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Title: Meta-Learning Transferable Parameterized Skills Abstract: We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient learning in long-horizon tasks. We first propose novel learning objectives -... |
Title: Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning Abstract: Network compression is crucial to making the deep networks to be more efficient, faster, and generalizable to low-end hardware. Current network compression methods have two open problems: first, there lacks a theoretical ... |
Title: Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines Abstract: Online travel agencies (OTA's) advertise their website offers on meta-search bidding engines. The problem of predicting the number of clicks a hotel would receive for a given bid amount is an important ... |
Title: Spatial Cross-Attention Improves Self-Supervised Visual Representation Learning Abstract: Unsupervised representation learning methods like SwAV are proved to be effective in learning visual semantics of a target dataset. The main idea behind these methods is that different views of a same image represent the sa... |
Title: Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation Abstract: Despite their high computation and communication costs, Newton-type methods remain an appealing option for distributed training due to their robustness against ill-conditioned convex problems. In this work, we stud... |
Title: Certifying Data-Bias Robustness in Linear Regression Abstract: Datasets typically contain inaccuracies due to human error and societal biases, and these inaccuracies can affect the outcomes of models trained on such datasets. We present a technique for certifying whether linear regression models are pointwise-ro... |
Title: Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure Abstract: The practicality of reinforcement learning algorithms has been limited due to poor scaling with respect to the problem size, as the sample complexity of learning an $\epsilon$-optimal policy i... |
Title: A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction Abstract: Recent emergence of high-throughput drug screening assays sparkled an intensive development of machine learning methods, including models for prediction of sensitivity of cancer cell lines to anti-cance... |
Title: Extending Momentum Contrast with Cross Similarity Consistency Regularization Abstract: Contrastive self-supervised representation learning methods maximize the similarity between the positive pairs, and at the same time tend to minimize the similarity between the negative pairs. However, in general the interplay... |
Title: NOMAD: Nonlinear Manifold Decoders for Operator Learning Abstract: Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By directly learning maps (operato... |
Title: A Penny for Your (visual) Thoughts: Self-Supervised Reconstruction of Natural Movies from Brain Activity Abstract: Reconstructing natural videos from fMRI brain recordings is very challenging, for two main reasons: (i) As fMRI data acquisition is difficult, we only have a limited amount of supervised samples, wh... |
Title: Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits Abstract: We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers many common reward distribution... |
Title: How does overparametrization affect performance on minority groups? Abstract: The benefits of overparameterization for the overall performance of modern machine learning (ML) models are well known. However, the effect of overparameterization at a more granular level of data subgroups is less understood. Recent e... |
Title: Few-Shot Learning by Dimensionality Reduction in Gradient Space Abstract: We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace. In experimental and theoretical analyses, we show that... |
Title: Parametric Chordal Sparsity for SDP-based Neural Network Verification Abstract: Many future technologies rely on neural networks, but verifying the correctness of their behavior remains a major challenge. It is known that neural networks can be fragile in the presence of even small input perturbations, yielding ... |
Title: SHRED: 3D Shape Region Decomposition with Learned Local Operations Abstract: We present SHRED, a method for 3D SHape REgion Decomposition. SHRED takes a 3D point cloud as input and uses learned local operations to produce a segmentation that approximates fine-grained part instances. We endow SHRED with three dec... |
Title: Short Blocklength Wiretap Channel Codes via Deep Learning: Design and Performance Evaluation Abstract: We design short blocklength codes for the Gaussian wiretap channel under information-theoretic security guarantees. Our approach consists in decoupling the reliability and secrecy constraints in our code design... |
Title: FDGNN: Fully Dynamic Graph Neural Network Abstract: Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an es... |
Title: Discrete State-Action Abstraction via the Successor Representation Abstract: When reinforcement learning is applied with sparse rewards, agents must spend a prohibitively long time exploring the unknown environment without any learning signal. Abstraction is one approach that provides the agent with an intrinsic... |
Title: Adversarial Reprogramming Revisited Abstract: Adversarial reprogramming, introduced by Elsayed, Goodfellow, and Sohl-Dickstein, seeks to repurpose a neural network to perform a different task, by manipulating its input without modifying its weights. We prove that two-layer ReLU neural networks with random weight... |
Title: Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach Abstract: Upcoming technologies like digital twins, autonomous, and artificial intelligent systems involving safety-critical applications require models which are accurate, inter... |
Title: FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning Abstract: Federated learning (FL) has emerged as an effective approach to address consumer privacy needs. FL has been successfully applied to certain machine learning tasks, such as training smart keyboard models and keywo... |
Title: Learning in Observable POMDPs, without Computationally Intractable Oracles Abstract: Much of reinforcement learning theory is built on top of oracles that are computationally hard to implement. Specifically for learning near-optimal policies in Partially Observable Markov Decision Processes (POMDPs), existing al... |
Title: Robust Sparse Mean Estimation via Sum of Squares Abstract: We study the problem of high-dimensional sparse mean estimation in the presence of an $\epsilon$-fraction of adversarial outliers. Prior work obtained sample and computationally efficient algorithms for this task for identity-covariance subgaussian distr... |
Title: A Benchmark for Federated Hetero-Task Learning Abstract: To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of bot... |
Title: Towards Bridging Algorithm and Theory for Unbiased Recommendation Abstract: This work studies the problem of learning unbiased algorithms from biased feedback for recommender systems. We address this problem from both theoretical and algorithmic perspectives. Recent works in unbiased learning have advanced the s... |
Title: Generating Long Videos of Dynamic Scenes Abstract: We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time. Existing video generation methods often fail to produce new content as a function of time while maintaining consiste... |
Title: Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage Abstract: Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discriminatory bias in predictions. Altho... |
Title: Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics Abstract: The rise of blockchain and distributed ledger technologies (DLTs) in the financial sector has generated a socio-economic shift that triggered legal concerns and regulatory initiatives. While the ... |
Title: FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning Abstract: Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medical institutions often use electrodes... |
Title: Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs Abstract: Human Activity Recognition (HAR) has become an increasingly popular task for embedded devices such as smartwatches. Most HAR systems for ultra-low power devices are based on classic Machine Learning (ML) models, where... |
Title: Towards a General Purpose CNN for Long Range Dependencies in $\mathrm{N}$D Abstract: The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learning framework. However, performant CNN architect... |
Title: Group privacy for personalized federated learning Abstract: Federated learning is a type of collaborative machine learning, where participating clients process their data locally, sharing only updates to the collaborative model. This enables to build privacy-aware distributed machine learning models, among other... |
Title: Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition Abstract: Speaker recognition systems (SRSs) have recently been shown to be vulnerable to adversarial attacks, raising significant security concerns. In this work, we systematically investigate transformation and adversarial ... |
Title: Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes? Abstract: In privacy-preserving machine learning, it is common that the owner of the learned model does not have any physical access to the data. Instead, only a secured remote access to a data lake is granted to the model own... |
Title: Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics Abstract: The statistical regularities in language corpora encode well-known social biases into word embeddings. Here, we focus on gender to provide a comprehensive analysis of group-based biases in widely-used static En... |
Title: On the Role of Discount Factor in Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) enables effective learning from previously collected data without exploration, which shows great promise in real-world applications when exploration is expensive or even infeasible. The discount factor,... |
Title: Imitating Past Successes can be Very Suboptimal Abstract: Prior work has proposed a simple strategy for reinforcement learning (RL): label experience with the outcomes achieved in that experience, and then imitate the relabeled experience. These outcome-conditioned imitation learning methods are appealing becaus... |
Title: On Outer Bi-Lipschitz Extensions of Linear Johnson-Lindenstrauss Embeddings of Low-Dimensional Submanifolds of $\mathbb{R}^N$ Abstract: Let $\mathcal{M}$ be a compact $d$-dimensional submanifold of $\mathbb{R}^N$ with reach $\tau$ and volume $V_{\mathcal M}$. Fix $\epsilon \in (0,1)$. In this paper we prove that... |
Title: Computational Doob's $h$-transforms for Online Filtering of Discretely Observed Diffusions Abstract: This paper is concerned with online filtering of discretely observed nonlinear diffusion processes. Our approach is based on the fully adapted auxiliary particle filter, which involves Doob's $h$-transforms that ... |
Title: DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings Abstract: The existence of multiple load-solution mappings of non-convex AC-OPF problems poses a fundamental challenge to deep neural network (DNN) schemes. As the training dataset may contain a mixture of data points... |
Title: Building Robust Ensembles via Margin Boosting Abstract: In the context of adversarial robustness, a single model does not usually have enough power to defend against all possible adversarial attacks, and as a result, has sub-optimal robustness. Consequently, an emerging line of work has focused on learning an en... |
Title: An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training Abstract: Large medical imaging data sets are becoming increasingly available. A common challenge in these data sets is to ensure that each sample meets minimum quality requirements de... |
Title: Adaptive Regularization for Adversarial Training Abstract: Adversarial training, which is to enhance robustness against adversarial attacks, has received much attention because it is easy to generate human-imperceptible perturbations of data to deceive a given deep neural network. In this paper, we propose a new... |
Title: AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems Abstract: Recent work has illuminated the vulnerability of speaker recognition systems (SRSs) against adversarial attacks, raising significant security concerns in deploying SRSs. However, they considered only a few settings (e.g.... |
Title: Improving trajectory calculations using deep learning inspired single image superresolution Abstract: Lagrangian trajectory or particle dispersion models as well as semi-Lagrangian advection schemes require meteorological data such as wind, temperature and geopotential at the exact spatio-temporal locations of t... |
Title: Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification Abstract: We consider using gradient descent to minimize the nonconvex function $f(X)=\phi(XX^{T})$ over an $n\times r$ factor matrix $X$, in which $\phi$ is an underlying smooth con... |
Title: Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images Abstract: Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Preoperative tumor localization, differential diagnosis, and subsequent selection of appropriate treatment for parotid gland tumors is critical. Howe... |
Title: Assessing Project-Level Fine-Tuning of ML4SE Models Abstract: Machine Learning for Software Engineering (ML4SE) is an actively growing research area that focuses on methods that help programmers in their work. In order to apply the developed methods in practice, they need to achieve reasonable quality in order t... |
Title: Improving the Diagnosis of Psychiatric Disorders with Self-Supervised Graph State Space Models Abstract: Single subject prediction of brain disorders from neuroimaging data has gained increasing attention in recent years. Yet, for some heterogeneous disorders such as major depression disorder (MDD) and autism sp... |
Title: DeepTPI: Test Point Insertion with Deep Reinforcement Learning Abstract: Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this paper, we propose a novel TPI approach based on deep reinfo... |
Title: Concentration bounds for SSP Q-learning for average cost MDPs Abstract: We derive a concentration bound for a Q-learning algorithm for average cost Markov decision processes based on an equivalent shortest path problem, and compare it numerically with the alternative scheme based on relative value iteration. |
Title: Subject Membership Inference Attacks in Federated Learning Abstract: Privacy in Federated Learning (FL) is studied at two different granularities: item-level, which protects individual data points, and user-level, which protects each user (participant) in the federation. Nearly all of the private FL literature i... |
Title: Neural Network Decoders for Permutation Codes Correcting Different Errors Abstract: Permutation codes were extensively studied in order to correct different types of errors for the applications on power line communication and rank modulation for flash memory. In this paper, we introduce the neural network decode... |
Title: Integrating Random Effects in Deep Neural Networks Abstract: Modern approaches to supervised learning like deep neural networks (DNNs) typically implicitly assume that observed responses are statistically independent. In contrast, correlated data are prevalent in real-life large-scale applications, with typical ... |
Title: PyTSK: A Python Toolbox for TSK Fuzzy Systems Abstract: This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang (TSK) fuzzy systems. Based on scikit-learn and PyTorch, PyTSK allows users to optimize TSK fuzzy systems using fuzzy clustering or mini-batch gradient descent (MBGD) based algorit... |
Title: Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking Abstract: Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. The model needs to capture the system behavior in multiple flight regimes and opera... |
Title: On the balance between the training time and interpretability of neural ODE for time series modelling Abstract: Most machine learning methods are used as a black box for modelling. We may try to extract some knowledge from physics-based training methods, such as neural ODE (ordinary differential equation). Neura... |
Title: Recent Advances in Bayesian Optimization Abstract: Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the development of new Bayesian optimization algorithms and their applications. Hence, th... |
Title: Generalization Error Bounds for Deep Neural Networks Trained by SGD Abstract: Generalization error bounds for deep neural networks trained by stochastic gradient descent (SGD) are derived by combining a dynamical control of an appropriate parameter norm and the Rademacher complexity estimate based on parameter n... |
Title: Joint Manifold Learning and Density Estimation Using Normalizing Flows Abstract: Based on the manifold hypothesis, real-world data often lie on a low-dimensional manifold, while normalizing flows as a likelihood-based generative model are incapable of finding this manifold due to their structural constraints. So... |
Title: Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning Abstract: While annotating decent amounts of data to satisfy sophisticated learning models can be cost-prohibitive for many real-world applications. Active learning (AL) and semi-supervised learni... |
Title: On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning Abstract: Intelligent agents should have the ability to leverage knowledge from previously learned tasks in order to learn new ones quickly and efficiently. Meta-learning approaches have emerged as a popular solution to achieve this. However,... |
Title: Machine Learning Sensors Abstract: Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from complex integration, lack of modularity, and privacy and security concerns from data movement. This... |
Title: Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection Abstract: Data augmentation has been rare in the cyber security domain due to technical difficulties in altering data in a manner that is semantically consistent with the original data. This shortfall is particularly onerous given ... |
Title: DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning Abstract: Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines. However, users tend to mistrust the optimizat... |
Title: Decentralized Online Regularized Learning Over Random Time-Varying Graphs Abstract: We study the decentralized online regularized linear regression algorithm over random time-varying graphs. At each time step, every node runs an online estimation algorithm consisting of an innovation term processing its own new ... |
Title: Analyzing the impact of feature selection on the accuracy of heart disease prediction Abstract: Heart Disease has become one of the most serious diseases that has a significant impact on human life. It has emerged as one of the leading causes of mortality among the people across the globe during the last decade.... |
Title: An Analysis of Selection Bias Issue for Online Advertising Abstract: In online advertising, a set of potential advertisements can be ranked by a certain auction system where usually the top-1 advertisement would be selected and displayed at an advertising space. In this paper, we show a selection bias issue that... |
Title: Inferring Unfairness and Error from Population Statistics in Binary and Multiclass Classification Abstract: We propose methods for making inferences on the fairness and accuracy of a given classifier, using only aggregate population statistics. This is necessary when it is impossible to obtain individual classif... |
Title: Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances Abstract: The Sliced-Wasserstein distance (SW) is a computationally efficient and theoretically grounded alternative to the Wasserstein distance. Yet, the literature on its statistical properties with respect to the distribution of slices, be... |
Title: Improved Cardiac Arrhythmia Prediction Based on Heart Rate Variability Analysis Abstract: Many types of ventricular and atrial cardiac arrhythmias have been discovered in clinical practice in the past 100 years, and these arrhythmias are a major contributor to sudden cardiac death. Ventricular tachycardia, ventr... |
Title: Deep Neural Patchworks: Coping with Large Segmentation Tasks Abstract: Convolutional neural networks are the way to solve arbitrary image segmentation tasks. However, when images are large, memory demands often exceed the available resources, in particular on a common GPU. Especially in biomedical imaging, where... |
Title: From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation Abstract: The emerging field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to today's powerful but opaque deep learning models. While local XAI methods explain individual predictions ... |
Title: FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning Abstract: Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving way. Since in real-world applica... |
Title: Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow Abstract: Transfer learning (TL) is becoming a powerful tool in scientific applications of neural networks (NNs), such as weather/climate prediction and turbulence modeling. TL enables out-of-distributio... |
Title: Generalized Data Distribution Iteration Abstract: To obtain higher sample efficiency and superior final performance simultaneously has been one of the major challenges for deep reinforcement learning (DRL). Previous work could handle one of these challenges but typically failed to address them concurrently. In t... |
Title: Asymptotic Stability in Reservoir Computing Abstract: Reservoir Computing is a class of Recurrent Neural Networks with internal weights fixed at random. Stability relates to the sensitivity of the network state to perturbations. It is an important property in Reservoir Computing as it directly impacts performanc... |
Title: A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem Abstract: Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehic... |
Title: Risk Measures and Upper Probabilities: Coherence and Stratification Abstract: Machine learning typically presupposes classical probability theory which implies that aggregation is built upon expectation. There are now multiple reasons to motivate looking at richer alternatives to classical probability theory as ... |
Title: Fooling Explanations in Text Classifiers Abstract: State-of-the-art text classification models are becoming increasingly reliant on deep neural networks (DNNs). Due to their black-box nature, faithful and robust explanation methods need to accompany classifiers for deployment in real-life scenarios. However, it ... |
Title: Learning Attention-based Representations from Multiple Patterns for Relation Prediction in Knowledge Graphs Abstract: Knowledge bases, and their representations in the form of knowledge graphs (KGs), are naturally incomplete. Since scientific and industrial applications have extensively adopted them, there is a ... |
Title: Look Back When Surprised: Stabilizing Reverse Experience Replay for Neural Approximation Abstract: Experience replay methods, which are an essential part of reinforcement learning(RL) algorithms, are designed to mitigate spurious correlations and biases while learning from temporally dependent data. Roughly spea... |
Title: Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge Abstract: The success of deep neural networks (DNNs) is heavily dependent on computational resources. While DNNs are often employed on cloud servers, there is a growing need to operate DNNs on edge devices. Edge devices are typically lim... |
Title: Utility of Equivariant Message Passing in Cortical Mesh Segmentation Abstract: The automated segmentation of cortical areas has been a long-standing challenge in medical image analysis. The complex geometry of the cortex is commonly represented as a polygon mesh, whose segmentation can be addressed by graph-base... |
Title: Shuffled Check-in: Privacy Amplification towards Practical Distributed Learning Abstract: Recent studies of distributed computation with formal privacy guarantees, such as differentially private (DP) federated learning, leverage random sampling of clients in each round (privacy amplification by subsampling) to a... |
Title: Group Meritocratic Fairness in Linear Contextual Bandits Abstract: We study the linear contextual bandit problem where an agent has to select one candidate from a pool and each candidate belongs to a sensitive group. In this setting, candidates' rewards may not be directly comparable between groups, for example ... |
Title: Transfer learning to decode brain states reflecting the relationship between cognitive tasks Abstract: Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance i... |
Title: Intra-agent speech permits zero-shot task acquisition Abstract: Human language learners are exposed to a trickle of informative, context-sensitive language, but a flood of raw sensory data. Through both social language use and internal processes of rehearsal and practice, language learners are able to build high... |
Title: CitySpec: An Intelligent Assistant System for Requirement Specification in Smart Cities Abstract: An increasing number of monitoring systems have been developed in smart cities to ensure that real-time operations of a city satisfy safety and performance requirements. However, many existing city requirements are ... |
Title: Towards Meta-learned Algorithm Selection using Implicit Fidelity Information Abstract: Automatically selecting the best performing algorithm for a given dataset or ranking multiple of them by their expected performance supports users in developing new machine learning applications. Most approaches for this probl... |
Title: Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting Abstract: Traffic forecasting is one canonical example of spatial-temporal learning task in Intelligent Traffic System. Existing approaches capture spatial dependency with a pre-determined matrix in graph convolution neura... |
Title: Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse Abstract: Transformers have achieved remarkable success in several domains, ranging from natural language processing to computer vision. Nevertheless, it has been recently shown that stacking self-attention layers - the di... |
Title: Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering Abstract: The ecological validity of soundscape studies usually rests on a choice of soundscapes that are representative of the perceptual space under investigation. For exam... |
Title: Better Best of Both Worlds Bounds for Bandits with Switching Costs Abstract: We study best-of-both-worlds algorithms for bandits with switching cost, recently addressed by Rouyer, Seldin and Cesa-Bianchi, 2021. We introduce a surprisingly simple and effective algorithm that simultaneously achieves minimax optima... |
Title: Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey Abstract: Black-box nature of Artificial Intelligence (AI) models do not allow users to comprehend and sometimes trust the output created by such model. In AI applications, where not only the results but also the decision paths to the res... |
Title: Beyond spectral gap: The role of the topology in decentralized learning Abstract: In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. We consider the setting in ... |
Title: A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy Abstract: Currently, the federated graph neural network (GNN) has attracted a lot of attention due to its wide applications in reality without violating the privacy regulations. Among all the privacy-preserving technologi... |
Title: An Empirical Study of IoT Security Aspects at Sentence-Level in Developer Textual Discussions Abstract: IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, ensuring the security of IoT devices is crucial. IoT devices can differ from traditional computing, ther... |
Title: EiX-GNN : Concept-level eigencentrality explainer for graph neural networks Abstract: Explaining is a human knowledge transfer process regarding a phenomenon between an explainer and an explainee. Each word used to explain this phenomenon must be carefully selected by the explainer in accordance with the current... |
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