This dissertation automates deep learning pipelines and uses meta-learning for better model selection and data augmentation.
problem Challenges in selecting and fine-tuning deep learning pipelines for new datasets.
method Meta-learning for DL pipeline selection and data augmentation, using synthetic data.
result Meta-learned approaches outperform traditional methods in automated DL pipeline selection and data augmentation.
Deep learning autoencoder model clusters unlabeled time series data.
problem Clustering unlabeled time series data.
method Two-stage approach: create labels from time series characteristics, then use autoencoder for clustering.
result 87.5% accuracy in clustering unseen time series data.
New RL method learns from passive data by modeling intentions.
problem Learning from passive data like videos without rewards or actions.
method Model intentions using temporal difference learning, learning representations from raw data.
result Successfully learns features from passive data that accelerate downstream RL tasks.
Library learns Bayesian networks from mixed data without discretization.
problem Learning Bayesian networks from mixed data (discrete and continuous variables).
method Proposes an algorithm for structural and parameter learning of Bayesian networks from mixed data using a mixed MI score function and Gaussian approximation. Offers two graph structure enumeration algorithms.
result Advantages in solving approximation and gap recovery problems on synthetic and real datasets.
Structured missing data complicates machine learning, presenting new challenges.
problem Structured missingness in data hinders machine learning at scale.
method No specific method is proposed; challenges are outlined.
result Structured missingness poses fundamental hindrance to machine learning.
Picket guards against corrupted data in machine learning models.
problem Data corruption biases models and invalidates predictions.
method PicketNet detects corrupted data using self-supervised deep learning; flags corrupted queries online.
result Picket consistently protects models from corrupted data during training and deployment.
Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that supports learning d…
New research shows unlabeled data is equally valuable as labeled data in certain semi-supervised learning scenarios.
problem Improving learning performance with limited labeled data.
method Statistical models with continuous parameters, showing equal utility of unlabeled data under specific conditions.
result The learning rate of semi-supervised learning scales similarly to supervised learning when unlabeled data is abundant.
Federated learning for data streams tackles real-time learning from IoT devices.
problem Efficiently learn from data streams generated by IoT devices and smartphones.
method Proposes a general federated learning algorithm for data streams using weighted empirical risk minimization.
result Demonstrates improved performance on various machine learning tasks compared to static dataset approaches.
Bayesian approach for learning from synthetic data, improving model accuracy.
problem Lack of statistical properties and robust methods for learning from synthetic data.
method Bayesian paradigm to update model parameters considering synthetic data generating process and learning task.
result Novel approach outperforms standard methods in supervised learning and inference problems.
ORDisCo learns from unlabeled data to improve semi-supervised continual learning.
problem Lack of effective use of unlabeled data in semi-supervised continual learning.
method Deep Online Replay with Discriminator Consistency (ORDisCo) that continually passes the learned data distribution to a classifier and selectively stabilizes discriminator parameters.
result Significant performance improvement on various semi-supervised learning benchmark datasets.
This review explores federated learning for IoT data privacy.
problem Privacy risks and data transfer costs in IoT data analytics.
method Federated learning approach to protect privacy and reduce data transfer.
result Survey of methods for improving communication efficiency and privacy in IoT federated learning.
VSAE learns from missing heterogeneous data by modeling latent dependencies.
problem Learning from partially-observed heterogeneous data with missingness.
method Variational selective autoencoder (VSAE) models joint distribution of observed, unobserved, and missing data.
result VSAE improves over state-of-the-art models in data generation and imputation tasks.
This paper investigates how data augmentation improves linear separation of manifold data.
problem Understanding how data augmentation enhances linear separation of manifold data.
method Investigates the conditions under which self-supervised representations can linearly separate multi-manifold data.
result Self-supervised learning can linearly separate manifolds with a smaller distance than unsupervised learning.
Study shows cliff-learning in transfer learning from foundation models.
problem Data-scaling of transfer learning from foundation models in low data regimes.
method Investigation of cliff-learning phenomenon through foundation-model analysis and toy models.
result Cliff-learning reflects compatibility between priors and tasks.
Paper shows Cox model optimisation leaks patient data in distributed learning.
problem Risk of patient data leakage in distributed learning models.
method Optimisation of Cox survival model using federated learning.
result Optimisation process can leak patient data, necessitating new validation methods.
Bayesian approach to selecting data for machine learning.
problem Iterative data selection in machine learning algorithms.
method Embedding data selection into decision theory and deriving Bayes-optimal criteria.
result Mitigates confirmation bias in data selection.
Slice Tuner optimizes data acquisition for accurate and fair machine learning models.
problem Acquiring enough data for all slices of data to ensure accurate and fair models.
method Selective data acquisition using convex optimization and iterative learning curve updates.
result Significantly outperforms baselines in model accuracy and fairness.
Synthetic data helps prevent forgetting when learning sequentially.
problem Catastrophic forgetting in neural networks.
method Generate synthetic data via two-step optimisation process using meta-gradients.
result Training on synthetic data prevents forgetting when learning sequentially.
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough …
Unified data representation learning improves non-parametric two-sample testing.
problem Improving non-parametric two-sample testing accuracy.
method Proposes RL-TST framework combining IRs and DRs for better test power.
result RL-TST outperforms existing methods by leveraging both IRs and DRs.
Probabilistic ML improves healthcare data analysis.
problem Insufficient understanding and incomplete data in healthcare.
method Examination of probabilistic machine learning models for healthcare challenges.
result Probabilistic models enhance healthcare data analysis and model building.
Python library for integrating TDA with machine learning.
problem Data exploration and interpretability in machine learning.
method Integrates TDA with scikit-learn API, uses C++ for performance.
result Enhanced data exploration and interpretability in machine learning.
ADS filters data points for efficient batch active learning.
problem Efficiently selecting data points for annotation in parallel settings.
method Active Data Shapley (ADS) using the Shapley value of data.
result Significantly increases efficiency of active learning by 6x.
The paper explores transferring functions from one data space to another.
problem Approximating a function on a new data set using a learned function from an old data set.
method Transfer learning from one data space to another, focusing on subsets of the target data space.
result Local smoothness of the function and its lifting are related.
Machine learning algorithms learn from data and use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the machine learning process is often difficult to automate. A unified analytical framework for trustable machine learning has been presented i…
The paper addresses data uncertainty in graph embedding by modeling data points as Gaussian distributions.
problem Data uncertainty in machine learning pipelines leads to misleading embeddings and lower accuracy.
method The paper proposes modeling data uncertainty using Gaussian distributions and reformulates graph embedding techniques.
result The proposed methods improve the accuracy of graph embedding by accounting for data uncertainty.
Active learning selects samples for labeling to build accurate models with minimal labeled data.
problem Costly acquisition of labeled data in supervised learning.
method Adaptive selection of unlabeled data samples for labeling.
result Efficient model building with minimal labeled data.
FedACS uses attention to select clients with similar data for federated learning.
problem Non-IID data and data scarcity in federated learning.
method FedACS integrates an attention mechanism to prioritize clients with similar data distributions.
result FedACS improves federated learning performance by addressing non-IID data and data scarcity.
Unified and noise-reduced data valuation framework for machine learning.
problem Quantifying the contribution of individual data points in machine learning.
method Beta Shapley, a generalization of Data Shapley, relaxes the efficiency axiom.
result Beta Shapley outperforms state-of-the-art data valuation methods on various ML tasks.
i-Mix improves contrastive learning across domains without domain-specific augmentations.
problem Improving contrastive representation learning for unlabeled data across diverse domains.
method i-Mix treats contrastive learning as a non-parametric classifier problem, mixing data in input and virtual label spaces.
result i-Mix consistently improves representation quality across image, speech, and tabular data domains.
Entropy-based model for hierarchical learning from multiscale data.
problem Learning from data with auxiliary information and multiscale target functions.
method Entropy-based hierarchical learning model with multiscale entropies.
result Entropy-based model yields stronger guarantees than uniform convergence bounds.
New active learning method uses combinatorial coverage to improve data transfer and reduce bias.
problem Inability to transfer sampled data to new models and sampling bias issues.
method Data-centric active learning methods utilizing combinatorial coverage.
result Sampling data with coverage leads to better data transfer and competitive sampling bias.
In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e., ordinary supervised learning). Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. In …
Better use of unlabelled data improves Bayesian active learning models.
problem Neglecting unlabelled data harms predictive performance and data acquisition decisions.
method A simple framework for semi-supervised Bayesian active learning.
result The proposed framework produces better models than conventional approaches.
Paper proposes methods to learn accurate models from incomplete data without imputation.
problem Learning accurate models from datasets with missing values.
method Unified approach for checking data imputation necessity and efficient algorithms.
result Significant reduction in time and effort needed for data imputation.
New PAC-Bayesian bounds for online learning with data streams.
problem Challenges of traditional PAC-Bayesian bounds in dynamic data collection.
method Developed new PAC-Bayesian bounds in online learning framework, using updated regret definition and batch-to-online conversion.
result PAC-Bayesian bounds hold for online learning with dependent data and non-convex losses.
Private learning needs more data or better features.
problem Improving differentially private machine learning performance.
method Demonstrates the need for either more private data or better features.
result Private learning requires either more data or better features.
Federated Learning solves privacy and data distribution challenges in machine learning.
problem Privacy concerns and distributed data in machine learning.
method Federated Learning framework to handle privacy and distributed data.
result Solves privacy and data distribution challenges in machine learning.
Paper proposes an online learning algorithm for a neuro-fuzzy classifier with mixed data.
problem Inability of GFMMNN learning algorithms to handle mixed-attribute data.
method Extended online learning algorithm for GFMMNN that can handle both continuous and categorical features.
result Superior and stable classification performance compared to other learning algorithms.
Probabilistic methods improve SHM by learning from noisy, incomplete data.
problem Noisy and incomplete SHM data, lack of prior labels.
method Probabilistic algorithms for semi-supervised, active, and multi-task learning.
result Probabilistic methods enhance SHM by incorporating new data.
Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training data one needs to label. A crucial aspect of active learning is determining when to stop labeling data. Three potential sources for informing …
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to…
Federated learning studies separate client data and distribution gaps.
problem Understanding performance differences in federated learning across different datasets.
method Proposed a framework to disentangle out-of-sample and participation gaps.
result Dataset synthesis strategy is crucial for realistic simulations of federated learning generalization.
FDSKL algorithm trains vertically partitioned data with kernels securely and efficiently.
problem Training vertically partitioned data with kernels while maintaining privacy.
method FDSKL algorithm using random features and doubly stochastic gradients for federated learning.
result FDSKL achieves sublinear convergence and guarantees data security.
Data augmentation affects feature importance, enhancing learning for neural networks.
problem Understanding the effect of data augmentation on feature importance and learning dynamics.
method Analyzing a two-layer convolutional neural network in a multi-view data model.
result Data augmentation alters feature importance, making certain features more likely to be learned.
Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, including both linear ones and nonlinear ones, supervised ones and unsupervised ones. Particularly, deep architectures are widely applied for representation learning in recent years, and have…
Reduces data leakage in distributed deep learning models.
problem Prevents reconstruction of sensitive raw data patterns during client communications.
method Reduces distance correlation between raw data and learned representations.
result Resilient to reconstruction attacks while maintaining model accuracy.