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Title: Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games Abstract: We study decentralized policy learning in Markov games where we control a single agent to play with nonstationary and possibly adversarial opponents. Our goal is to develop a no-regret online learning algor... |
Title: MCD: Marginal Contrastive Discrimination for conditional density estimation Abstract: We consider the problem of conditional density estimation, which is a major topic of interest in the fields of statistical and machine learning. Our method, called Marginal Contrastive Discrimination, MCD, reformulates the cond... |
Title: Non-Intrusive Reduced Models based on Operator Inference for Chaotic Systems Abstract: This work explores the physics-driven machine learning technique Operator Inference (OpInf) for predicting the state of chaotic dynamical systems. OpInf provides a non-intrusive approach to infer approximations of polynomial o... |
Title: Excess risk analysis for epistemic uncertainty with application to variational inference Abstract: We analyze the epistemic uncertainty (EU) of supervised learning in Bayesian inference by focusing on the excess risk. Existing analysis is limited to the Bayesian setting, which assumes a correct model and exact B... |
Title: A Comparative Study on Energy Consumption Models for Drones Abstract: Creating an appropriate energy consumption prediction model is becoming an important topic for drone-related research in the literature. However, a general consensus on the energy consumption model is yet to be reached at present. As a result,... |
Title: OmniXAI: A Library for Explainable AI Abstract: We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of understanding and inte... |
Title: Learning programs by combining programs Abstract: The goal of inductive logic programming is to induce a set of rules (a logic program) that generalises examples. Inducing programs with many rules and literals is a major challenge. To tackle this challenge, we decompose programs into \emph{non-separable} fragmen... |
Title: Beyond Tabula Rasa: Reincarnating Reinforcement Learning Abstract: Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. However, RL systems, when applied to large-scale settings, rarely operate tabula rasa. Such large-scale systems undergo ... |
Title: Pruning for Interpretable, Feature-Preserving Circuits in CNNs Abstract: Deep convolutional neural networks are a powerful model class for a range of computer vision problems, but it is difficult to interpret the image filtering process they implement, given their sheer size. In this work, we introduce a method ... |
Title: Reinforcement Learning with Neural Radiance Fields Abstract: It is a long-standing problem to find effective representations for training reinforcement learning (RL) agents. This paper demonstrates that learning state representations with supervision from Neural Radiance Fields (NeRFs) can improve the performanc... |
Title: PROMISSING: Pruning Missing Values in Neural Networks Abstract: While data are the primary fuel for machine learning models, they often suffer from missing values, especially when collected in real-world scenarios. However, many off-the-shelf machine learning models, including artificial neural network models, a... |
Title: Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules Abstract: Neural ordinary differential equations (ODEs) have attracted much attention as continuous-time counterparts of deep residual neural networks (NNs), and numerous extensions for recurrent NNs have been pro... |
Title: Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning Abstract: Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management. In this paper, deep reinforcement learning (DRL... |
Title: BaCaDI: Bayesian Causal Discovery with Unknown Interventions Abstract: Learning causal structures from observation and experimentation is a central task in many domains. For example, in biology, recent advances allow us to obtain single-cell expression data under multiple interventions such as drugs or gene knoc... |
Title: Algorithm for Constrained Markov Decision Process with Linear Convergence Abstract: The problem of constrained Markov decision process is considered. An agent aims to maximize the expected accumulated discounted reward subject to multiple constraints on its costs (the number of constraints is relatively small). ... |
Title: Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning Abstract: Gradient based meta-learning methods are prone to overfit on the meta-training set, and this behaviour is more prominent with large and complex networks. Moreover, large networks restrict the application of meta... |
Title: Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals Abstract: Does the grammatical gender of a language interfere when measuring the semantic gender information captured by its word embeddings? A number of anomalous gender bias measurements in the embe... |
Title: Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics Abstract: Multiscale simulations are demanding in terms of computational resources. In the context of continuum micromechanics, the multiscale problem arises from the need of inferr... |
Title: Deep Learning Prediction of Severe Health Risks for Pediatric COVID-19 Patients with a Large Feature Set in 2021 BARDA Data Challenge Abstract: Most children infected with COVID-19 have no or mild symptoms and can recover automatically by themselves, but some pediatric COVID-19 patients need to be hospitalized o... |
Title: Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank Abstract: Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH), which assumes that the click probability can be factor... |
Title: KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems Abstract: Learning a dynamical system requires stabilizing the unknown dynamics to avoid state blow-ups. However, current reinforcement learning (RL) methods lack stabilization guarantees, which limits th... |
Title: Compositional Visual Generation with Composable Diffusion Models Abstract: Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible, they struggle to understand the composition of certain co... |
Title: Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis Abstract: Randomized smoothing is the dominant standard for provable defenses against adversarial examples. Nevertheless, this method has recently been proven to suffer from important information theoretic limitations. In this paper, we a... |
Title: A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features Abstract: An important characteristic of neural networks is their ability to learn representations of the input data with effective features for prediction, which is believed to be a key factor ... |
Title: Revisiting the "Video" in Video-Language Understanding Abstract: What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks. We pro... |
Title: A Learning-Based Method for Automatic Operator Selection in the Fanoos XAI System Abstract: We describe an extension of the Fanoos XAI system [Bayani et al 2022] which enables the system to learn the appropriate action to take in order to satisfy a user's request for description to be made more or less abstract.... |
Title: A review of machine learning approaches, challenges and prospects for computational tumor pathology Abstract: Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinic... |
Title: Torsional Diffusion for Molecular Conformer Generation Abstract: Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-art cheminformatics methods. We propose torsional diffusion, a novel d... |
Title: Nonsmooth automatic differentiation: a cheap gradient principle and other complexity results Abstract: We provide a simple model to estimate the computational costs of the backward and forward modes of algorithmic differentiation for a wide class of nonsmooth programs. Prominent examples are the famous relu and ... |
Title: Empirical Study of Quality Image Assessment for Synthesis of Fetal Head Ultrasound Imaging with DCGANs Abstract: In this work, we present an empirical study of DCGANs for synthetic generation of fetal head ultrasound, consisting of hyperparameter heuristics and image quality assessment. We present experiments to... |
Title: Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline Abstract: Deep learning technologies have become the backbone for the development of computer vision. With further explorations, deep neural networks have been found vulnerable to well-designed adversarial attacks. Most of the vision devices are equi... |
Title: Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection Abstract: Recent methods based on Deep Neural Networks (DNNs) have reached high accuracy for medical image analysis, including the three basic tasks: segmentation, landmark detection, and object d... |
Title: Orthogonal Transform based Generative Adversarial Network for Image Dehazing Abstract: Image dehazing has become one of the crucial preprocessing steps for any computer vision task. Most of the dehazing methods try to estimate the transmission map along with the atmospheric light to get the dehazed image in the ... |
Title: Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach Abstract: Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ... |
Title: Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification Abstract: Realizing human-like perception is a challenge in open driving scenarios due to corner cases and visual occlusions. To gather knowledge of rare and occluded instances, federated learning empowered connected autonomous ... |
Title: Uncertainty Estimation in Machine Learning Abstract: Most machine learning techniques are based upon statistical learning theory, often simplified for the sake of computing speed. This paper is focused on the uncertainty aspect of mathematical modeling in machine learning. Regression analysis is chosen to furthe... |
Title: Optimal Competitive-Ratio Control Abstract: Inspired by competitive policy designs approaches in online learning, new control paradigms such as competitive-ratio and regret-optimal control have been recently proposed as alternatives to the classical $\mathcal{H}_2$ and $\mathcal{H}_\infty$ approaches. These comp... |
Title: R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation Abstract: U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. ... |
Title: Additive MIL: Intrinsic Interpretability for Pathology Abstract: Multiple Instance Learning (MIL) has been widely applied in pathology towards solving critical problems such as automating cancer diagnosis and grading, predicting patient prognosis, and therapy response. Deploying these models in a clinical settin... |
Title: Robust Topological Inference in the Presence of Outliers Abstract: The distance function to a compact set plays a crucial role in the paradigm of topological data analysis. In particular, the sublevel sets of the distance function are used in the computation of persistent homology -- a backbone of the topologica... |
Title: Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength Abstract: Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data. However, existing methods suffer from merely using a large amount of labeled data and ign... |
Title: Learning Fine Scale Dynamics from Coarse Observations via Inner Recurrence Abstract: Recent work has focused on data-driven learning of the evolution of unknown systems via deep neural networks (DNNs), with the goal of conducting long term prediction of the dynamics of the unknown system. In many real-world appl... |
Title: Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks Abstract: Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi. However, this type of reasoning is less often observed in long-horizon, continuous domains ... |
Title: Contrastive learning unifies $t$-SNE and UMAP Abstract: Neighbor embedding methods $t$-SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. They appear to use very different loss functions with different motivations, and the exact relationship between them has been unclear. Here we s... |
Title: QAGCN: A Graph Convolutional Network-based Multi-Relation Question Answering System Abstract: Answering multi-relation questions over knowledge graphs is a challenging task as it requires multi-step reasoning over a huge number of possible paths. Reasoning-based methods with complex reasoning mechanisms, such as... |
Title: A Robust Backpropagation-Free Framework for Images Abstract: While current deep learning algorithms have been successful for a wide variety of artificial intelligence (AI) tasks, including those involving structured image data, they present deep neurophysiological conceptual issues due to their reliance on the g... |
Title: Debiased Machine Learning without Sample-Splitting for Stable Estimators Abstract: Estimation and inference on causal parameters is typically reduced to a generalized method of moments problem, which involves auxiliary functions that correspond to solutions to a regression or classification problem. Recent line ... |
Title: Drawing out of Distribution with Neuro-Symbolic Generative Models Abstract: Learning general-purpose representations from perceptual inputs is a hallmark of human intelligence. For example, people can write out numbers or characters, or even draw doodles, by characterizing these tasks as different instantiations... |
Title: Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization Abstract: This paper considers the generalization performance of differentially private convex learning. We demonstrate that the convergence analysis of Langevin algorithms can be used to obtain new generalization bou... |
Title: Differentially Private Model Compression Abstract: Recent papers have shown that large pre-trained language models (LLMs) such as BERT, GPT-2 can be fine-tuned on private data to achieve performance comparable to non-private models for many downstream Natural Language Processing (NLP) tasks while simultaneously ... |
Title: Coffee Roast Intelligence Abstract: As the coffee industry has grown, there would be more demand for roasted coffee beans, as well as increased rivalry for selling coffee and attracting customers. As the flavor of each variety of coffee is dependent on the degree of roasting of the coffee beans, it is vital to m... |
Title: Out-of-Distribution Detection using BiGAN and MDL Abstract: We consider the following problem: we have a large dataset of normal data available. We are now given a new, possibly quite small, set of data, and we are to decide if these are normal data, or if they are indicating a new phenomenon. This is a novelty ... |
Title: Extreme Compression for Pre-trained Transformers Made Simple and Efficient Abstract: Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices. However, to preserve the accuracy for such aggressive compression... |
Title: ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers Abstract: How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memory/computation requirements. In ... |
Title: Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow Abstract: The optimal power flow (OPF) problem, as a critical component of power system operations, becomes increasingly difficult to solve due to the variability, intermittency, and unpredictability of renewable energy brough... |
Title: Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball Abstract: In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure of an batter's hittin... |
Title: An Unpooling Layer for Graph Generation Abstract: We propose a novel and trainable graph unpooling layer for effective graph generation. Given a graph with features, the unpooling layer enlarges this graph and learns its desired new structure and features. Since this unpooling layer is trainable, it can be appli... |
Title: Learning in Congestion Games with Bandit Feedback Abstract: Learning Nash equilibria is a central problem in multi-agent systems. In this paper, we investigate congestion games, a class of games with benign theoretical structure and broad real-world applications. We first propose a centralized algorithm based on... |
Title: Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning Abstract: We expose the danger of reward poisoning in offline multi-agent reinforcement learning (MARL), whereby an attacker can modify the reward vectors to different learners in an offline data set while incurring a poisoning cost. Based on... |
Title: Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection Abstract: In this research, we study the change in the performance of machine learning (ML) classifiers when various linguistic preprocessing methods of a dataset were u... |
Title: Adaptive Tree Backup Algorithms for Temporal-Difference Reinforcement Learning Abstract: Q($\sigma$) is a recently proposed temporal-difference learning method that interpolates between learning from expected backups and sampled backups. It has been shown that intermediate values for the interpolation parameter ... |
Title: Saliency Attack: Towards Imperceptible Black-box Adversarial Attack Abstract: Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised effective black-box attacks with high query efficiency. H... |
Title: Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction Abstract: Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game do not have the ball for abo... |
Title: Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios Abstract: Evaluation of intervention in a multi-agent system, e.g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering ... |
Title: Soft Adversarial Training Can Retain Natural Accuracy Abstract: Adversarial training for neural networks has been in the limelight in recent years. The advancement in neural network architectures over the last decade has led to significant improvement in their performance. It sparked an interest in their deploym... |
Title: Hybrid Architectures for Distributed Machine Learning in Heterogeneous Wireless Networks Abstract: The ever-growing data privacy concerns have transformed machine learning (ML) architectures from centralized to distributed, leading to federated learning (FL) and split learning (SL) as the two most popular privac... |
Title: Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation Abstract: Data augmentation has been proven to be an effective technique for developing machine learning models that are robust to known classes of distributional shifts (e.g., rotations of images), and align... |
Title: Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees Abstract: Learning for control of dynamical systems with formal guarantees remains a challenging task. This paper proposes a learning framework to simultaneously stabilize an unknown nonlinear system with a neural controller and learn... |
Title: Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity Abstract: Data classification, the process of analyzing data and organizing it into categories, is a fundamental computing problem of natural and artificial information processing systems. Ideally, the performance of classifier models w... |
Title: Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks Abstract: The accurate numerical solution of partial differential equations is a central task in numerical analysis allowing to model a wide range of natural phenomena by employing specialized solvers depending on the scenari... |
Title: Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer Abstract: Functional connectivity plays an essential role in modern neuroscience. The modality sheds light on the brain's functional and structural aspects, including mechanisms behind multiple pathologies. One such pathology is schizop... |
Title: Stochastic Multiple Target Sampling Gradient Descent Abstract: Sampling from an unnormalized target distribution is an essential problem with many applications in probabilistic inference. Stein Variational Gradient Descent (SVGD) has been shown to be a powerful method that iteratively updates a set of particles ... |
Title: Learning Generative Factors of Neuroimaging Data with Variational auto-encoders Abstract: Neuroimaging techniques produce high-dimensional, stochastic data from which it might be challenging to extract high-level knowledge about the phenomena of interest. We address this challenge by applying the framework of ge... |
Title: Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile Abstract: Recent years have seen a surge of interest in meta-learning techniques for tackling the few-shot learning (FSL) problem. However, the meta-learner is prone to overfitting since there are only a few available samples, which can b... |
Title: Exploring the Potential of Feature Density in Estimating Machine Learning Classifier Performance with Application to Cyberbullying Detection Abstract: In this research. we analyze the potential of Feature Density (HD) as a way to comparatively estimate machine learning (ML) classifier performance prior to traini... |
Title: Comparing Performance of Different Linguistically-Backed Word Embeddings for Cyberbullying Detection Abstract: In most cases, word embeddings are learned only from raw tokens or in some cases, lemmas. This includes pre-trained language models like BERT. To investigate on the potential of capturing deeper relatio... |
Title: C$^3$Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy Abstract: 3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined surfaces remains an unsolved problem. The challenges arise from the nature of optical colonoscopy data, characterized by highly reflective low-... |
Title: Formal Specifications from Natural Language Abstract: We study the ability of language models to translate natural language into formal specifications with complex semantics. In particular, we fine-tune off-the-shelf language models on three datasets consisting of structured English sentences and their correspon... |
Title: Modelling and Mining of Patient Pathways: A Scoping Review Abstract: The sequence of visits and procedures performed by the patient in the health system, also known as the patient's pathway or trajectory, can reveal important information about the clinical treatment adopted and the health service provided. The r... |
Title: Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis Abstract: Transport-based metrics and related embeddings (transforms) have recently been used to model signal classes where nonlinear structures or variations are present. In this paper, we study the geodesic properties of time s... |
Title: Rethinking the Openness of CLIP Abstract: Contrastive Language-Image Pre-training (CLIP) has demonstrated great potential in realizing open-vocabulary image classification in a matching style, because of its holistic use of natural language supervision that covers unconstrained real-world visual concepts. Howeve... |
Title: Combinatorial Causal Bandits Abstract: In combinatorial causal bandits (CCB), the learning agent chooses at most $K$ variables in each round to intervene, collects feedback from the observed variables, with the goal of minimizing expected regret on the target variable $Y$. Different from all prior studies on cau... |
Title: MSR: Making Self-supervised learning Robust to Aggressive Augmentations Abstract: Most recent self-supervised learning methods learn visual representation by contrasting different augmented views of images. Compared with supervised learning, more aggressive augmentations have been introduced to further improve t... |
Title: Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning Abstract: Value function estimation is an indispensable subroutine in reinforcement learning, which becomes more challenging in the offline setting. In this paper, we propose Hybrid Value Estimation (HVE) to reduce value estimat... |
Title: CVNets: High Performance Library for Computer Vision Abstract: We introduce CVNets, a high-performance open-source library for training deep neural networks for visual recognition tasks, including classification, detection, and segmentation. CVNets supports image and video understanding tools, including data loa... |
Title: Combinatorial optimization for low bit-width neural networks Abstract: Low-bit width neural networks have been extensively explored for deployment on edge devices to reduce computational resources. Existing approaches have focused on gradient-based optimization in a two-stage train-and-compress setting or as a c... |
Title: Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis Abstract: Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, however, this assumption is almost always violated due to distribution shifts betw... |
Title: Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? Abstract: The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The $L^2$ Physics-Informed Loss is the de-facto standard in training P... |
Title: Between Rate-Distortion Theory & Value Equivalence in Model-Based 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 reinforce... |
Title: Guided Deep Metric Learning Abstract: Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficulty of the dataset caused ... |
Title: A Neural Network Approach for Homogenization of Multiscale Problems Abstract: We propose a neural network-based approach to the homogenization of multiscale problems. The proposed method uses a derivative-free formulation of a training loss, which incorporates Brownian walkers to find the macroscopic description... |
Title: A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning Abstract: Adaptive gradient methods have become popular in optimizing deep neural networks; recent examples include AdaGrad and Adam. Although Adam usually converges faster, variations of Adam, for instance, the AdaBelief algorith... |
Title: Interpolating Between Softmax Policy Gradient and Neural Replicator Dynamics with Capped Implicit Exploration Abstract: Neural replicator dynamics (NeuRD) is an alternative to the foundational softmax policy gradient (SPG) algorithm motivated by online learning and evolutionary game theory. The NeuRD expected up... |
Title: Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL Abstract: Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios. Unfortunately, this evaluation approach provides limited evidence for post-deployment generalization beyond the t... |
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 ... |
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: 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: 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: 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: 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... |
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