Paper uses bootstrapping to estimate ensemble methods' performance.
problem Estimating operating characteristics of ensemble methods.
method Bootstrap resampling for infinite resampling without refitting.
result Alternative methods improve predictive accuracy in meta-parameter selection.
The paper develops a safe policy gradient algorithm for reinforcement learning.
problem Safety issues in reinforcement learning for real-world control tasks.
method Stochastic optimization perspective, meta-parameter schedules, adaptive selection of step size and batch size.
result Monotonic improvement guarantees for a wide class of parametric policies.
Conditional meta-learning improves meta-learning performance in diverse task environments.
problem Meta-learning struggles with tasks that have heterogeneous complexity.
method Conditional meta-learning infers a task-specific meta-parameter vector.
result Conditional meta-learning outperforms standard meta-learning in diverse task environments.
New meta-learning framework for minimizing simple regret in bandits.
problem Minimizing simple regret in a sequence of bandit tasks with unknown distributions.
method Developed Bayesian and frequentist meta-learning algorithms for bandits, analyzing their meta simple regret.
result Bayesian algorithm achieves i l d e O ( m / n ) ilde{O}(m / \sqrt{n}) i l d e O ( m / n ) meta simple regret, frequentist algorithm i l d e O ( m n + m / n ) ilde{O}(\sqrt{m} n + m/ \sqrt{n}) i l d e O ( m n + m / n ) . Learning models of artificial intelligence can nowadays perform very well on a large variety of tasks. However, in practice different task environments are best handled by different learning models, rather than a single, universal, approach. Most non-trivial models thus require the adjustment of several to many learnin…
Paper tackles continual learning with single-index models, proving regret bounds.
problem Continual learning with single-index models across multiple tasks.
method Proposes a randomized strategy to learn a common single-index and task-specific link functions.
result Proves regret bounds for the proposed strategy under various loss function assumptions.
Proposes efficient user cold start recommendation via meta parameter partition.
problem User cold start in recommendation systems.
method Divides model parameters into fixed and adaptive parts, learning them separately offline and online.
result Significant improvement in AUC (2.48% absolute improvement).
CosML combines domain-specific meta-learners for cross-domain few-shot classification.
problem Generalizing to unseen domains while meta-learning on multiple seen domains.
method CosML trains domain-specific meta-learners and combines their meta-parameters in the parameter space.
result CosML outperforms state-of-the-art methods and achieves strong cross-domain generalization.
Natural gradient optimization improves model parameter estimation in graphical models.
problem Estimating model parameters in graphical models.
method Reformulated as an information geometric optimization problem, introduced natural gradient descent strategy.
result Natural gradient strategy leads to optimal parameter learning without fitting an incorrect distribution.
WRS improves CNN hyperparameter optimization.
problem Finding optimal hyperparameters for CNNs efficiently.
method Combination of Random Search and probabilistic greedy heuristic.
result WRS outperforms other methods in CNN hyperparameter optimization.
Integrates deep learning and logic reasoning for intelligent agents.
problem Combining deep learning and logic reasoning for robust decision-making.
method Deep Logic Models integrating deep learners and logic reasoning.
result Proposed models outperform other approaches in joint learning and inference.
This work introduces online meta-learning, merging paradigms to enhance continual learning.
problem Continuous learning of new tasks with fast adaptation.
method Follow the meta leader algorithm, extending MAML to online setting with theoretical guarantees.
result Significant performance improvement over traditional online learning approaches.
Simple linear relationship explains test performance differences in deep networks.
problem Understanding why two deep networks with identical training and architecture have different test performance.
method Showed that cross-entropy loss can lead to drastically different generalization performances for networks with different initialization or corrupted training.
result A linear relationship emerges between training and test losses, revealing the intrinsic problem of measuring test performance with cross-entropy loss.
Proposes incorporating noise sources in machine learning evaluation for more reliable conclusions.
problem Inadequate handling of nondeterminism in machine learning research leads to unreliable results.
method Uses linear mixed effects models (LMEMs) and generalized likelihood ratio tests (GLRT) to analyze performance evaluation scores and assess performance differences.
result Demonstrates how to incorporate various sources of noise and data properties into statistical significance testing and reliability analysis.
A tensor model for meta-learning adapts to task-specific features.
problem Learning shared representations for diverse tasks without task-specific observable information.
method Modeling meta-parameters as an order-3 tensor, estimating through tensor regression and method of moments.
result Tensor-based approach improves meta-learning performance with fewer samples.
Develops implicit MAML for efficient few-shot learning.
problem Efficient few-shot learning with limited data.
method Implicit differentiation for inner loop optimization.
result Agrees with inner loop optimizer choice and handles many gradient steps.
Meta-learning framework uses task similarity through nonparametric kernel regression.
problem Limited tasks and outliers/dissimilar tasks hinder meta-learning performance.
method Nonparametric kernel regression to quantify and use task similarity.
result Meta-learning algorithm outperforms existing methods in task-limited settings.
DiFF-RF detects point-wise and collective anomalies using random partitioning trees.
problem Detecting anomalies in data, especially collective anomalies.
method Random partitioning binary trees with distance-based leaves and semi-supervised learning.
result DiFF-RF significantly outperforms isolation forest and one-class SVM.
New method estimates selection event for black-box models.
problem Infeasibility of conditional selective inference due to unavailable selection event.
method Bootstrapping to estimate selection event and conditional distribution.
result Feasibility of conditional selective inference for models without exact selection event.
Cluster stability selection improves feature selection in correlated data.
problem Feature selection stability in correlated data.
method Cluster stability selection exploiting known cluster structure.
result Better predictive performance than lasso alone and stability selection.
New method corrects selection bias in post-selective inference for Group LASSO.
problem Inference after Group LASSO selection is unreliable.
method Develops a consistent, post-selective Bayesian method to adjust for selection bias.
result Corrects bias in recovering effects of selected variables.
Proposes PEMI for online selective conformal prediction with asymmetric rules.
problem Challenges of handling asymmetric selection mechanisms in online selective conformal prediction.
method PEMI: permutation-based framework for selective conformal prediction with arbitrary asymmetric selection rules.
result Achieves exact selection-conditional coverage for any asymmetric selection mechanism and any prediction model.
The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.
problem Online selective conformal prediction's exchangeability issues and false coverage rate control problems.
method Evaluation and correction of existing calibration selection strategies, proposing new ones that preserve exchangeability.
result Novel calibration selection strategies ensure both selection-conditional coverage and FCR control.
This work develops scalable model selection methods with fast update and selection.
problem Efficient model selection for large pools of candidate models.
method Isolated model embedding, which supports asymptotically fast update and selection.
result Standardized Embedder achieves competitive model selection performances.
Online method selects candidates from data streams, ensuring irreversible decisions.
problem Conformal selection's incompatibility with irreversible decisions in online scenarios.
method Online Conformal Selection with Accept-to-Reject Changes (OCS-ARC) incorporating online Benjamini-Hochberg procedure.
result OCS-ARC controls FDR at or below nominal level, improving selection power.
Stability Selection improves structured variable selection but requires careful tuning.
problem Finding a right-sized model or controlling false positives in structured selection problems.
method Stability Selection applied to group lasso and structured input-output lasso.
result Stability Selection often increases power but reduces error control reliability in structured settings.
Paper improves k k k -NN predictive performance with efficient variable selection.
problem Improving predictive performance of k k k -NN models. method Efficient forward selection of predictor variables.
result Novel approach approaches outperformance of stepwise selection models.
Optimizes biomarker selection for cost-effective treatment rules.
problem Incorporating multiple biomarkers in treatment selection rules can be costly and reduce model performance.
method Developed procedures for estimating linear and nonlinear combinations of biomarkers using 0-norm penalized weighted classification.
result Demonstrated the importance of feature selection and marker cost in treatment selection rules.
Value selection reduces model size while maintaining accuracy.
problem Space efficiency in model size reduction.
method Two probabilistic methods based on information theory's metric: PVS and P + VS.
result Value selection achieves balance between accuracy and model size reduction.
Proposes HSIC-Lasso for selective inference in non-linear data.
problem Detecting influential features in non-linear and high-dimensional data.
method Model-free HSIC-Lasso based on truncated Gaussians and polyhedral lemma.
result Tight control of type-I error even for small sample sizes.
Paper proposes a statistical test for feature selection pipelines using selective inference.
problem Assessing the significance of feature selection pipelines in data analysis.
method Selective inference technique applied to feature selection pipelines composed of various algorithms.
result The proposed statistical test controls false positive feature selection probabilities.
New Bitcoin coin selection method improves cost savings.
problem Improving cost savings in Bitcoin coin selection.
method Coin selection with leverage, allowing user-tunable parameters.
result Natural replacement for standard knapsack method.
More powerful feature selection tests using selective inference.
problem Selection bias in feature selection leading to specious analysis.
method Conditioning on minimal selection event using Maximum Mean Discrepancy and Hilbert Schmidt Independence Criterion with multiscale bootstrap.
result Proposed test is more powerful in most scenarios.
New method improves feature selection by integrating stability paths.
problem Improving feature selection with tighter false positive control.
method Integrating stability paths to strengthen theoretical bounds on E(FP).
result Significantly more true positives with same E(FP) control.
UniFeat is an open-source Java tool for feature selection.
problem Efficient feature selection in various research areas.
method Provides a set of advanced feature selection methods.
result Facilitates rapid development of new feature selection algorithms.
We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the sel…
Efficiently selects data points for deep learning using smaller proxy models.
problem Expensive data selection methods in deep learning.
method Uses a small proxy model to perform data selection.
result Improves data selection runtime by an order of magnitude without significant accuracy loss.
Proposes a new method using Copula Entropy for variable selection.
problem Variable selection in machine learning and statistics.
method Copula Entropy (CE) based ranks for variable selection, model-free and tuning-free.
result CE based method selects variables more effectively and derives better interpretable results.
Flexible selective inference using flow-based transport maps.
problem Selective inference with complex selection events.
method Flow-based generative modeling for conditional distribution approximation.
result Valid p-values and confidence sets for adaptively selected hypotheses and parameters.
Recently, many regularized procedures have been proposed for variable selection in linear regression, but their performance depends on the tuning parameter selection. Here a criterion for the tuning parameter selection is proposed, which combines the strength of both stability selection and cross-validation and therefo…
Develops a more powerful selective inference method for stepwise feature selection.
problem Loss of power in existing conditional SI methods due to over-conditioning.
method Uses homotopy continuation approach to overcome over-conditioning.
result Shows improved power and efficiency in selective inference for feature selection.
Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture…
Paper tackles selective regression using uncertainty estimation.
problem Selective regression for machine learning models to avoid predictions when uncertain.
method Model-agnostic non-parametric uncertainty estimation.
result Superior performance compared to state-of-the-art selective regressors.
A new method selects clean samples to train DNNs with noisy labels.
problem Training deep neural networks with noisy labeled data.
method Adaptive k-set selection to choose clean samples at each epoch.
result The method guarantees performance with a theoretical bound on regret.
IWeS selects examples by entropy-based importance sampling for subset selection.
problem Efficiently selecting examples for model training in batch settings.
method IWeS uses importance sampling based on model entropy to select examples.
result IWeS outperforms other subset selection algorithms on seven datasets.
ACS is an interactive framework for model-free selection with guaranteed error control.
problem Model-free selection with rigorous error control.
method Adaptive conformal selection with human-in-the-loop data exploration and new information incorporation.
result ACS provides concrete selection algorithms for various goals, including model update/selection, diversified selection, and incorporating new data.
NGP selects N features from P using neural networks in a greedy, iterative process.
problem Feature selection for non-linear prediction problems.
method Neural Greedy Pursuit (NGP) algorithm, selecting features sequentially in an iterative loss minimization procedure.
result NGP provides better performance than DeepLIFT and Drop-one-out loss methods.
Selective inference for group lasso estimators across various distributions and covariates.
problem Developing selective inference methods for group lasso estimators.
method Randomized group-regularized optimization problem with post-selection likelihood.
result Selective point estimator and Wald-type confidence regions for regression parameters.