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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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Towards Efficient 3D Object Detection with Knowledge Distillation Abstract: Despite substantial progress in 3D object detection, advanced 3D detectors often suffer from heavy computation overheads. To this end, we explore the potential of knowledge distillation (KD) for developing efficient 3D object detectors, ... |
Title: Parameter Efficient Diff Pruning for Bias Mitigation Abstract: In recent years language models have achieved state of the art performance on a wide variety of natural language processing tasks. As these models are continuously growing in size it becomes increasingly important to explore methods to make them more... |
Title: A Review and Evaluation of Elastic Distance Functions for Time Series Clustering Abstract: Time series clustering is the act of grouping time series data without recourse to a label. Algorithms that cluster time series can be classified into two groups: those that employ a time series specific distance measure; ... |
Title: Do Deep Neural Networks Always Perform Better When Eating More Data? Abstract: Data has now become a shortcoming of deep learning. Researchers in their own fields share the thinking that "deep neural networks might not always perform better when they eat more data," which still lacks experimental validation and ... |
Title: PAC Generalization via Invariant Representations Abstract: One method for obtaining generalizable solutions to machine learning tasks when presented with diverse training environments is to find invariant representations of the data. These are representations of the covariates such that the best model on top of ... |
Title: Flowification: Everything is a Normalizing Flow Abstract: We develop a method that can be used to turn any multi-layer perceptron or convolutional network into a normalizing flow. In some cases this requires the addition of uncorrelated noise to the model but in the simplest case no additional parameters. The te... |
Title: Gradient Backpropagation Through Combinatorial Algorithms: Identity with Projection Works Abstract: Embedding discrete solvers as differentiable layers has given modern deep learning architectures combinatorial expressivity and discrete reasoning capabilities. The derivative of these solvers is zero or undefined... |
Title: Support Recovery in Sparse PCA with Incomplete Data Abstract: We study a practical algorithm for sparse principal component analysis (PCA) of incomplete and noisy data. Our algorithm is based on the semidefinite program (SDP) relaxation of the non-convex $l_1$-regularized PCA problem. We provide theoretical and ... |
Title: A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction Abstract: Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path convolution... |
Title: Automatic Short Math Answer Grading via In-context Meta-learning Abstract: Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art approaches use neural language models to create vec... |
Title: Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models Abstract: Pre-trained masked language models successfully perform few-shot learning by formulating downstream tasks as text infilling. However, as a strong alternative in full-shot settings, discriminative pre-trained models like ELECTRA... |
Title: Few-Shot Adaptation of Pre-Trained Networks for Domain Shift Abstract: Deep networks are prone to performance degradation when there is a domain shift between the source (training) data and target (test) data. Recent test-time adaptation methods update batch normalization layers of pre-trained source models depl... |
Title: Non-convex online learning via algorithmic equivalence Abstract: We study an algorithmic equivalence technique between nonconvex gradient descent and convex mirror descent. We start by looking at a harder problem of regret minimization in online non-convex optimization. We show that under certain geometric and s... |
Title: RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression Abstract: Data imbalance, in which a plurality of the data samples come from a small proportion of labels, poses a challenge in training deep neural networks. Unlike classification, in regression the labels are continuous, potentially boun... |
Title: VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models Abstract: Recent advances in vision-language pre-training (VLP) have demonstrated impressive performance in a range of vision-language (VL) tasks. However, there exist several challenges for measuring the community's progress in building general ... |
Title: Conformal Credal Self-Supervised Learning Abstract: In semi-supervised learning, the paradigm of self-training refers to the idea of learning from pseudo-labels suggested by the learner itself. Across various domains, corresponding methods have proven effective and achieve state-of-the-art performance. However, ... |
Title: Multi-Game Decision Transformers Abstract: A longstanding goal of the field of AI is a strategy for compiling diverse experience into a highly capable, generalist agent. In the subfields of vision and language, this was largely achieved by scaling up transformer-based models and training them on large, diverse d... |
Title: Re-parameterizing Your Optimizers rather than Architectures Abstract: The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to training them with model-agnostic optimizers (e.g., SGD). In this pa... |
Title: Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning Abstract: Multi-Agent Reinforcement Learning (MARL) is useful in many problems that require the cooperation and coordination of multiple agents. Learning optimal policies using reinforcement learning in a multi-agent setting... |
Title: Pooling Revisited: Your Receptive Field is Suboptimal Abstract: The size and shape of the receptive field determine how the network aggregates local information and affect the overall performance of a model considerably. Many components in a neural network, such as kernel sizes and strides for convolution and po... |
Title: bsnsing: A decision tree induction method based on recursive optimal boolean rule composition Abstract: This paper proposes a new mixed-integer programming (MIP) formulation to optimize split rule selection in the decision tree induction process, and develops an efficient search algorithm that is able to solve p... |
Title: Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance? Abstract: Technological and computational advances continuously drive forward the broad field of deep learning. In recent years, the derivation of quantities describing theuncertainty in the prediction - which natur... |
Title: Federated X-Armed Bandit Abstract: This work establishes the first framework of federated $\mathcal{X}$-armed bandit, where different clients face heterogeneous local objective functions defined on the same domain and are required to collaboratively figure out the global optimum. We propose the first federated a... |
Title: Kernel Neural Optimal Transport Abstract: We study the Neural Optimal Transport (NOT) algorithm which uses the general optimal transport formulation and learns stochastic transport plans. We show that NOT with the weak quadratic cost might learn fake plans which are not optimal. To resolve this issue, we introdu... |
Title: MetaSSD: Meta-Learned Self-Supervised Detection Abstract: Deep learning-based symbol detector gains increasing attention due to the simple algorithm design than the traditional model-based algorithms such as Viterbi and BCJR. The supervised learning framework is often employed to predict the input symbols, where... |
Title: Testing for Geometric Invariance and Equivariance Abstract: Invariant and equivariant models incorporate the symmetry of an object to be estimated (here non-parametric regression functions $f : \mathcal{X} \rightarrow \mathbb{R}$). These models perform better (with respect to $L^2$ loss) and are increasingly bei... |
Title: Self-Supervised Visual Representation Learning with Semantic Grouping Abstract: In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still,... |
Title: Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer Abstract: Lung cancer is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most common histologic subtypes of non-small-cell lung cance... |
Title: Efficient $\Phi$-Regret Minimization in Extensive-Form Games via Online Mirror Descent Abstract: A conceptually appealing approach for learning Extensive-Form Games (EFGs) is to convert them to Normal-Form Games (NFGs). This approach enables us to directly translate state-of-the-art techniques and analyses in NF... |
Title: Adapting Rapid Motor Adaptation for Bipedal Robots Abstract: Recent advances in legged locomotion have enabled quadrupeds to walk on challenging terrains. However, bipedal robots are inherently more unstable and hence it's harder to design walking controllers for them. In this work, we leverage recent advances i... |
Title: A Combination of Deep Neural Networks and K-Nearest Neighbors for Credit Card Fraud Detection Abstract: Detection of a Fraud transaction on credit cards became one of the major problems for financial institutions, organizations and companies. As the global financial system is highly connected to non-cash transac... |
Title: Improvements to Supervised EM Learning of Shared Kernel Models by Feature Space Partitioning Abstract: Expectation maximisation (EM) is usually thought of as an unsupervised learning method for estimating the parameters of a mixture distribution, however it can also be used for supervised learning when class lab... |
Title: A Design Space for Explainable Ranking and Ranking Models Abstract: Item ranking systems support users in multi-criteria decision-making tasks. Users need to trust rankings and ranking algorithms to reflect user preferences nicely while avoiding systematic errors and biases. However, today only few approaches he... |
Title: A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks Abstract: Tensorial Convolutional Neural Networks (TCNNs) have attracted much research attention for their power in reducing model parameters or enhancing the generalization ability. However, exploration of TCNNs is hindered eve... |
Title: Parameter-Efficient and Student-Friendly Knowledge Distillation Abstract: Knowledge distillation (KD) has been extensively employed to transfer the knowledge from a large teacher model to the smaller students, where the parameters of the teacher are fixed (or partially) during training. Recent studies show that ... |
Title: Mean Field inference of CRFs based on GAT Abstract: In this paper we propose an improved mean-field inference algorithm for the fully connected paired CRFs model. The improved method Message Passing operation is changed from the original linear convolution to the present graph attention operation, while the proc... |
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: 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: 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: Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training Abstract: Recent works on sparse neural network training (sparse training) have shown that a compelling trade-off between performance and efficiency can be achieved by training intrinsically sparse neural networks from sc... |
Title: Searching for the Essence of Adversarial Perturbations Abstract: Neural networks have achieved the state-of-the-art performance on various machine learning fields, yet the incorporation of malicious perturbations with input data (adversarial example) is able to fool neural networks' predictions. This would lead ... |
Title: Associative Learning Mechanism for Drug-Target Interaction Prediction Abstract: As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interactio... |
Title: Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning Abstract: We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in a trajectory independen... |
Title: Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations Abstract: The paper has two major themes. The first part of the paper establishes certain general results for infinite-dimensional optimization problems on Hilbert spaces. These results cover the classical ... |
Title: Optimistic Whittle Index Policy: Online Learning for Restless Bandits Abstract: Restless multi-armed bandits (RMABs) extend multi-armed bandits to allow for stateful arms, where the state of each arm evolves restlessly with different transitions depending on whether that arm is pulled. However, solving RMABs req... |
Title: Reinforcement Learning with a Terminator Abstract: We present the problem of reinforcement learning with exogenous termination. We define the Termination Markov Decision Process (TerMDP), an extension of the MDP framework, in which episodes may be interrupted by an external non-Markovian observer. This formulati... |
Title: Truly Deterministic Policy Optimization Abstract: In this paper, we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape. By avoiding noise injection all sources of estimation variance can be eliminated in systems with deterministic ... |
Title: Attention Flows for General Transformers Abstract: In this paper, we study the computation of how much an input token in a Transformer model influences its prediction. We formalize a method to construct a flow network out of the attention values of encoder-only Transformer models and extend it to general Transfo... |
Title: A hybrid approach to seismic deblending: when physics meets self-supervision Abstract: To limit the time, cost, and environmental impact associated with the acquisition of seismic data, in recent decades considerable effort has been put into so-called simultaneous shooting acquisitions, where seismic sources are... |
Title: Minimax Optimal Online Imitation Learning via Replay Estimation Abstract: Online imitation learning is the problem of how best to mimic expert demonstrations, given access to the environment or an accurate simulator. Prior work has shown that in the infinite sample regime, exact moment matching achieves value eq... |
Title: Designing Rewards for Fast Learning Abstract: To convey desired behavior to a Reinforcement Learning (RL) agent, a designer must choose a reward function for the environment, arguably the most important knob designers have in interacting with RL agents. Although many reward functions induce the same optimal beha... |
Title: Neural Optimal Transport with General Cost Functionals Abstract: We present a novel neural-networks-based algorithm to compute optimal transport (OT) plans and maps for general cost functionals. The algorithm is based on a saddle point reformulation of the OT problem and generalizes prior OT methods for weak and... |
Title: Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos Abstract: The interest for video anomaly detection systems has gained traction for the past few years. The current approaches use deep learning to perform anomaly detection in videos, but this approach has multiple problems. For starters, dee... |
Title: Painful intelligence: What AI can tell us about human suffering Abstract: This book uses the modern theory of artificial intelligence (AI) to understand human suffering or mental pain. Both humans and sophisticated AI agents process information about the world in order to achieve goals and obtain rewards, which ... |
Title: PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps Abstract: Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can... |
Title: Fooling SHAP with Stealthily Biased Sampling Abstract: SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arb... |
Title: Connecting adversarial attacks and optimal transport for domain adaptation Abstract: We present a novel algorithm for domain adaptation using optimal transport. In domain adaptation, the goal is to adapt a classifier trained on the source domain samples to the target domain. In our method, we use optimal transpo... |
Title: Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation Abstract: Generalizability is seen as one of the major challenges in deep learning, in particular in the domain of medical imaging, where a change of hospital or in imaging routines can lead to a complete failure... |
Title: Learning Risk-Averse Equilibria in Multi-Agent Systems Abstract: In multi-agent systems, intelligent agents are tasked with making decisions that have optimal outcomes when the actions of the other agents are as expected, whilst also being prepared for unexpected behaviour. In this work, we introduce a new risk-... |
Title: Fairness in the First Stage of Two-Stage Recommender Systems Abstract: Many large-scale recommender systems consist of two stages, where the first stage focuses on efficiently generating a small subset of promising candidates from a huge pool of items for the second-stage model to curate final recommendations fr... |
Title: FBM: Fast-Bit Allocation for Mixed-Precision Quantization Abstract: Quantized neural networks are well known for reducing latency, power consumption, and model size without significant degradation in accuracy, making them highly applicable for systems with limited resources and low power requirements. Mixed prec... |
Title: StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis Abstract: Text-to-Speech (TTS) has recently seen great progress in synthesizing high-quality speech owing to the rapid development of parallel TTS systems, but producing speech with naturalistic prosodic variations, speakin... |
Title: Continual Object Detection: A review of definitions, strategies, and challenges Abstract: The field of Continual Learning investigates the ability to learn consecutive tasks without losing performance on those previously learned. Its focus has been mainly on incremental classification tasks. We believe that rese... |
Title: Holistic Generalized Linear Models Abstract: Holistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality. These constraints include sparsity-inducing constraints, sign-coherence constraints and linear constraints. The $\tex... |
Title: Posterior and Computational Uncertainty in Gaussian Processes Abstract: Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which inevitably introduce approximation error. This additional source of uncertainty, due to limited computati... |
Title: GLDQN: Explicitly Parameterized Quantile Reinforcement Learning for Waste Reduction Abstract: We study the problem of restocking a grocery store's inventory with perishable items over time, from a distributional point of view. The objective is to maximize sales while minimizing waste, with uncertainty about the ... |
Title: Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning Abstract: Recent progress in machine learning methods, and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs), have propelled automated and autonomous microscopies to the... |
Title: Critic Sequential Monte Carlo Abstract: We introduce CriticSMC, a new algorithm for planning as inference built from a novel composition of sequential Monte Carlo with learned soft-Q function heuristic factors. This algorithm is structured so as to allow using large numbers of putative particles leading to effic... |
Title: Few-Shot Diffusion Models Abstract: Denoising diffusion probabilistic models (DDPM) are powerful hierarchical latent variable models with remarkable sample generation quality and training stability. These properties can be attributed to parameter sharing in the generative hierarchy, as well as a parameter-free d... |
Title: Data Banzhaf: A Data Valuation Framework with Maximal Robustness to Learning Stochasticity Abstract: This paper studies the robustness of data valuation to noisy model performance scores. Particularly, we find that the inherent randomness of the widely used stochastic gradient descent can cause existing data val... |
Title: Post-hoc Concept Bottleneck Models Abstract: Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts (``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances interpretability since it can be investigated to understand what concepts the model "sees"... |
Title: Sepsis Prediction with Temporal Convolutional Networks Abstract: We design and implement a temporal convolutional network model to predict sepsis onset. Our model is trained on data extracted from MIMIC III database, based on a retrospective analysis of patients admitted to intensive care unit who did not fall u... |
Title: Certifying Some Distributional Fairness with Subpopulation Decomposition Abstract: Extensive efforts have been made to understand and improve the fairness of machine learning models based on observational metrics, especially in high-stakes domains such as medical insurance, education, and hiring decisions. Howev... |
Title: Rethinking Graph Neural Networks for Anomaly Detection Abstract: Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first step towards analyzing anomalies via the lens of the graph spectrum.... |
Title: Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning Abstract: This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron cor... |
Title: Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game Abstract: Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms h... |
Title: Variational Transfer Learning using Cross-Domain Latent Modulation Abstract: To successfully apply trained neural network models to new domains, powerful transfer learning solutions are essential. We propose to introduce a novel cross-domain latent modulation mechanism to a variational autoencoder framework so a... |
Title: itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection Abstract: Recently, point-cloud based 3D object detectors have achieved remarkable progress. However, most studies are limited to the development of deep learning architectures for improving only their accuracy. In this paper, we pro... |
Title: Gluing Neural Networks Symbolically Through Hyperdimensional Computing Abstract: Hyperdimensional Computing affords simple, yet powerful operations to create long Hyperdimensional Vectors (hypervectors) that can efficiently encode information, be used for learning, and are dynamic enough to be modified on the fl... |
Title: DeepDefacer: Automatic Removal of Facial Features via U-Net Image Segmentation Abstract: Recent advancements in the field of magnetic resonance imaging (MRI) have enabled large-scale collaboration among clinicians and researchers for neuroimaging tasks. However, researchers are often forced to use outdated and s... |
Title: MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation Abstract: Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods often assume that the underlying machine-learning m... |
Title: Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series Abstract: This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based method and in sharp contrast to the existing wi... |
Title: VC Theoretical Explanation of Double Descent Abstract: There has been growing interest in generalization performance of large multilayer neural networks that can be trained to achieve zero training error, while generalizing well on test data. This regime is known as 'second descent' and it appears to contradict ... |
Title: Graph-level Neural Networks: Current Progress and Future Directions Abstract: Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of graphs instead of a single graph. Traditional graph-le... |
Title: Secure Federated Clustering Abstract: We consider a foundational unsupervised learning task of $k$-means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients. We develop SecFC, which is a secure federated clustering algorithm that simultaneously achiev... |
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