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Title: SubStrat: A Subset-Based Strategy for Faster AutoML Abstract: Automated machine learning (AutoML) frameworks have become important tools in the data scientists' arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions of... |
Title: Patch-based image Super Resolution using generalized Gaussian mixture model Abstract: Single Image Super Resolution (SISR) methods aim to recover the clean images in high resolution from low resolution observations.A family of patch-based approaches have received considerable attention and development. The minim... |
Title: Recent Advances for Quantum Neural Networks in Generative Learning Abstract: Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is through quantum machine learning, especially quantum genera... |
Title: Universal Speech Enhancement with Score-based Diffusion Abstract: Removing background noise from speech audio has been the subject of considerable research and effort, especially in recent years due to the rise of virtual communication and amateur sound recording. Yet background noise is not the only unpleasant ... |
Title: Learning Backward Compatible Embeddings Abstract: Embeddings, low-dimensional vector representation of objects, are fundamental in building modern machine learning systems. In industrial settings, there is usually an embedding team that trains an embedding model to solve intended tasks (e.g., product recommendat... |
Title: Intelligent Circuit Design and Implementation with Machine Learning Abstract: The stagnation of EDA technologies roots from insufficient knowledge reuse. In practice, very similar simulation or optimization results may need to be repeatedly constructed from scratch. This motivates my research on introducing more... |
Title: How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression Abstract: Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets. We propose $\textbf{Go}$al-conditioned $f$-$\t... |
Title: Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation Abstract: Nonnegative matrix factorization (NMF) has been widely used to dimensionality reduction in machine learning. However, the traditional NMF does not properly handle outliers, so that it is sensitive to noise. In order to... |
Title: The Survival Bandit Problem Abstract: We study the survival bandit problem, a variant of the multi-armed bandit problem introduced in an open problem by Perotto et al. (2019), with a constraint on the cumulative reward; at each time step, the agent receives a (possibly negative) reward and if the cumulative rewa... |
Title: Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images Abstract: The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast detection and patient triage is becoming critical. Thi... |
Title: Histogram Estimation under User-level Privacy with Heterogeneous Data Abstract: We study the problem of histogram estimation under user-level differential privacy, where the goal is to preserve the privacy of all entries of any single user. While there is abundant literature on this classical problem under the i... |
Title: Driving in Real Life with Inverse Reinforcement Learning Abstract: In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set of trajectory proposals, filters these trajectories wit... |
Title: Distributionally Invariant Learning: Rationalization and Practical Algorithms Abstract: The invariance property across environments is at the heart of invariant learning methods for the Out-of-Distribution (OOD) Generalization problem. Although intuitively reasonable, strong assumptions on the availability and q... |
Title: DynaMaR: Dynamic Prompt with Mask Token Representation Abstract: Recent research has shown that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. Typically when adapting these language models to downstream tasks, like a classi... |
Title: DETR++: Taming Your Multi-Scale Detection Transformer Abstract: Convolutional Neural Networks (CNN) have dominated the field of detection ever since the success of AlexNet in ImageNet classification [12]. With the sweeping reform of Transformers [27] in natural language processing, Carion et al. [2] introduce th... |
Title: Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm Abstract: Pruning techniques have been successfully used in neural networks to trade accuracy for sparsity. However, the impact of network pruning is not uniform: prior work has shown that the recall for underrepresented classes in a... |
Title: Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics Abstract: Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how neural activity relates to perception and behavior. Models of neural dynamics oft... |
Title: A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk Abstract: We design simple and optimal policies that ensure safety against heavy-tailed risk in the classical multi-armed bandit problem. Recently, \cite{fan2021fragility} showed that information-theoretically optimized ... |
Title: Improving Knowledge Graph Embedding via Iterative Self-Semantic Knowledge Distillation Abstract: Knowledge graph embedding (KGE) has been intensively investigated for link prediction by projecting entities and relations into continuous vector spaces. Current popular high-dimensional KGE methods obtain quite slig... |
Title: Confounder Analysis in Measuring Representation in Product Funnels Abstract: This paper discusses an application of Shapley values in the causal inference field, specifically on how to select the top confounder variables for coarsened exact matching method in a scalable way. We use a dataset from an observationa... |
Title: Beyond Faithfulness: A Framework to Characterize and Compare Saliency Methods Abstract: Saliency methods calculate how important each input feature is to a machine learning model's prediction, and are commonly used to understand model reasoning. "Faithfulness", or how fully and accurately the saliency output ref... |
Title: GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation Abstract: Machine Learning (ML) systems are a building part of the modern tools which impact our daily life in several application domains. Due to their black-box nature, those systems are hardly adopted in application domains (e.g. heal... |
Title: Learning to Efficiently Propagate for Reasoning on Knowledge Graphs Abstract: Path-based methods are more appealing solutions than embedding methods for knowledge graph reasoning, due to their interpretability and generalization ability to unseen graphs. However, path-based methods usually suffer from the proble... |
Title: Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection Abstract: Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications. Despite its popularity, it is prone to noises and outliers, which leads to singularity problem and bias in the meas... |
Title: Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization Abstract: We analyze the convergence rates of stochastic gradient algorithms for smooth finite-sum minimax optimization and show that, for many such algorithms, sampling the data points without replacement leads to faster conve... |
Title: On the Convergence of Optimizing Persistent-Homology-Based Losses Abstract: Topological loss based on persistent homology has shown promise in various applications. A topological loss enforces the model to achieve certain desired topological property. Despite its empirical success, less is known about the optimi... |
Title: Predicting Electricity Infrastructure Induced Wildfire Risk in California Abstract: This paper examines the use of risk models to predict the timing and location of wildfires caused by electricity infrastructure. Our data include historical ignition and wire-down points triggered by grid infrastructure collected... |
Title: Neuro-Symbolic Causal Language Planning with Commonsense Prompting Abstract: Language planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps. Such procedural reasoning ability is essential for applications such as household robots and virtual assistants. Alth... |
Title: Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime Abstract: We provide quantitative bounds measuring the $L^2$ difference in function space between the trajectory of a finite-width network trained on finitely many samples from the idealized kernel dynamics of infinite width and infini... |
Title: Tight basis cycle representatives for persistent homology of large data sets Abstract: Persistent homology (PH) is a popular tool for topological data analysis that has found applications across diverse areas of research. It provides a rigorous method to compute robust topological features in discrete experiment... |
Title: Stable and memory-efficient image recovery using monotone operator learning (MOL) Abstract: We introduce a monotone deep equilibrium learning framework for large-scale inverse problems in imaging. The proposed algorithm relies on forward-backward splitting, where each iteration consists of a gradient descent inv... |
Title: Schema-Guided Event Graph Completion Abstract: We tackle a new task, event graph completion, which aims to predict missing event nodes for event graphs. Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such a... |
Title: Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks Abstract: We propose an algorithm that compresses the critical information of a large dataset into compact addressable memories. These memories can then be recalled to quickly re-train a neural network and recover the performanc... |
Title: Training Subset Selection for Weak Supervision Abstract: Existing weak supervision approaches use all the data covered by weak signals to train a classifier. We show both theoretically and empirically that this is not always optimal. Intuitively, there is a tradeoff between the amount of weakly-labeled data and ... |
Title: 8-bit Numerical Formats for Deep Neural Networks Abstract: Given the current trend of increasing size and complexity of machine learning architectures, it has become of critical importance to identify new approaches to improve the computational efficiency of model training. In this context, we address the advant... |
Title: Boundary informed inverse PDE problems on discrete Riemann surfaces Abstract: We employ neural networks to tackle inverse partial differential equations on discretized Riemann surfaces with boundary. To this end, we introduce the concept of a graph with boundary which models these surfaces in a natural way. Our ... |
Title: Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data Abstract: Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learning techniques on the UK-B... |
Title: Goal-Space Planning with Subgoal Models Abstract: This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often wor... |
Title: Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics Abstract: Group fairness metrics are an established way of assessing the fairness of prediction-based decision-making systems. However, these metrics are still insufficiently linked t... |
Title: A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs Abstract: In prediction-based decision-making systems, different perspectives can be at odds: The short-term business goals of the decision makers are often in conflict with the decision subjects' wish to be treated fairly. Bal... |
Title: Conditional Seq2Seq model for the time-dependent two-level system Abstract: We apply the deep learning neural network architecture to the two-level system in quantum optics to solve the time-dependent Schrodinger equation. By carefully designing the network structure and tuning parameters, above 90 percent accur... |
Title: Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks Abstract: We consider the off-policy evaluation problem of reinforcement learning using deep neural networks. We analyze the deep fitted Q-evaluation method for estimating the expected cumulative reward of a... |
Title: Graph Rationalization with Environment-based Augmentations Abstract: Rationale is defined as a subset of input features that best explains or supports the prediction by machine learning models. Rationale identification has improved the generalizability and interpretability of neural networks on vision and langua... |
Title: A Human-Centric Take on Model Monitoring Abstract: Predictive models are increasingly used to make various consequential decisions in high-stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that these models make accurate predictions, are robust to shifts in the data, do not re... |
Title: Efficient entity-based reinforcement learning Abstract: Recent deep reinforcement learning (DRL) successes rely on end-to-end learning from fixed-size observational inputs (e.g. image, state-variables). However, many challenging and interesting problems in decision making involve observations or intermediary rep... |
Title: A Bird's-Eye Tutorial of Graph Attention Architectures Abstract: Graph Neural Networks (GNNs) have shown tremendous strides in performance for graph-structured problems especially in the domains of natural language processing, computer vision and recommender systems. Inspired by the success of the transformer ar... |
Title: On Efficient Approximate Queries over Machine Learning Models Abstract: The question of answering queries over ML predictions has been gaining attention in the database community. This question is challenging because the cost of finding high quality answers corresponds to invoking an oracle such as a human exper... |
Title: Invertible Sharpening Network for MRI Reconstruction Enhancement Abstract: High-quality MRI reconstruction plays a critical role in clinical applications. Deep learning-based methods have achieved promising results on MRI reconstruction. However, most state-of-the-art methods were designed to optimize the evalua... |
Title: Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds Abstract: We consider a linear stochastic bandit problem involving $M$ agents that can collaborate via a central server to minimize regret. A fraction $\alpha$ of these agents are adversarial and can act arbitrarily, leading to the ... |
Title: RORL: Robust Offline Reinforcement Learning via Conservative Smoothing Abstract: Offline reinforcement learning (RL) provides a promising direction to exploit the massive amount of offline data for complex decision-making tasks. Due to the distribution shift issue, current offline RL algorithms are generally des... |
Title: Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission Abstract: A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data, but modeling this emission relies on observations of rare gas... |
Title: Quantum Neural Network Classifiers: A Tutorial Abstract: Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both power... |
Title: GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions Abstract: We investigate the generalization capabilities of neural signed distance functions (SDFs) for learning 3D object representations for unseen and unlabeled point clouds. Existing methods can fit SDFs to a handful of object classes and ... |
Title: Blended Latent Diffusion Abstract: The tremendous progress in neural image generation, coupled with the emergence of seemingly omnipotent vision-language models has finally enabled text-based interfaces for creating and editing images. Handling generic images requires a diverse underlying generative model, hence... |
Title: Neuro CROSS exchange: Learning to CROSS exchange to solve realistic vehicle routing problems Abstract: CROSS exchange (CE), a meta-heuristic that solves various vehicle routing problems (VRPs), improves the solutions of VRPs by swapping the sub-tours of the vehicles. Inspired by CE, we propose Neuro CE (NCE), a ... |
Title: The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization Abstract: The logit outputs of a feedforward neural network at initialization are conditionally Gaussian, given a random covariance matrix defined by the penultimate layer. In this work, we study the distribution of this random... |
Title: Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities Abstract: We study hypothesis testing under communication constraints, where each sample is quantized before being revealed to a statistician. Without communication constraints, it is well known that th... |
Title: Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images Abstract: A key concern in integrating machine learning models in medicine is the ability to interpret their reasoning. Popular explainability methods have demonstrated satisfactory results in natural image recognition, y... |
Title: Robust Calibration with Multi-domain Temperature Scaling Abstract: Uncertainty quantification is essential for the reliable deployment of machine learning models to high-stakes application domains. Uncertainty quantification is all the more challenging when training distribution and test distribution are differe... |
Title: Predicting and Understanding Human Action Decisions during Skillful Joint-Action via Machine Learning and Explainable-AI Abstract: This study uses supervised machine learning (SML) and explainable artificial intelligence (AI) to model, predict and understand human decision-making during skillful joint-action. Lo... |
Title: Global Mixup: Eliminating Ambiguity with Clustering Abstract: Data augmentation with \textbf{Mixup} has been proven an effective method to regularize the current deep neural networks. Mixup generates virtual samples and corresponding labels at once through linear interpolation. However, this one-stage generation... |
Title: Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networks Abstract: The power system state estimation (SE) algorithm estimates the complex bus voltages based on the available set of measurements. Because phasor measurement units (PMUs) are becoming more widely employed in transmission ... |
Title: Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning Abstract: In this work we propose Neuro-Nav, an open-source library for neurally plausible reinforcement learning (RL). RL is among the most common modeling frameworks for studying decision making, learning, and navigation in biological organisms... |
Title: Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Abstract: Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full... |
Title: FedNST: Federated Noisy Student Training for Automatic Speech Recognition Abstract: Federated Learning (FL) enables training state-of-the-art Automatic Speech Recognition (ASR) models on user devices (clients) in distributed systems, hence preventing transmission of raw user data to a central server. A key chall... |
Title: FuSS: Fusing Superpixels for Improved Segmentation Consistency Abstract: In this work, we propose two different approaches to improve the semantic consistency of Open Set Semantic Segmentation. First, we propose a method called OpenGMM that extends the OpenPCS framework using a Gaussian Mixture of Models to mode... |
Title: Is a Modular Architecture Enough? Abstract: Inspired from human cognition, machine learning systems are gradually revealing advantages of sparser and more modular architectures. Recent work demonstrates that not only do some modular architectures generalize well, but they also lead to better out-of-distribution ... |
Title: Specification-Guided Learning of Nash Equilibria with High Social Welfare Abstract: Reinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent systems, we propose a novel reinforcement learning fr... |
Title: Perturbation Learning Based Anomaly Detection Abstract: This paper presents a simple yet effective method for anomaly detection. The main idea is to learn small perturbations to perturb normal data and learn a classifier to classify the normal data and the perturbed data into two different classes. The perturbat... |
Title: Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Abstract: Stochastic variance reduction has proven effective at accelerating first-order algorithms for solving convex finite-sum optimization tasks such as empirical risk minimization. Incorporating additional second-ord... |
Title: Multi-Behavior Sequential Recommendation with Temporal Graph Transformer Abstract: Modeling time-evolving preferences of users with their sequential item interactions, has attracted increasing attention in many online applications. Hence, sequential recommender systems have been developed to learn the dynamic us... |
Title: Separable Self-attention for Mobile Vision Transformers Abstract: Mobile vision transformers (MobileViT) can achieve state-of-the-art performance across several mobile vision tasks, including classification and detection. Though these models have fewer parameters, they have high latency as compared to convolutio... |
Title: Risk-Sensitive Reinforcement Learning: Iterated CVaR and the Worst Path Abstract: In this paper, we study a novel episodic risk-sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, where the objective is to maximize the tail of the reward-to-go at each step. Different from existing risk-aware R... |
Title: Enhancing Safe Exploration Using Safety State Augmentation Abstract: Safe exploration is a challenging and important problem in model-free reinforcement learning (RL). Often the safety cost is sparse and unknown, which unavoidably leads to constraint violations -- a phenomenon ideally to be avoided in safety-cri... |
Title: Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning Abstract: The danger of adversarial attacks to unprotected Uncrewed Aerial Vehicle (UAV) agents operating in public is growing. Adopting AI-based techniques and more specifically Deep Learning (DL)... |
Title: Multi-learner risk reduction under endogenous participation dynamics Abstract: Prediction systems face exogenous and endogenous distribution shift -- the world constantly changes, and the predictions the system makes change the environment in which it operates. For example, a music recommender observes exogeneou... |
Title: Robust Pareto Set Identification with Contaminated Bandit Feedback Abstract: We consider the Pareto set identification (PSI) problem in multi-objective multi-armed bandits (MO-MAB) with contaminated reward observations. At each arm pull, with some probability, the true reward samples are replaced with the sample... |
Title: Learning with Capsules: A Survey Abstract: Capsule networks were proposed as an alternative approach to Convolutional Neural Networks (CNNs) for learning object-centric representations, which can be leveraged for improved generalization and sample complexity. Unlike CNNs, capsule networks are designed to explici... |
Title: TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning Abstract: With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important. Motivated by the tuning behaviors of human experts, it is intuitive to leverage auxiliary knowl... |
Title: Port-Hamiltonian Neural Networks with State Dependent Ports Abstract: Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems. In this work, we stress-test the method on both simple mass-spring systems and more complex and realistic syst... |
Title: Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees Abstract: We consider transfer learning approaches that fine-tune a pretrained deep neural network on a target task. We investigate generalization properties of fine-tuning to understand the problem of overfitting, which ofte... |
Title: A Regret-Variance Trade-Off in Online Learning Abstract: We consider prediction with expert advice for strongly convex and bounded losses, and investigate trade-offs between regret and "variance" (i.e., squared difference of learner's predictions and best expert predictions). With $K$ experts, the Exponentially ... |
Title: Crust Macrofracturing as the Evidence of the Last Deglaciation Abstract: Machine learning methods were applied to reconsider the results of several passive seismic experiments in Finland. We created datasets from different stages of the receiver function technique and processed them with one of basic machine lea... |
Title: Mixed Graph Contrastive Network for Semi-Supervised Node Classification Abstract: Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the pe... |
Title: Policy Optimization for Markov Games: Unified Framework and Faster Convergence Abstract: This paper studies policy optimization algorithms for multi-agent reinforcement learning. We begin by proposing an algorithm framework for two-player zero-sum Markov Games in the full-information setting, where each iteratio... |
Title: Learning Generalized Wireless MAC Communication Protocols via Abstraction Abstract: To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6G wireless networks, conventional medium access control (MAC) procedures need to evolve to enable base stations (BSs) and user equipments (UEs) to automatica... |
Title: Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations Abstract: The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by ... |
Title: Generalized Federated Learning via Sharpness Aware Minimization Abstract: Federated Learning (FL) is a promising framework for performing privacy-preserving, distributed learning with a set of clients. However, the data distribution among clients often exhibits non-IID, i.e., distribution shift, which makes effi... |
Title: Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent Abstract: Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all datapoints in the dataset. We propose... |
Title: Real-World Image Super-Resolution by Exclusionary Dual-Learning Abstract: Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials. A... |
Title: CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations Abstract: The excessive runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduced-order modeling (ROM). Whereas... |
Title: Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning Abstract: In this paper, we use tools from rate-distortion theory to establish new upper bounds on the generalization error of statistical distributed learning algorithms. Specifically, there are $K$ clients whose individually chos... |
Title: [Reproducibility Report] Explainable Deep One-Class Classification Abstract: Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods. The authors claim that FC... |
Title: Pessimistic Off-Policy Optimization for Learning to Rank Abstract: Off-policy learning is a framework for optimizing policies without deploying them, using data collected by another policy. In recommender systems, this is especially challenging due to the imbalance in logged data: some items are recommended and ... |
Title: Certified Robustness in Federated Learning Abstract: Federated learning has recently gained significant attention and popularity due to its effectiveness in training machine learning models on distributed data privately. However, as in the single-node supervised learning setup, models trained in federated learni... |
Title: Sparse Bayesian Learning for Complex-Valued Rational Approximations Abstract: Surrogate models are used to alleviate the computational burden in engineering tasks, which require the repeated evaluation of computationally demanding models of physical systems, such as the efficient propagation of uncertainties. Fo... |
Title: Forecasting COVID- 19 cases using Statistical Models and Ontology-based Semantic Modelling: A real time data analytics approach Abstract: SARS-COV-19 is the most prominent issue which many countries face today. The frequent changes in infections, recovered and deaths represents the dynamic nature of this pandemi... |
Title: UTTS: Unsupervised TTS with Conditional Disentangled Sequential Variational Auto-encoder Abstract: In this paper, we propose a novel unsupervised text-to-speech (UTTS) framework which does not require text-audio pairs for the TTS acoustic modeling (AM). UTTS is a multi-speaker speech synthesizer developed from t... |
Title: Transfer Learning based Search Space Design for Hyperparameter Tuning Abstract: The tuning of hyperparameters becomes increasingly important as machine learning (ML) models have been extensively applied in data mining applications. Among various approaches, Bayesian optimization (BO) is a successful methodology ... |
Title: Learning to Control under Time-Varying Environment Abstract: This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, designing online control algorithms for unknown LTV systems remains a chall... |
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