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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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…
Proposes a method to align and differentiate feature clusters for unsupervised domain adaptation.
problem Difficulty in obtaining labeled data for domain adaptation.
method Label propagation and cycle consistency to align feature clusters.
result Successfully formed aligned and discriminative clusters for better domain adaptation.
This work proves convergence of adaptive resampling for random Fourier features.
problem Sampling Fourier frequencies well for high-dimensional data.
method Data adaptive resampling of Fourier frequencies, asymptotically optimal.
result Proves convergence of adaptive resampling method for regression and classification problems.
AdaEnsemble learns adaptive feature interactions for CTR prediction.
problem Learning feature interactions for CTR prediction in recommender systems and Ads ranking.
method AdaEnsemble is a Sparsely-Gated Mixture-of-Experts (SparseMoE) architecture that dynamically selects feature interaction depth.
result AdaEnsemble achieves better prediction accuracy and inference efficiency compared to state-of-the-art models.
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.
Adaptive Group Lasso selects important features in neural networks.
problem Lack of interpretability in neural networks.
method Adaptive Group Lasso for feature selection.
result Consistent feature selection for neural networks with theoretical guarantee.
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.
Guided adaptive shrinkage uses co-data to improve feature selection in genomic studies.
problem Feature selection challenges in high-dimensional genomics data, especially in clinical settings.
method Guided adaptive shrinkage methods that use co-data to adapt shrinkage parameters.
result Improves feature selection in genomic studies, demonstrated through comparisons and examples.
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.
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental le…
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.
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.
Discriminative active learning reduces data annotation costs for domain adaptation.
problem Conditional shift problem hinders domain adaptation between related but different domains.
method Three-stage active adversarial training: invariant feature space learning, uncertainty and diversity criteria, re-training with queried labels.
result Empirical comparisons show the proposed approach is more effective than existing methods.
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.
DFIV uses deep features for IV regression, achieving optimal rates.
problem Optimal IV regression with deep features for complex target functions.
method Two-stage approach: deep feature learning followed by IV regression.
result DFIV achieves minimax optimal learning rate under certain conditions.
Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature-level domain adaptation (FLDA), …
A framework isolates and learns approximately shared features for better domain adaptation.
problem Reducing performance degradation in unseen domains using machine learning models.
method Statistical framework distinguishing feature utilities based on correlation variance across domains. Learning approximately shared features from source tasks and fine-tuning on target tasks.
result Improved population risk compared to previous results on both source and target tasks, resolving the paradox of feature selection.
Proposes TFDF to learn transferable and discriminative features for unsupervised domain adaptation.
problem Difficult to induce supervised classifier without labeled data in unsupervised domain adaptation.
method TFDF optimizes transferability and discriminability by aligning distributions and minimizing class confusion.
result TFDF achieves better performance on real-world datasets compared to existing methods.
Adaptive regularization prevents overfitting in large-scale sparse feature models.
problem Overfitting in models with large-scale sparse categorical features.
method Adaptive regularization of embedding layers' norm budget.
result Improves model performance within a single epoch and prevents multi-epoch performance degradation.
ACNet improves CNNs by adaptively connecting feature nodes.
problem Improving traditional CNNs for better performance and handling non-Euclidean data.
method Adaptively connects feature nodes to switch between global and local inference.
result ACNet achieves state-of-the-art performance and overcomes limitations of MLP and CNN.
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.
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…
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.
AdaTrans adapts to feature and sample transfer in high-dimensional regression.
problem High-dimensional linear regression with more features than samples.
method F-AdaTrans and S-AdaTrans methods using fused-penalties and adaptive weights.
result AdaTrans achieves convergence rates close to oracle estimators and near-minimax optimal rates.
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.
In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory. We build upon a recent theoretical analysis of optimal transport in domain adaptation and show that it can directly suggest a feature selection procedure leveraging the shift …
New SAE algorithm proves feature recovery for LLMs with theoretical guarantees.
problem Achieving interpretable features in large language models (LLMs).
method Proposed a statistical framework and bias adaptation technique for sparse autoencoders (SAEs).
result Proved correct recovery of all monosemantic features under specific data sampling.
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.
Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domai…
Continuous dropout mimics brain neuron firing rates to prevent feature detector co-adaptation.
problem Preventing overfitting in deep neural networks.
method Extending binary dropout to continuous dropout, inspired by brain neuron firing rates.
result Continuous dropout improves test performance by preventing feature detector co-adaptation.
A new method for adapting to label shifts using class probability matching.
problem Adapting to label shifts where class probabilities differ between source and target domains.
method Class Probability Matching using Kernel Methods (CPMKM) framework.
result CPMKM outperforms existing methods on real datasets.
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.
Enhances DGPs with adaptive RKHS Fourier features for better non-stationary pattern modeling.
problem Capturing complex non-stationary patterns in non-linear dynamical systems.
method Integrates ODE-based RKHS Fourier features into DGPs using convolution operations for adaptive amplitude and phase modulation. Uses a doubly stochastic variational inference framework.
result Improved predictive performance across various regression tasks.