Paper proposes SiSTA for single-shot domain adaptation using target-aware generative augmentation.
problem Adapting models from source to target domains with limited target data.
method Fine-tunes a generative model on a single-shot target and uses novel sampling strategies for synthetic data.
result Improves performance by up to 20% over existing baselines in face attribute detection.
SapAugment learns adaptive augmentation policies for better model training.
problem Fixed data augmentation methods often apply the same augmentation to all samples, ignoring sample difficulty.
method SapAugment adapts augmentation parameters based on training loss, learning a sample-adaptive policy.
result SapAugment achieves up to 21% relative reduction in word error rate on LibriSpeech dataset.
Adaptive weighting schemes enhance time-series data augmentation for financial and UCR datasets.
problem Limited size of time-series datasets hinders model performance.
method Two adaptive weighting schemes for automatic data augmentation.
result Improves annualized returns by over 50% on financial dataset and outperforms state-of-the-art on half of UCR datasets.
PANDA augments data to regularize GLM estimation and inference.
problem Regularizing estimation and inference in GLMs with noisy data.
method Iteratively optimizes augmented noise data to converge to regularized model estimates.
result Established convergence and asymptotic distributions for regularized parameters.
Paper improves Thompson Sampling for linear contextual bandits.
problem Empirical Thompson Sampling does not achieve optimal regret bounds.
method Develops a novel estimator with adaptive data augmentation and coupling.
result Achieves nearly minimax optimal performance.
We propose an AdaPtive Noise Augmentation (PANDA) technique to regularize the estimation and construction of undirected graphical models. PANDA iteratively optimizes the objective function given the noise augmented data until convergence to achieve regularization on model parameters. The augmented noises can be designe…
AGM improves model accuracy through adaptive generation and feature augmentation.
problem Improving model accuracy in machine learning competitions.
method Adaptive Generation Model (AGM) using stacked ensemble learning with horizontal and vertical model expansion and feature augmentation.
result AGM outperforms previous models in 7 data sets.
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…
A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio. Machine learning provides effective tools to automate cognitive radio functionalities by reliably extracting and learning intrinsic spectrum dynamics. However, there are two im…
Cross-regularization adapts model complexity during training.
problem Manual tuning of model complexity for overfitting prevention.
method Directly adapts regularization parameters through validation gradients during training.
result Organic emergence of architecture-specific regularization during training.
Develops a framework for assessing adversarial robustness in deep learning models.
problem Adversarial examples can falsely flip deep learning models' predictions with imperceptible perturbations.
method Defines adversarial robustness as a locally adaptive measure and develops a data-augmentation framework.
result Proves that the adaptive data-augmentation maintains consistency of 1-nearest neighbor classification under deterministic labels.
Meta-learning improves by adding more data to tasks.
problem Overfitting to meta-training tasks impairs generalization to novel tasks.
method Task augmentation methods (MetaMix and Channel Shuffle) are proposed to improve generalization.
result MetaMix and Channel Shuffle outperform state-of-the-art results across multiple datasets.
This paper studies the problem of learning with augmented classes (LAC), where augmented classes unobserved in the training data might emerge in the testing phase. Previous studies generally attempt to discover augmented classes by exploiting geometric properties, achieving inspiring empirical performance yet lacking t…
Data augmentation affects estimates' uncertainty and distribution in complex ways.
problem Understanding how data augmentation impacts the variance and limiting distribution of estimates.
method Developed an adaptation of Lindeberg's technique for block dependence.
result Data augmentation can increase rather than decrease uncertainty, and it may shift the double-descent peak of an empirical risk.
Adaptive financial dataflow system improves model robustness in dynamic markets.
problem Static historical data leads to poor performance in dynamic financial markets.
method Drift-aware dataflow system with adaptive control and optimization.
result Enhanced model robustness and improved risk-adjusted returns.
Method generates intermediate domains to align source and target domains.
problem Challenges of domain adaptation with significant domain divergence.
method Progressive domain augmentation via domain interpolation and multiple subspace alignment.
result Achieves state-of-the-art performance on multiple domain adaptation tasks.
CODA resolves coordination issues in offline multi-agent reinforcement learning.
problem Coordination failure in offline multi-agent reinforcement learning.
method Diffusion-based multi-agent trajectory generator for data augmentation.
result CODA resolves coordination pathologies in continuous polynomial games and complex benchmarks.
Framework adds invariance to pretrained networks without fine-tuning.
problem Adding invariance to pretrained networks without altering original behavior.
method Post-training augmentation invariance framework with Markov-Wasserstein minimization and Wasserstein correlation maximization losses.
result Adapter networks improve classification accuracy on rotated and noisy images.
This paper tackles overfitting in CTR models by introducing Multi-Epoch learning with Data Augmentation.
problem Overfitting of the embedding layer in CTR models during multi-epoch training.
method Introduces Multi-Epoch learning with Data Augmentation (MEDA) framework to reduce overfitting and enhance performance.
result MEDA minimizes overfitting and achieves data augmentation through varied embedding spaces, improving performance without overfitting.
Study adapts AI research methods to analyze image augmentation impacts on neural network operations.
problem Understanding how image augmentation affects neural network performance and sensitivity.
method Adapted treatment-control paradigm, uses variance decomposition, Sobol indices, and Shapley values for sensitivity analysis.
result Visualizes and quantifies sensitivity to different image augmentation parameters.
AugMask trains diffusion models on incomplete tabular data by augmenting missing values and applying denoising supervision.
problem Training diffusion models on incomplete tabular data with missing values.
method AugMask uses stochastic augmentation and denoising supervision to adapt diffusion models to incomplete data.
result AugMask enables diffusion-based tabular generators to outperform specialized missing-aware baselines across various datasets and missingness regimes.
Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new a…
New algorithms tackle data challenges in physics model selection.
problem Lack of labeled data, high dimensionality, and inapplicability of data augmentation techniques to physics data.
method Two algorithms: feature selection and data augmentation combined with classifiers and stacking ensemble.
result Achieved 90% accuracy on nonlinear structural mechanics classification problem.
A new method uses Hamiltonian Monte Carlo for imputation and augmentation of healthcare data.
problem Missing values in clinical studies lead to biased results and loss of statistical power.
method Folded Hamiltonian Monte Carlo (F-HMC) with Bayesian inference to handle high-dimensional, small sample size datasets.
result The method enriches the quality of data in precision, accuracy, recall, F1 score, and propensity metric.
New method solves constrained optimization problems efficiently.
problem Equality-constrained nonlinear, nonconvex optimization problems.
method Adaptive inexact Newton method with randomized iterative sketching.
result Global almost sure convergence and local linear/superlinear convergence.
New algorithm solves stochastic optimization problems with unknown gradients.
problem Solving nonlinear optimization problems with stochastic objectives and deterministic constraints.
method Adaptive SQP with differentiable exact augmented Lagrangian and stochastic line search.
result Global convergence established for both non-adaptive and adaptive SQP methods.
Role-wise data augmentation improves knowledge distillation effectiveness.
problem Existing knowledge distillation methods fail to utilize the full potential of teacher-student data interaction.
method Design and implement data augmentation agents with distinct roles for teacher and student.
result Specially tailored data points enhance the demonstration of teacher's knowledge to the student.
Paper introduces invariance-adapted latent space for contrastive learning.
problem Understanding the effectiveness of contrastive learning in data representation.
method Introduces invariance-adapted latent space and uses Lasso-type metric.
result Contrastive learning with Lasso-type metric can find an invariance-adapted latent space.
Enhances financial time-series prediction by adapting pre-trained models to new data.
problem Adapting pre-trained financial models to new data sets efficiently.
method Augmented Bilinear Network that retains and adjusts pre-trained neural network knowledge.
result Improves prediction performance and reduces model complexity.
Augmenting a neural network with memory that can grow without growing the number of trained parameters is a recent powerful concept with many exciting applications. We propose a design of memory augmented neural networks (MANNs) called Labeled Memory Networks (LMNs) suited for tasks requiring online adaptation in class…
Synthetic continued pretraining enhances model performance with synthetic data.
problem Data inefficiency in pretrained models when adapting to domain-specific documents.
method Synthetic data augmentation using EntiGraph to create a large synthetic corpus.
result Language models can answer questions and follow instructions without access to domain-specific documents.
Study improves GFM accuracy with time series augmentation.
problem Limited time series data hinders GFM performance.
method Data augmentation techniques (GRATIS, MBB, DBA) and transfer learning.
result Significant improvement in GFM accuracy over baseline.
RAG-IT automates financial analysis using LLMs and specialized datasets.
problem Manual financial analysis is time-consuming and requires expertise.
method Retrieval-Augmented Instruction Tuning (RAG-IT) fine-tunes an LLM for financial tasks.
result RAG-IT improves financial report generation performance compared to commercial systems.
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models see…
KOMET identifies Koopman operators from model parameter trajectories to adapt to evolving data distributions.
problem Adaptation of parametric models to non-stationary environments.
method Data-driven framework using Koopman operator identification and Extended Dynamic Mode Decomposition (EDMD).
result KOMET achieves high autonomous-rollout accuracies of 0.981 to 1.000 over 100 time steps on various drifting datasets.
Our article considers the class of recently developed stochastic models that combine claims payments and incurred losses information into a coherent reserving methodology. In particular, we develop a family of Heirarchical Bayesian Paid-Incurred-Claims models, combining the claims reserving models of Hertig et al. (198…
Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy. We propose a framework to augment this collaborative model-building with per-user dom…
Neurally Augmented ALISTA improves sparse reconstruction performance.
problem Improving sparse reconstruction performance with theoretical guarantees and empirical improvements.
method Integrates an LSTM network to compute adaptive step sizes and thresholds for each target vector during reconstruction.
result Empirical performance is further improved, especially as compression ratios become more challenging.
Financial Wind Tunnel generates versatile market data for model testing.
problem Inconsistent market dynamics across different scales and sources.
method Retrieval-augmented diffusion-based simulator integrating macro and micro patterns.
result Enhanced performance and adaptability of downstream models in complex markets.
Improved generalization with semantic perturbations using normalizing flows.
problem Overfitting in deep neural networks training.
method Use normalizing flows for generating semantically meaningful perturbations in latent space.
result Achieved 96.6% test accuracy on CIFAR-10 with ResNet-18, outperforming existing methods.
Physics-consistent method improves seismic inversion accuracy.
problem Challenges in seismic full-waveform inversion (FWI) due to ill-posedness and high cost.
method Hybrid approach combining physics-based models with data-driven methodologies, incorporating physics into data augmentation.
result Physics-consistent data-driven inversion yields higher accuracy and better generalization.
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made b…
Paper tackles test-time adaptation for tabular data.
problem Performance degradation due to distribution shifts in testing.
method Proposes FTAT for robustly adapting tabular models during testing.
result FTAT outperforms state-of-the-art methods on benchmark datasets.
Mixup improves model robustness and generalization by convexly combining examples.
problem Improving model robustness and generalization.
method Using Mixup augmentation in training, which involves convex combinations of pairs of examples and their labels.
result Mixup training helps models exhibit robustness to adversarial attacks and reduces overfitting.
Improved county-level COVID-19 forecasting model using LSTM and data augmentation.
problem Accurately forecasting county-level COVID-19 cases to optimize medical resources.
method Adapted TDEFSI-LONLY model, utilized LSTM, data augmentation, and inter-county mixing.
result CLEIR-Net model provides better forecasts than TDEFSI-LONLY.
This thesis explores fast algorithms for large matrices and data augmentation to improve model efficiency.
problem Efficient handling of large models and data in scientific computing and machine learning.
method Randomized low-rank decomposition algorithms and data augmentation techniques.
result Improved sample efficiency and generalization of machine learning models.
Contrastive learning benefits from generated data but can be harmed by it too.
problem Contrastive learning's reliance on data augmentation and the impact of generated data.
method Investigates the role of generated data in contrastive learning and proposes Adaptive Inflation (AdaInf).
result Generated data can sometimes harm contrastive learning, and AdaInf improves performance.
TEAFormers preserve multi-dimensional time series structures for better forecasting.
problem Traditional Transformers flatten multi-dimensional time series data, losing critical multi-dimensional relationships.
method Tensor-Augmented Transformer (TEAFormer) with Tensor-Augmentation (TEA) module.
result Significant performance enhancements in time series forecasting across benchmarks.