This paper improves neural network generalization by dynamically learning kernel parameters.
problem Improving neural network generalization and adaptability.
method Diagonal adaptive kernel model that learns kernel eigenvalues and output coefficients during training.
result The diagonal adaptive kernel model significantly improves generalization over fixed-kernel methods.
FOCA method prevents co-adaptation between feature extractor and classifier.
problem Co-adaptation between feature extractor and classifier degrades neural network performance.
method FOCA method uses randomly-generated, weak classifiers to optimize feature extractor without explicit co-adaptation.
result FOCA features form a point-like distribution within the same class under special conditions.
New method learns domain-invariant local feature patterns for unsupervised domain adaptation.
problem Performance degradation due to domain-shift in unsupervised domain adaptation.
method Jointly learns domain-invariant local feature patterns and holistic feature distributions.
result Superior performance on benchmark datasets compared to state-of-the-art methods.
Improves unsupervised domain adaptation by enforcing feature extractor to focus on task-relevant information.
problem Leveraging label information from source domain for accurate target domain models without labels.
method Variational Information Bottleneck (VBDA) method that explicitly enforces feature extractor to ignore irrelevant task factors.
result Significantly outperforms state-of-the-art methods across three domain adaptation benchmark datasets.
Interventional domain adaptation improves feature transferability by removing spurious correlations.
problem Improper feature transferability due to spurious correlations in domain adaptation.
method Intervention strategy using unlabeled target data to generate counterfactual features and train discriminability invariance.
result Consistent performance improvements over state-of-the-art approaches in various domain adaptation tasks.
AFN learns adaptive-order feature interactions for better predictive models.
problem Learning optimal feature interactions in predictive models.
method AFN uses a logarithmic transformation layer to learn arbitrary-order cross features adaptively.
result AFN outperforms state-of-the-art models on four real datasets.
Adapts feature generation for structured prediction under budget constraints.
problem Structured prediction under test-time budget constraints.
method Adaptive feature generation during test-time, reducing computational cost.
result Strong performance in reducing feature costs without degrading accuracy.
Method evaluates classification uncertainty with adaptively chosen features.
problem Finding a balance between model efficiency and fairness.
method Adaptively selects features for equalized coverage in classification.
result Valid and effective method demonstrated on simulated and real data.
Greedy PIG adapts integrated gradients for better feature attribution.
problem Interpreting deep learning model predictions.
method Unified discrete optimization framework for feature attribution and selection.
result Greedy PIG improves feature attribution on various tasks.
Supporting evidence for adaptive feature program across diverse models.
problem Analyzing feature learning in neural networks.
method Over-parameterized sequence models and feature error measure (FEM).
result FEM is decreasing during training of adaptive feature models.
ASA improves ASR by adapting SD models to SI model's deep feature distribution.
problem Improving ASR performance on new speakers with limited data.
method Adversarial learning to regularize SD model's deep features to match SI model's.
result ASA achieves significant word error rate improvements over SI models.
Adaptive template systems improve feature extraction from persistence diagrams for machine learning.
problem Feature extraction from persistence diagrams for machine learning.
method Adaptive template systems using CDER, GMM, and HDBSCAN algorithms.
result Adaptive template systems yield competitive and often superior results in classification tasks.
AdapVAE learns streaming data clustering and feature learning adaptively.
problem Adaptive clustering and feature learning for streaming data.
method Bayesian Nonparametric (BNP) modeling with Deep Neural Networks (DNNs) for feature learning, online variational inference algorithm.
result AdapVAE can adaptively detect novel clusters in emerging data without catastrophic forgetting.
This paper explores how effective sample size, dimensionality, and model performance are related in covariate shift adaptation.
problem Understanding the relationship between effective sample size, dimensionality, and generalization in covariate shift adaptation.
method Building a unified theory connecting effective sample size, data dimensionality, and generalization in the context of covariate shift adaptation.
result Dimensionality reduction or feature selection can increase effective sample size, supporting the practice of reducing dimensionality before covariate shift adaptation.
TAROT enhances robustness and domain adaptability with domain-invariant features.
problem Developing models robust to adversarial attacks across diverse domains.
method Derives a new generalization bound and proposes TAROT algorithm.
result TAROT outperforms state-of-the-art methods in accuracy and robustness.
This review categorizes domain adaptation methods without target labels.
problem How to train a classifier from a source domain to generalize to a target domain.
method Sample-based, feature-based, and inference-based methods.
result Recurring ideas and conditions for cross-domain generalization error.
Semi-generative model learns causes and effects for covariate-shift adaptation.
problem Covariate shift adaptation with unlabelled data and causal features.
method Combines semi-supervised learning with causal features XC and XE. result Significant improvements in classification over baselines.
New benchmark and COAL model tackle class-imbalanced domain adaptation.
problem Aligning feature and label distributions across domains with different label distributions.
method COAL model combining feature and label distribution alignment.
result COAL model outperforms recent domain adaptation methods.
CoSCA improves unsupervised domain adaptation by better aligning ambiguous target samples.
problem Missing alignment of ambiguous target samples in unsupervised domain adaptation.
method CoSCA explicitly incorporates intra- and inter-class domain discrepancy, estimating label hypotheses and optimizing a contrastive loss with MMD for better global alignment.
result CoSCA outperforms state-of-the-art approaches in producing more discriminative features.
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.
Proposes WDGRL for better domain adaptation.
problem Improving performance on target domain via source domain knowledge.
method WDGRL uses Wasserstein distance to learn domain invariant feature representations.
result WDGRL outperforms state-of-the-art approaches in domain adaptation.
The paper adapts step sizes in TD learning to identify relevant features.
problem Identifying which features are relevant for temporal-difference learning.
method Adapting step sizes in stochastic gradient descent for feature relevance in TD learning.
result TD IDBD effectively distinguishes relevant features in gridworld and robotic tasks.
No-trick kernel adaptive filtering uses deterministic features for scalability and robustness.
problem Scalability issues in kernel methods for large datasets.
method Deterministic feature-map construction using polynomial-exact solutions.
result Deterministic features outperform random Fourier features in performance and scalability.
Aligns uncertainty predictions for domain adaptation using pre-trained deep networks.
problem Domain adaptation with unlabelled target data.
method Adversarial learning to align uncertainty predictions between source and target domains.
result Class prediction uncertainty on target domain matches source domain.
This work bridges two views of feature learning in neural networks.
problem The relationship between kernel scale changes and data-adaptive feature learning in neural networks remains unresolved.
method Using statistical mechanics, the work derives analytical expressions for network output statistics across scaling regimes.
result Kernel adaptation can be reduced to an effective kernel rescaling, but multi-scale adaptive approach provides richer insights.
MetaVRF learns adaptive kernels for fast few-shot learning.
problem Few-shot learning with limited data.
method MetaVRF with latent variable model and variational inference.
result MetaVRF produces kernels with high representational power and fast adaptation.
DMFAW improves multi-view clustering with adaptive weights and feature selection.
problem Lack of effective feature selection and empirical hyperparameter selection in existing deep matrix factorization methods.
method Introduces Deep Matrix Factorization with Adaptive Weights (DMFAW) for multi-view clustering, incorporating feature selection and dynamically updating weights using Control Theory.
result DMFAW outperforms state-of-the-art methods in clustering performance.
New decentralized KRR algorithm adapts to node-specific data.
problem Consistent node-specific data in decentralized KRR.
method Data-dependent random features for adaptive RF generation.
result Average regression accuracy improved by 25.5% across six datasets.
AFS-BM improves model accuracy by dynamically selecting features.
problem Feature selection challenges in ML, especially scalability and adaptability.
method Joint optimization for feature selection and model training with binary masking.
result AFS-BM achieves significant improvements in model accuracy and computational efficiency.
Multi-task feature learning aims to identity the shared features among tasks to improve generalization. It has been shown that by minimizing non-convex learning models, a better solution than the convex alternatives can be obtained. Therefore, a non-convex model based on the capped-ℓ1,ℓ1 regularization wa…
Adapts IG for better feature attributions and robustness.
problem Reliability concerns in feature attributions for deep learning models.
method Adaptation of path-based feature attribution to Riemannian geometry of data manifolds.
result IG along geodesics generates more intuitive and robust explanations.
New method restores source features for SFDA without source data.
problem Domain adaptation without access to source data.
method Feature Restoration (FR) and Bottom-Up Feature Restoration (BUFR).
result BUFR outperforms existing SFDA methods in accuracy, calibration, and data efficiency.
Improves unsupervised domain adaptation methods by aligning class conditional distributions.
problem Domain shift between source and target domains makes supervised learning models fail to generalize.
method Co-regularized domain alignment, creating multiple feature spaces and aligning them individually while encouraging agreement across class predictions.
result Significant performance improvements on domain adaptation benchmarks.
FedCONST adapts update magnitudes to enhance feature generalization in FL.
problem Heterogeneous client data in FL leads to overfitting and distorted transferable features.
method FedCONST uses linear convex constraints to stabilize training and preserve generalization.
result FedCONST enhances feature transferability and robustness, achieving state-of-the-art performance.
Random matrix theory explains how neural networks adapt to data.
problem Understanding how neural networks learn and generalize from data.
method Random matrix analysis of two-layer neural networks.
result Sharp characterization of feature spectrum and generalization error.
Proposes joint domain alignment and discriminative feature learning for deep domain adaptation.
problem Reduces domain shift and misclassification of target domain samples.
method Instance-based and center-based discriminative feature learning methods.
result Learning discriminative features in shared feature space significantly boosts deep domain adaptation performance.
Distributed sensors compress and send features to a fusion center for linear regression.
problem Efficiently compress and transmit features from distributed sensors to a fusion center under varying communication constraints.
method Designs a distributed and adaptive feature compression scheme using optimal quantizers and simple adaptive strategies.
result Demonstrates improved inference performance through simulated experiments.
New method generates data across domains using latent variables.
problem Understanding and adapting to distribution changes across domains.
method Causal Generative Domain Adaptation Network (CG-DAN) with latent variable decomposition.
result CG-DAN improves learning efficiency and data generation across domains.
FLDA adapts classifiers across domains using feature-level transfer models.
problem Adapting classifiers to different data distributions.
method FLDA models domain dependence with a feature-level transfer model, then trains a classifier to minimize expected loss.
result FLDA performs similarly to state-of-the-art techniques on real-world problems.
New online feature selection method handles streaming data with concept drift.
problem Handling streaming data with concept drift and sparsity.
method Online feature screening method with model adaptation.
result Online screening methods with model adaptation outperform without model adaptation on data streams with concept drift.
New method ranks network features in real-time for adaptive IDS.
problem Static feature selection ignores dynamic network data.
method Incremental learning of SVM for real-time feature ranking.
result Adapts to changes in normal network behavior and new attack patterns.
New method selects correlated features for unsupervised domain adaptation.
problem Improving unsupervised domain adaptation performance.
method Optimal transport theory for feature selection.
result Proposes a novel algorithm for feature selection.
Paper tackles online budgeted learning for feature acquisition in machine learning.
problem Finding optimal feature values to acquire from each instance in a data stream under budget constraints.
method Introduces a general framework for online budgeted learning, proposing two feature value acquisition policies: random and adaptive.
result Adaptive policies outperform random policies for most budget limitations and datasets, achieving near-optimal results in some cases.
A new method learns both global and local features for domain adaptation.
problem Lack of local relationship learning between instances in different domains.
method Dual autoencoders (MDAad and MMDA) for global and local feature learning, leveraging label information.
result Outperforms state-of-the-art methods in domain adaptation tasks.
The study analyzes transfer learning in infinite-width neural networks, improving generalization on target tasks.
problem Improving generalization in neural networks when using pretraining on a source task.
method Developed a theory under gradient flow for infinitely wide networks, analyzing fine-tuning and joint pretraining.
result Summary statistics of randomly initialized networks after pretraining are adaptive kernels that depend on both source and target data.
Proposes CDTD, a diffusion model for mixed-type tabular data.
problem Adapting diffusion models to mixed-type tabular data.
method Score matching and score interpolation for continuous features, adaptive noise schedules for categorical features.
result Consistently outperforms state-of-the-art models in mixed-type tabular data.
We introduce an unsupervised approach to efficiently discover the underlying features in a data set via crowdsourcing. Our queries ask crowd members to articulate a feature common to two out of three displayed examples. In addition we also ask the crowd to provide binary labels to the remaining examples based on the di…
CAM-GAN improves GANs for continual learning with efficient feature map transformations.
problem Efficient continual learning for GANs with reduced parameter growth.
method Designing and leveraging parameter-efficient feature map transformations, including global and task-specific parameters, residual bias, and Fisher information matrix.
result Significantly improved model performance and high-quality samples with fewer parameters.