When to Exploit Abstract: Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states. To encourage exploration, recent approaches proposed adding stochasticity to a... |
Title: A Transistor Operations Model for Deep Learning Energy Consumption Scaling Abstract: Deep Learning (DL) has transformed the automation of a wide range of industries and finds increasing ubiquity in society. The increasing complexity of DL models and its widespread adoption has led to the energy consumption doubl... |
Title: Hilbert Curve Projection Distance for Distribution Comparison Abstract: Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling. In this study, we propose a novel metric, called Hilbert curve projection (HCP) distance, to measure the distance b... |
Title: Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength Abstract: Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in... |
Title: Metrizing Fairness Abstract: We study supervised learning problems for predicting properties of individuals who belong to one of two demographic groups, and we seek predictors that are fair according to statistical parity. This means that the distributions of the predictions within the two groups should be close... |
Title: RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch Abstract: Training deep reinforcement learning (DRL) models usually requires high computation costs. Therefore, compressing DRL models possesses immense potential for training acceleration and model deployment. However, existing methods that ... |
Title: Agnostic Physics-Driven Deep Learning Abstract: This work establishes that a physical system can perform statistical learning without gradient computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combines energy minimization, homeostatic control, and nudging towards the correct response... |
Title: Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification Abstract: Transductive methods always outperform inductive methods in few-shot image classification scenarios. However, the existing few-shot methods contain a latent condition: the number of samples in each class is the same, whic... |
Title: Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Abstract: This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stochastic processes characterized by: 1) the C processes are non-stationary, 2) the C processes are dependent... |
Title: Chefs' Random Tables: Non-Trigonometric Random Features Abstract: We introduce chefs' random tables (CRTs), a new class of non-trigonometric random features (RFs) to approximate Gaussian and softmax kernels. CRTs are an alternative to standard random kitchen sink (RKS) methods, which inherently rely on the trigo... |
Title: Running the Dual-PQC GAN on noisy simulators and real quantum hardware Abstract: In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN. We applied the model on a realistic High-Energy Physics (HEP) use case: the ex... |
Title: Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets Abstract: Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in reinforcement learning, and it is well-known that classical algorithms for bandits with time horizon $T$ suffer $\Omega(\sqrt{T})$ regret... |
Title: A Continuous Time Framework for Discrete Denoising Models Abstract: We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and corresponding reverse time generative process as Continuous Time Markov Chai... |
Title: Fast Nonlinear Vector Quantile Regression Abstract: Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable $\mathrm{Y}$ given explanatory features $\boldsymbol{\mathrm{X}}$. A limitation of QR is that it is only defined for scalar target variables, due t... |
Title: Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model Abstract: Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically investigate how to explain the EEG-based deep learning models. We conduct ... |
Title: Knowledge Distillation for 6D Pose Estimation by Keypoint Distribution Alignment Abstract: Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose ... |
Title: Sampling-free Inference for Ab-Initio Potential Energy Surface Networks Abstract: Obtaining the energy of molecular systems typically requires solving the associated Schr\"odinger equation. Unfortunately, analytical solutions only exist for single-electron systems, and accurate approximate solutions are expensiv... |
Title: FedAUXfdp: Differentially Private One-Shot Federated Distillation Abstract: Federated learning suffers in the case of non-iid local datasets, i.e., when the distributions of the clients' data are heterogeneous. One promising approach to this challenge is the recently proposed method FedAUX, an augmentation of fe... |
Title: White-box Membership Attack Against Machine Learning Based Retinopathy Classification Abstract: The advances in machine learning (ML) have greatly improved AI-based diagnosis aid systems in medical imaging. However, being based on collecting medical data specific to individuals induces several security issues, e... |
Title: Dataset Condensation via Efficient Synthetic-Data Parameterization Abstract: The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset condensation attempt to reduce the dependence on such massive d... |
Title: AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications Abstract: Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based commu... |
Title: Multi-Agent Reinforcement Learning is a Sequence Modeling Problem Abstract: Large sequence model (SM) such as GPT series and BERT has displayed outstanding performance and generalization capabilities on vision, language, and recently reinforcement learning tasks. A natural follow-up question is how to abstract m... |
Title: Data-driven Numerical Invariant Synthesis with Automatic Generation of Attributes Abstract: We propose a data-driven algorithm for numerical invariant synthesis and verification. The algorithm is based on the ICE-DT schema for learning decision trees from samples of positive and negative states and implications ... |
Title: Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles Abstract: Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attract... |
Title: Detecting Unknown DGAs without Context Information Abstract: New malware emerges at a rapid pace and often incorporates Domain Generation Algorithms (DGAs) to avoid blocking the malware's connection to the command and control (C2) server. Current state-of-the-art classifiers are able to separate benign from mali... |
Title: Harnessing spectral representations for subgraph alignment Abstract: With the rise and advent of graph learning techniques, graph data has become ubiquitous. However, while several efforts are being devoted to the design of new convolutional architectures, pooling or positional encoding schemes, less effort is b... |
Title: Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review Abstract: Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has eme... |
Title: Neural Volumetric Object Selection Abstract: We introduce an approach for selecting objects in neural volumetric 3D representations, such as multi-plane images (MPI) and neural radiance fields (NeRF). Our approach takes a set of foreground and background 2D user scribbles in one view and automatically estimates ... |
Title: CalFAT: Calibrated Federated Adversarial Training with Label Skewness Abstract: Recent studies have shown that, like traditional machine learning, federated learning (FL) is also vulnerable to adversarial attacks. To improve the adversarial robustness of FL, few federated adversarial training (FAT) methods have ... |
Title: Unbalanced CO-Optimal Transport Abstract: Optimal transport (OT) compares probability distributions by computing a meaningful alignment between their samples. CO-optimal transport (COOT) takes this comparison further by inferring an alignment between features as well. While this approach leads to better alignmen... |
Title: ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection Abstract: Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively. Existing CIL either suffers from serious accuracy loss due to catastrophic forgetting... |
Title: A Deep Learning Approach for Automatic Detection of Qualitative Features of Lecturing Abstract: Artificial Intelligence in higher education opens new possibilities for improving the lecturing process, such as enriching didactic materials, helping in assessing students' works or even providing directions to the t... |
Title: Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning Abstract: Bringing deep neural networks (DNNs) into safety critical applications such as automated driving, medical imaging and finance, requires a thorough treatment of the model's uncertainties. Training deep neural... |
Title: Confederated Learning: Federated Learning with Decentralized Edge Servers Abstract: Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider a centralized framework, in which a single server... |
Title: FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation Abstract: Federated Learning (FL) is a decentralized learning paradigm in which multiple clients collaboratively train deep learning models without centralizing their local data and hence preserve data privacy. Real-world ap... |
Title: Daisy Bloom Filters Abstract: Weighted Bloom filters (Bruck, Gao and Jiang, ISIT 2006) are Bloom filters that adapt the number of hash functions according to the query element. That is, they use a sequence of hash functions $h_1, h_2, \dots$ and insert $x$ by setting the bits in $k_x$ positions $h_1(x), h_2(x), ... |
Title: Exploring the Open World Using Incremental Extreme Value Machines Abstract: Dynamic environments require adaptive applications. One particular machine learning problem in dynamic environments is open world recognition. It characterizes a continuously changing domain where only some classes are seen in one batch ... |
Title: Blind Estimation of a Doubly Selective OFDM Channel: A Deep Learning Algorithm and Theory Abstract: We provide a new generation solution to the fundamental old problem of a doubly selective fading channel estimation for orthogonal frequency division multiplexing (OFDM) systems. For systems based on OFDM, we prop... |
Title: Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors Abstract: Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we inte... |
Title: Adversarial synthesis based data-augmentation for code-switched spoken language identification Abstract: Spoken Language Identification (LID) is an important sub-task of Automatic Speech Recognition(ASR) that is used to classify the language(s) in an audio segment. Automatic LID plays an useful role in multiling... |
Title: Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina Abstract: Voter suppression and associated racial disparities in access to voting are long-standing civil rights concerns in the United States. Barriers to voting have taken many forms over the... |
Title: Rites de Passage: Elucidating Displacement to Emplacement of Refugees Abstract: Social media deliberations allow to explore refugee-related is-sues. AI-based studies have investigated refugee issues mostly around a specific event and considered unimodal approaches. Contrarily, we have employed a multimodal archi... |
Title: Leave-one-out Singular Subspace Perturbation Analysis for Spectral Clustering Abstract: The singular subspaces perturbation theory is of fundamental importance in probability and statistics. It has various applications across different fields. We consider two arbitrary matrices where one is a leave-one-column-ou... |
Title: Anti-virus Autobots: Predicting More Infectious Virus Variants for Pandemic Prevention through Deep Learning Abstract: More infectious virus variants can arise from rapid mutations in their proteins, creating new infection waves. These variants can evade one's immune system and infect vaccinated individuals, low... |
Title: Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning Abstract: Humans are capable of completing a range of challenging manipulation tasks that require reasoning jointly over modalities such as vision, touch, and sound. Moreover, many such tasks are partially-observed; for exam... |
Title: Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression Abstract: As modern machine learning models continue to advance the computational frontier, it has become increasingly important to develop precise estimates for expected performance improvements under different model and d... |
Title: Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning Abstract: We study data poisoning attacks on online deep reinforcement learning (DRL) where the attacker is oblivious to the learning algorithm used by the agent and does not necessarily have full knowledge of the environment. We demonstrat... |
Title: To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning Abstract: Federated learning (FL) facilitates collaboration between a group of clients who seek to train a common machine learning model without directly sharing their local data. Although there is an abundance of research o... |
Title: Adversarial Bandits Robust to $S$-Switch Regret Abstract: We study the adversarial bandit problem under $S$ number of switching best arms for unknown $S$. For handling this problem, we adopt the master-base framework using the online mirror descent method (OMD). We first provide a master-base algorithm with basi... |
Title: GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization Abstract: Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes called the seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given... |
Title: Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning Abstract: To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system, c... |
Title: Temporal Multiresolution Graph Neural Networks For Epidemic Prediction Abstract: In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to... |
Title: Adaptive Learning for Discovery Abstract: In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively label the points with the goal to maximize the sum of responses. This problem has wide appli... |
Title: Lepton Flavour Violation Identification in Tau Decay ($τ^{-} \rightarrow μ^{-}μ^{-}μ^{+}$) Using Artificial Intelligence Abstract: The discovery of neutrino oscillation, proving that neutrinos do have masses, reveals the misfits of particles in the current Standard Model (SM) theory. In theory, neutrinos having ... |
Title: Robust Weight Perturbation for Adversarial Training Abstract: Overfitting widely exists in adversarial robust training of deep networks. An effective remedy is adversarial weight perturbation, which injects the worst-case weight perturbation during network training by maximizing the classification loss on advers... |
Title: Bayesian Low-Rank Interpolative Decomposition for Complex Datasets Abstract: In this paper, we introduce a probabilistic model for learning interpolative decomposition (ID), which is commonly used for feature selection, low-rank approximation, and identifying hidden patterns in data, where the matrix factors are... |
Title: Universality of group convolutional neural networks based on ridgelet analysis on groups Abstract: We investigate the approximation property of group convolutional neural networks (GCNNs) based on the ridgelet theory. We regard a group convolution as a matrix element of a group representation, and formulate a ve... |
Title: Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods Abstract: While deep learning has outperformed other methods for various tasks, theoretical frameworks that explain its reason have not been fully established. To address this issue, we investigate the... |
Title: Mitigating Out-of-Distribution Data Density Overestimation in Energy-Based Models Abstract: Deep energy-based models (EBMs), which use deep neural networks (DNNs) as energy functions, are receiving increasing attention due to their ability to learn complex distributions. To train deep EBMs, the maximum likelihoo... |
Title: Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding Abstract: Contrastive learning, especially Self-Supervised Contrastive Learning (SSCL), has achieved great success in extracting powerful features from unlabeled data, enabling comparable performance to the supervised counterpart. In this ... |
Title: TaSIL: Taylor Series Imitation Learning Abstract: We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control. TaSIL penalizes deviations in the higher-order Taylor series terms between the learned and expert policies. We sho... |
Title: Last-iterate convergence analysis of stochastic momentum methods for neural networks Abstract: The stochastic momentum method is a commonly used acceleration technique for solving large-scale stochastic optimization problems in artificial neural networks. Current convergence results of stochastic momentum method... |
Title: BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis Abstract: Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing them from mono audio has attr... |
Title: Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning Abstract: Recurrent neural networks have a strong inductive bias towards learning temporally compressed representations, as the entire history of a sequence is represented by a single vector. By contrast, Transforme... |
Title: End-to-End Topology-Aware Machine Learning for Power System Reliability Assessment Abstract: Conventional power system reliability suffers from the long run time of Monte Carlo simulation and the dimension-curse of analytic enumeration methods. This paper proposes a preliminary investigation on end-to-end machin... |
Title: Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret Abstract: The stochastic multi-armed bandit setting has been recently studied in the non-stationary regime, where the mean payoff of each action is a non-decreasing function of the number of rounds passed since it was last p... |
Title: Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks Abstract: Automatic modulation classification (AMC) using the Deep Neural Network (DNN) approach outperforms the traditional classification techniques, even in the presence of challenging wireless channel environments. However, the ... |
Title: Bayes Classification using an approximation to the Joint Probability Distribution of the Attributes Abstract: The Naive-Bayes classifier is widely used due to its simplicity, speed and accuracy. However this approach fails when, for at least one attribute value in a test sample, there are no corresponding traini... |
Title: TransforMAP: Transformer for Memory Access Prediction Abstract: Data Prefetching is a technique that can hide memory latency by fetching data before it is needed by a program. Prefetching relies on accurate memory access prediction, to which task machine learning based methods are increasingly applied. Unlike pr... |
Title: An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge Abstract: We propose an algorithm for non-stationary kernel bandits that does not require prior knowledge of the degree of non-stationarity. The algorithm follows randomized strategies obtained by solving optimization probl... |
Title: Unfooling Perturbation-Based Post Hoc Explainers Abstract: Monumental advancements in artificial intelligence (AI) have lured the interest of doctors, lenders, judges, and other professionals. While these high-stakes decision-makers are optimistic about the technology, those familiar with AI systems are wary abo... |
Title: Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing Abstract: Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision making in healthcare contexts. This is especially true s... |
Title: Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks Abstract: Ransomware attacks are increasing at an alarming rate, leading to large financial losses, unrecoverable encrypted data, data leakage, and privacy concerns. The prompt detection of ransomware attacks is required to mini... |
Title: A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare Abstract: Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics. Effective synthesis for ex... |
Title: CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers Abstract: Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation. Its application to video generation is still facing many challenges: The potential huge computat... |
Title: Stochastic Zeroth Order Gradient and Hessian Estimators: Variance Reduction and Refined Bias Bounds Abstract: We study stochastic zeroth order gradient and Hessian estimators for real-valued functions in $\mathbb{R}^n$. We show that, via taking finite difference along random orthogonal directions, the variance o... |
Title: Intelligent analysis of EEG signals to assess consumer decisions: A Study on Neuromarketing Abstract: Neuromarketing is an emerging field that combines neuroscience and marketing to understand the factors that influence consumer decisions better. The study proposes a method to understand consumers' positive and ... |
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