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Title: An Optimal Transport Approach to Personalized Federated Learning Abstract: Federated learning is a distributed machine learning paradigm, which aims to train a model using the local data of many distributed clients. A key challenge in federated learning is that the data samples across the clients may not be iden... |
Title: Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning Abstract: In federated learning (FL), model performance typically suffers from client drift induced by data heterogeneity, and mainstream works focus on correcting client drift. We propose a different approach named virtual ... |
Title: Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models Abstract: We consider a high-dimensional mean estimation problem over a binary hidden Markov model, which illuminates the interplay between memory in data, sample size, dimension, and signal strength in statistical inference. In this model... |
Title: Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning Abstract: Gradient coding schemes effectively mitigate full stragglers in distributed learning by introducing identical redundancy in coded local partial derivatives corresponding to all model parameters... |
Title: Class Prior Estimation under Covariate Shift -- no Problem? Abstract: We show that in the context of classification the property of source and target distributions to be related by covariate shift may break down when the information content captured in the covariates is reduced, for instance by discretization of... |
Title: Spam Detection Using BERT Abstract: Emails and SMSs are the most popular tools in today communications, and as the increase of emails and SMSs users are increase, the number of spams is also increases. Spam is any kind of unwanted, unsolicited digital communication that gets sent out in bulk, spam emails and SMS... |
Title: Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation Abstract: Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly parallelised. By using representativ... |
Title: Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study Abstract: In order to enhance levels of engagement with conversational systems, our long term research goal seeks to monitor the confusion state of a user and adapt dialogue policies in response to such user confusion states. To th... |
Title: Tackling covariate shift with node-based Bayesian neural networks Abstract: Bayesian neural networks (BNNs) promise improved generalization under covariate shift by providing principled probabilistic representations of epistemic uncertainty. However, weight-based BNNs often struggle with high computational compl... |
Title: Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach Abstract: We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as fo... |
Title: Evaluating the Predictive Performance of Positive-Unlabelled Classifiers: a brief critical review and practical recommendations for improvement Abstract: Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled... |
Title: Towards Responsible AI for Financial Transactions Abstract: The application of AI in finance is increasingly dependent on the principles of responsible AI. These principles - explainability, fairness, privacy, accountability, transparency and soundness form the basis for trust in future AI systems. In this study... |
Title: Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM Abstract: In this paper, we focus our attention on private Empirical Risk Minimization (ERM), which is one of the most commonly used data analysis method. We take the first step towards solving ... |
Title: Fast Adversarial Training with Adaptive Step Size Abstract: While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it hard to scale to large datasets like ImageNet. The key idea of recent works ... |
Title: Embrace the Gap: VAEs Perform Independent Mechanism Analysis Abstract: Variational autoencoders (VAEs) are a popular framework for modeling complex data distributions; they can be efficiently trained via variational inference by maximizing the evidence lower bound (ELBO), at the expense of a gap to the exact (lo... |
Title: Is More Data All You Need? A Causal Exploration Abstract: Curating a large scale medical imaging dataset for machine learning applications is both time consuming and expensive. Balancing the workload between model development, data collection and annotations is difficult for machine learning practitioners, espec... |
Title: A Simple yet Effective Method for Graph Classification Abstract: In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to boost performance by decreasing the complexity of such models. Intuiti... |
Title: Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks Abstract: Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impa... |
Title: Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity Abstract: Morphology of mitochondria plays critical roles in mediating their physiological functions. Accurate segmentation of mitochondria from 3D electron microscopy (EM) images is essential to quantitative ch... |
Title: Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference Abstract: With the ever increasing complexity of specifications, manual sizing for analog circuits recently became very challenging. Especially for innovative, large-scale circuits designs, with tens... |
Title: Restructuring Graph for Higher Homophily via Learnable Spectral Clustering Abstract: While a growing body of literature has been studying new Graph Neural Networks (GNNs) that work on both homophilic and heterophilic graphs, little work has been done on adapting classical GNNs to less-homophilic graphs. Although... |
Title: Efficient Minimax Optimal Global Optimization of Lipschitz Continuous Multivariate Functions Abstract: In this work, we propose an efficient minimax optimal global optimization algorithm for multivariate Lipschitz continuous functions. To evaluate the performance of our approach, we utilize the average regret in... |
Title: Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL Abstract: Model-based reinforcement learning promises to learn an optimal policy from fewer interactions with the environment compared to model-free reinforcement learning by learning an intermediate model of the environment in order to predict ... |
Title: Exploring Chemical Space with Score-based Out-of-distribution Generation Abstract: A well-known limitation of existing works on molecule generation is that the generated molecules highly resemble those in the training set. To generate truly novel molecules with completely different structures that may have even ... |
Title: Machine learning models for determination of weldbead shape parameters for gas metal arc welded T-joints -- A comparative study Abstract: The shape of a weld bead is critical in assessing the quality of the welded joint. In particular, this has a major impact in the accuracy of the results obtained from a numeri... |
Title: Markovian Interference in Experiments Abstract: We consider experiments in dynamical systems where interventions on some experimental units impact other units through a limiting constraint (such as a limited inventory). Despite outsize practical importance, the best estimators for this `Markovian' interference p... |
Title: Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation Abstract: We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance. It consists of two procedures: bidirectional pretraining and unidirectional finetuning. Both procedures utilize Si... |
Title: Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data Abstract: Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in the field of computer vision, speech, natural language processing (NLP), and recently, with other types of mod... |
Title: FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data Abstract: Algorithmic fairness plays an important role in machine learning and imposing fairness constraints during learning is a common approach. However, many datasets are imbalanced in certain label classes (e.g. "healthy") and... |
Title: Finite-Sample Maximum Likelihood Estimation of Location Abstract: We consider 1-dimensional location estimation, where we estimate a parameter $\lambda$ from $n$ samples $\lambda + \eta_i$, with each $\eta_i$ drawn i.i.d. from a known distribution $f$. For fixed $f$ the maximum-likelihood estimate (MLE) is well-... |
Title: Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs Abstract: We study sequential decision making problems aimed at maximizing the expected total reward while satisfying a constraint on the expected total utility. We employ the natural policy gradient method to s... |
Title: Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation Abstract: In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifiers. However, it is very chal... |
Title: Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets Abstract: We study the problem of online learning in competitive settings in the context of two-sided matching markets. In particular, one side of the market, the agents, must learn about their preferences over the other ... |
Title: Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence Abstract: In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies. Showing confidence scores in human-a... |
Title: Complex Locomotion Skill Learning via Differentiable Physics Abstract: Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers. However, existing work typically only delivers NN controllers with limited capability and generalizability. We present a practical learn... |
Title: Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms Abstract: Abnormalities of the sewage pipe network will affect the normal operation of the whole city. Therefore, it is important to detect the abnormalities early. This paper propose an early abnormal-detection met... |
Title: Hashing Learning with Hyper-Class Representation Abstract: Existing unsupervised hash learning is a kind of attribute-centered calculation. It may not accurately preserve the similarity between data. This leads to low down the performance of hash function learning. In this paper, a hash algorithm is proposed wit... |
Title: JigsawHSI: a network for Hyperspectral Image classification Abstract: This article describes Jigsaw, a convolutional neural network (CNN) used in geosciences and based on Inception but tailored for geoscientific analyses. Introduces JigsawHSI (based on Jigsaw) and uses it on the land-use land-cover (LULC) classi... |
Title: Asymptotic Instance-Optimal Algorithms for Interactive Decision Making Abstract: Past research on interactive decision making problems (bandits, reinforcement learning, etc.) mostly focuses on the minimax regret that measures the algorithm's performance on the hardest instance. However, an ideal algorithm should... |
Title: Efficient Machine Learning, Compilers, and Optimizations for Embedded Systems Abstract: Deep Neural Networks (DNNs) have achieved great success in a massive number of artificial intelligence (AI) applications by delivering high-quality computer vision, natural language processing, and virtual reality application... |
Title: Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation Abstract: Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for bot... |
Title: Deep Learning-based FEA surrogate for sub-sea pressure vessel Abstract: During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. The pressure vessel contains dry electronics, power sources, and other sensors that can not be flooded. A traditional design approa... |
Title: Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes Abstract: Recent advances in applying Graph Neural Networks (GNNs) to molecular science have showcased the power of learning three-dimensional (3D) structure representations with GNNs. However,... |
Title: AugLoss: A Learning Methodology for Real-World Dataset Corruption Abstract: Deep Learning (DL) models achieve great successes in many domains. However, DL models increasingly face safety and robustness concerns, including noisy labeling in the training stage and feature distribution shifts in the testing stage. ... |
Title: Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning Abstract: Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman... |
Title: Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling Abstract: We revisit the classical problem of finding an approximately stationary point of the average of $n$ smooth and possibly nonconvex functions. The optimal complexity of stochastic first-order methods in terms of the numb... |
Title: Information Threshold, Bayesian Inference and Decision-Making Abstract: We define the information threshold as the point of maximum curvature in the prior vs. posterior Bayesian curve, both of which are described as a function of the true positive and negative rates of the classification system in question. The ... |
Title: Diffusion-GAN: Training GANs with Diffusion Abstract: For stable training of generative adversarial networks (GANs), injecting instance noise into the input of the discriminator is considered as a theoretically sound solution, which, however, has not yet delivered on its promise in practice. This paper introduce... |
Title: Towards Individual Grevy's Zebra Identification via Deep 3D Fitting and Metric Learning Abstract: This paper combines deep learning techniques for species detection, 3D model fitting, and metric learning in one pipeline to perform individual animal identification from photographs by exploiting unique coat patter... |
Title: Use-Case-Grounded Simulations for Explanation Evaluation Abstract: A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use cases. However, running user studies is challenging and costly, an... |
Title: Augmenting Netflix Search with In-Session Adapted Recommendations Abstract: We motivate the need for recommendation systems that can cater to the members in-the-moment intent by leveraging their interactions from the current session. We provide an overview of an end-to-end in-session adaptive recommendations sys... |
Title: Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models Abstract: We present a novel way of conditioning a pretrained denoising diffusion speech model to produce speech in the voice of a novel person unseen during training. The method requires a short (~3 seconds) sample from the target person, and gener... |
Title: OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes Abstract: Duplication of nodes is a common problem encountered when building knowledge graphs (KGs) from heterogeneous datasets, where it is crucial to be able to merge nodes having the same meaning. OntoMerger is ... |
Title: Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency Abstract: Binary decision making classifiers are not fair by default. Fairness requirements are an additional element to the decision making rationale, which is typically driven by maximizing some utility function. In... |
Title: Offline RL for Natural Language Generation with Implicit Language Q Learning Abstract: Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by finetuning such models via supervised learning... |
Title: Models of human preference for learning reward functions Abstract: The utility of reinforcement learning is limited by the alignment of reward functions with the interests of human stakeholders. One promising method for alignment is to learn the reward function from human-generated preferences between pairs of t... |
Title: Efficient decentralized multi-agent learning in asymmetric queuing systems Abstract: We study decentralized multi-agent learning in bipartite queuing systems, a standard model for service systems. In particular, $N$ agents request service from $K$ servers in a fully decentralized way, i.e, by running the same al... |
Title: U(1) Symmetry-breaking Observed in Generic CNN Bottleneck Layers Abstract: We report on a significant discovery linking deep convolutional neural networks (CNN) to biological vision and fundamental particle physics. A model of information propagation in a CNN is proposed via an analogy to an optical system, wher... |
Title: Statistical Deep Learning for Spatial and Spatio-Temporal Data Abstract: Deep neural network models have become ubiquitous in recent years, and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., ... |
Title: Variable-rate hierarchical CPC leads to acoustic unit discovery in speech Abstract: The success of deep learning comes from its ability to capture the hierarchical structure of data by learning high-level representations defined in terms of low-level ones. In this paper we explore self-supervised learning of hie... |
Title: Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classification Abstract: The task of text classification using Bidirectional based LSTM architectures is computationally expensive and time consuming to train. For this, transformers were discovered which effectively give good perf... |
Title: GridShift: A Faster Mode-seeking Algorithm for Image Segmentation and Object Tracking Abstract: In machine learning and computer vision, mean shift (MS) qualifies as one of the most popular mode-seeking algorithms used for clustering and image segmentation. It iteratively moves each data point to the weighted me... |
Title: Machine learning applications for electricity market agent-based models: A systematic literature review Abstract: The electricity market has a vital role to play in the decarbonisation of the energy system. However, the electricity market is made up of many different variables and data inputs. These variables an... |
Title: Functional Ensemble Distillation Abstract: Bayesian models have many desirable properties, most notable is their ability to generalize from limited data and to properly estimate the uncertainty in their predictions. However, these benefits come at a steep computational cost as Bayesian inference, in most cases, ... |
Title: Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces Abstract: This paper introduces the factored conditional filter, a new filtering algorithm for simultaneously tracking states and estimating parameters in high-dimensional state spaces. The conditional nature of ... |
Title: A Survey on Deep Learning based Channel Estimation in Doubly Dispersive Environments Abstract: Wireless communications systems are impacted by multi-path fading and Doppler shift in dynamic environments, where the channel becomes doubly-dispersive and its estimation becomes an arduous task. Only a few pilots are... |
Title: Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach Abstract: Curb space is one of the busiest areas in urban road networks. Especially in recent years, the rapid increase of ride-hailing trips and commercial deliveries has induced massive pick-ups/drop... |
Title: Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training Abstract: Adversarial training has been widely explored for mitigating attacks against deep models. However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness si... |
Title: Never mind the metrics -- what about the uncertainty? Visualising confusion matrix metric distributions Abstract: There are strong incentives to build models that demonstrate outstanding predictive performance on various datasets and benchmarks. We believe these incentives risk a narrow focus on models and on th... |
Title: Perspectives of Non-Expert Users on Cyber Security and Privacy: An Analysis of Online Discussions on Twitter Abstract: Current research on users` perspectives of cyber security and privacy related to traditional and smart devices at home is very active, but the focus is often more on specific modern devices such... |
Title: HPGNN: Using Hierarchical Graph Neural Networks for Outdoor Point Cloud Processing Abstract: Inspired by recent improvements in point cloud processing for autonomous navigation, we focus on using hierarchical graph neural networks for processing and feature learning over large-scale outdoor LiDAR point clouds. W... |
Title: Which models are innately best at uncertainty estimation? Abstract: Deep neural networks must be equipped with an uncertainty estimation mechanism when deployed for risk-sensitive tasks. This paper studies the relationship between deep architectures and their training regimes with their corresponding selective p... |
Title: Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks Abstract: The convergence of GD and SGD when training mildly parameterized neural networks starting from random initialization is studied. For a broad range of models and loss functions, including the most commonly us... |
Title: Federated Adversarial Training with Transformers Abstract: Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the global trained model is vulnerable to the evasion attacks especially, the adversarial examples (AEs), carefully ... |
Title: DeeprETA: An ETA Post-processing System at Scale Abstract: Estimated Time of Arrival (ETA) plays an important role in delivery and ride-hailing platforms. For example, Uber uses ETAs to calculate fares, estimate pickup times, match riders to drivers, plan deliveries, and more. Commonly used route planning algori... |
Title: Learning Dynamics and Generalization in Reinforcement Learning Abstract: Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a potentially discontinuous value function, and generalizing well to new observations. In this paper, we analyze the learning dynamics of temporal differe... |
Title: Impossibility of Collective Intelligence Abstract: Democratization of AI involves training and deploying machine learning models across heterogeneous and potentially massive environments. Diversity of data opens up a number of possibilities to advance AI systems, but also introduces pressing concerns such as pri... |
Title: Searching Similarity Measure for Binarized Neural Networks Abstract: Being a promising model to be deployed in resource-limited devices, Binarized Neural Networks (BNNs) have drawn extensive attention from both academic and industry. However, comparing to the full-precision deep neural networks (DNNs), BNNs suff... |
Title: Interpretable Mixture of Experts for Structured Data Abstract: With the growth of machine learning for structured data, the need for reliable model explanations is essential, especially in high-stakes applications. We introduce a novel framework, Interpretable Mixture of Experts (IME), that provides interpretabi... |
Title: GAAF: Searching Activation Functions for Binary Neural Networks through Genetic Algorithm Abstract: Binary neural networks (BNNs) show promising utilization in cost and power-restricted domains such as edge devices and mobile systems. This is due to its significantly less computation and storage demand, but at t... |
Title: AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows Abstract: Nonlinear monotone transformations are used extensively in normalizing flows to construct invertible triangular mappings from simple distributions to complex ones. In existing literature, monotonicity is usually enforced by res... |
Title: Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification Abstract: As deep neural networks achieve unprecedented performance in various tasks, neural architecture search (NAS), a research field for designing neural network architectures with automated processes, is actively u... |
Title: PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding Abstract: We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lack of a standard benchmark to evaluate the performance of different methods, which... |
Title: ARC -- Actor Residual Critic for Adversarial Imitation Learning Abstract: Adversarial Imitation Learning (AIL) is a class of popular state-of-the-art Imitation Learning algorithms where an artificial adversary's misclassification is used as a reward signal and is optimized by any standard Reinforcement Learning ... |
Title: Using Connectome Features to Constrain Echo State Networks Abstract: We report an improvement to the conventional Echo State Network (ESN), which already achieves competitive performance in one-dimensional time series prediction of dynamical systems. Our model -- a 20$\%$-dense ESN with reservoir weights derived... |
Title: Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization Abstract: Directed Evolution (DE), a landmark wet-lab method originated in 1960s, enables discovery of novel protein designs via evolving a population of candidate sequences. Recent advances in biotechnology has made it possi... |
Title: Inference for Interpretable Machine Learning: Fast, Model-Agnostic Confidence Intervals for Feature Importance Abstract: In order to trust machine learning for high-stakes problems, we need models to be both reliable and interpretable. Recently, there has been a growing body of work on interpretable machine lear... |
Title: Demystifying the Global Convergence Puzzle of Learning Over-parameterized ReLU Nets in Very High Dimensions Abstract: This theoretical paper is devoted to developing a rigorous theory for demystifying the global convergence phenomenon in a challenging scenario: learning over-parameterized Rectified Linear Unit (... |
Title: Straggler-Resilient Personalized Federated Learning Abstract: Federated Learning is an emerging learning paradigm that allows training models from samples distributed across a large network of clients while respecting privacy and communication restrictions. Despite its success, federated learning faces several c... |
Title: Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning Abstract: The quintessential model-based reinforcement-learning agent iteratively refines its estimates or prior beliefs about the true underlying model of the environment. Recent empirical successes in model-based reinforcement learnin... |
Title: Learning Robust Representations Of Generative Models Using Set-Based Artificial Fingerprints Abstract: With recent progress in deep generative models, the problem of identifying synthetic data and comparing their underlying generative processes has become an imperative task for various reasons, including fightin... |
Title: Active Bayesian Causal Inference Abstract: Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of interventions. However, such a two-stage approach is uneconomical, especially in terms of a... |
Title: Your Neighbors Are Communicating: Towards Powerful and Scalable Graph Neural Networks Abstract: Message passing graph neural networks (GNNs) are known to have their expressiveness upper-bounded by 1-dimensional Weisfeiler-Lehman (1-WL) algorithm. To achieve more powerful GNNs, existing attempts either require ad... |
Title: When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction Abstract: The standard approach to personalization in machine learning consists of training a model with group attributes like sex, age group, and blood type. In this work, we show that this approach to personalization fails to i... |
Title: Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets Abstract: Application of interpretable machine learning techniques on medical datasets facilitate early and fast diagnoses, along with getting deeper insight into the data. Furthermore, the transparen... |
Title: On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model Abstract: In this paper, we study the generalization performance of overparameterized 3-layer NTK models. We show that, for a specific set of ground-truth functions (which we refer to as the "learnable set"), the test error of ... |
Title: UAV-Aided Multi-Community Federated Learning Abstract: In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In this setting, spatial... |
Title: Developing hierarchical anticipations via neural network-based event segmentation Abstract: Humans can make predictions on various time scales and hierarchical levels. Thereby, the learning of event encodings seems to play a crucial role. In this work we model the development of hierarchical predictions via auto... |
Title: First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces Abstract: From optimal transport to robust dimensionality reduction, a plethora of machine learning applications can be cast into the min-max optimization problems over Riemannian manifolds. Though many min-max algorithms have been analyze... |
Title: MetaNOR: A Meta-Learnt Nonlocal Operator Regression Approach for Metamaterial Modeling Abstract: We propose MetaNOR, a meta-learnt approach for transfer-learning operators based on the nonlocal operator regression. The overall goal is to efficiently provide surrogate models for new and unknown material-learning ... |
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