Estimates variance function using aggregation methods in regression models.
problem Estimating variance function in regression models.
method Two-step procedure involving model selection or convex aggregation, using two independent samples.
result Consistency of the proposed method in L2 error for MS and C aggregations.
Proposes a method for stable variable selection in high-dimensional data.
problem Challenges of variable selection in high-dimensional, correlated data.
method Resample-aggregate framework using diffusion models.
result Stable subset of predictors with calibrated stability scores.
New method improves CATE model selection with optimal regret rates.
problem Nontrivial task of selecting accurate CATE models.
method Causal Q-aggregation using doubly robust loss.
result Achieves optimal oracle model selection regret rates of log(M)/n.
Given a finite family of functions, the goal of model selection aggregation is to construct a procedure that mimics the function from this family that is the closest to an unknown regression function. More precisely, we consider a general regression model with fixed design and measure the distance between functions by …
Early-stopped aggregation improves computational efficiency in adaptive statistical inference.
problem Computational inefficiency in model selection and aggregation for adaptive inference.
method Early-stopped aggregation (ESA) framework: compute only a few simpler models using early-stopping criteria.
result ESA achieves optimal adaptive contraction rates in variational Bayes and frequentist settings.
New method aggregates GDS analyses of randomly selected interaction models to identify important factors in screening experiments.
problem Erroneous conclusions from main-effects models in screening experiments.
method Gauss-Dantzig Selector Aggregation over Random Models (GDS-ARM).
result Identifies important factors by aggregating GDS analyses of randomly selected interaction models.
Paper proposes a new method to aggregate multiple sources with different label distributions.
problem Aggregating from multiple target-shifted sources with different label distributions.
method Unified framework to select relevant sources for domain adaptation with limited label, unsupervised, and label partial unsupervised scenarios.
result Empirical results significantly outperform baselines.
A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.
problem Balancing computational efficiency and uncertainty quantification in Gaussian processes for big data.
method Using graphical models to select important experts and aggregate their predictions while ensuring uncertainty quantification.
result Substantially reduces computational cost of aggregating dependent experts while ensuring calibrated uncertainty quantification.
A new method for feature selection robust to noise and design variability.
problem Feature selection in high-dimensional regression under sampling variability and measurement error.
method Injects controlled additive noise into the design matrix, fits a base selector, and aggregates selection frequencies.
result Improved robustness compared to Stability Selection and standard base selectors.
Dynamic assessment of patient status (e.g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerting, decision support and resource allocation. Extraction and cleaning of expert-selected clinical variables discards information and protrac…
Active learning method reduces labeling cost for regression models with aggregated data.
problem Reducing labeling cost for training regression models with aggregated data.
method Sequentially selects sets to be labeled using mutual information quantifying model parameter uncertainty.
result Achieves better predictive performance with fewer labeled sets.
DAGgr aggregates multiple DAGs to stabilize causal structure learning.
problem Stability in learning causal structure from data.
method Model averaging of candidate DAGs weighted by predictive likelihood, with acyclicity enforced.
result DAGgr consistently outperforms individual DAGs and bootstrap-aggregation baselines.
A new method sorts projects using Quicksort and Bradley-Terry model for uncertain long-term benefits.
problem Selecting projects with uncertain long-term benefits.
method Combining Quicksort and Bradley-Terry model for ranking projects based on uncertain long-term benefits.
result Proposed methods outperform existing aggregation methods and can be combined with sampling techniques.
A conformal procedure improves CoT reasoning by aggregating reasoning paths and calibrating abstention rules.
problem Aggregation uncertainty in chain-of-thought reasoning makes correct answers less reliable.
method Introduces a conformal procedure for CoT reasoning that uses weighted score aggregation and abstention rules.
result Achieves higher selective accuracy with abstention, reducing confident-error rate.
Approach for modeling EHR data with rare features, improving prediction and interpretation.
problem Challenges in modeling rare binary features in EHR data.
method Tree-guided feature selection and logic aggregation for large-scale regression.
result Improved prediction and model interpretation of suicide risk in EHR data.
Paper introduces WWAggr for ensemble CPD, improving accuracy and decision threshold selection.
problem Challenges in detecting abrupt distribution shifts in high-dimensional data streams.
method Introduces WWAggr, a novel task-specific ensemble aggregation method based on Wasserstein distance.
result Demonstrates WWAggr outperforms standard aggregation techniques and decision threshold selection.
SACP aggregates nonconformity scores from multiple predictors to create more efficient uncertainty sets.
problem Combining predictive uncertainties from multiple models for efficient and reliable uncertainty quantification.
method SACP (Symmetric Aggregated Conformal Prediction) aggregates nonconformity scores using a flexible symmetric aggregation function.
result SACP consistently improves efficiency and often outperforms state-of-the-art model aggregation baselines.
COMA combines prediction sets from multiple models for online, adaptive prediction.
problem Combining multiple prediction models with uncertainty guarantees.
method Online model aggregation using weighted voting of conformal prediction sets.
result COMA retains coverage guarantees under negative correlation assumptions.
New method for multi-modal depth prediction challenges.
problem Challenges in applying unimodal coreset selection to multi-modal data.
method Adapted state-of-the-art coreset selection technique for multimodal data.
result Challenges in extending unimodal algorithms to multi-modal scenarios.
Simplifies GNN models by selecting important features for node classification.
problem Challenges in analyzing and selecting important features in GNN models.
method Decoupling feature aggregation and depth, using softmax and L2-normalization.
result FSGNN achieves comparable or higher accuracy than state-of-the-art GNN models.
A new method reduces complexity in estimating dynamic choice models.
problem Estimating structural parameters in dynamic discrete choice models using behavioral data.
method Two-stage approach: inverse reinforcement learning for Q-function estimation, state selection via clustering, and maximum likelihood estimation with nested fixed-point algorithm.
result The method mitigates the curse of dimensionality and provides finite-sample bounds on estimation error.
For massive data sets, efficient computation commonly relies on distributed algorithms that store and process subsets of the data on different machines, minimizing communication costs. Our focus is on regression and classification problems involving many features. A variety of distributed algorithms have been proposed …
Aggregates predictions from multiple regression models using random projections and kernel methods.
problem Combining predictions from multiple regression models to improve accuracy.
method Random projection of high-dimensional feature space, followed by kernel-based consensual aggregation.
result The aggregation scheme performs similarly to using the original high-dimensional features, with high probability.
U-aggregation combines multiple models without labels for better risk prediction.
problem Challenges in selecting best model for new populations due to limited data and lack of true labels.
method U-aggregation, an unsupervised model aggregation method that integrates pre-trained models without observed labels.
result U-aggregation improves genetic risk prediction of complex traits using publicly available models.
L1 regularized logistic regression has now become a workhorse of data mining and bioinformatics: it is widely used for many classification problems, particularly ones with many features. However, L1 regularization typically selects too many features and that so-called false positives are unavoidable. In this pape…
Majority Vote is optimal for reliable data labeling under certain conditions.
problem Reliable data labeling requires aggregating multiple annotators' labels, but the optimality of Majority Vote is not well understood.
method Characterized conditions under which Majority Vote achieves the optimal label estimation error.
result Majority Vote optimally recovers labels for a given class distribution under tolerable annotation noise limits.
Paper uses machine learning for stock prediction using fundamental data.
problem Predicting stock prices using fundamental data.
method Used three machine learning algorithms (FNN, RF, ANFIS) and feature selection for stock prediction.
result Random Forest (RF) model achieved the best prediction results.
AEW estimator achieves optimal risk in expectation for large enough temperatures.
problem Understanding minimax-rate optimality of AEW estimator in model selection aggregation.
method Analyzing AEW estimator with exponential weights for squared loss under random design.
result AEW achieves excess risk Tlog(M)/(n+1) in expectation for large enough temperatures. We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the L1-norm error fitting model with an assumption of the fitting function. The proposed algorithm generalizes the recent algorithm in the literature, aggregate and iterative disaggregate (AID), whi…
Two new algorithms reduce feature space while preserving non-linear relationships.
problem High-dimensional data and overfitting issues.
method Bias-variance analysis for non-linear transformations and generalized linear models.
result Competitive performance on regression and classification tasks.
Model selection for time series forecasting can be biased by the distribution of scores.
problem Model selection for probabilistic forecasting on time series data.
method Using proper scoring rules to aggregate scores across multiple time series.
result The mean score is immune to the skewness of the score distribution.
We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs. In contrast to current graph neural networks which follow a simple neighborhood aggregation scheme, our DNA procedure allows for a selective and node-adaptive aggregation of neighboring …
Study counterfactuals in combinatorial choice using a representative agent model.
problem Analyzing decision-making from aggregated binary polytope data.
method Nonparametric approach based on a representative agent model, solving polynomial and mixed-integer convex programs.
result Developed a method for counterfactual prediction that works even under model misspecification.
Improved graph neural networks by separating feature aggregation and depth.
problem Understanding feature importance in graph neural networks without prior information.
method Decoupling feature aggregation and depth, using softmax as a regularizer, and introducing 'Soft-Selector' and 'Hop-Normalization'.
result FSGNN model achieves up to 64% accuracy improvements in node classification tasks.
Dynamic defense against Byzantine poisoning in federated learning.
problem Byzantine poisoning attacks on federated learning models.
method Dynamic federated aggregation operator to filter out adversarial clients.
result Dynamic selection of clients improves global learning model performance.
Improved local explainer aggregation for interpretable machine learning models.
problem Improving the interpretability of black box machine learning models.
method Non-convex optimization and integer optimization framework for local explainer aggregation.
result Our method outperforms existing methods in terms of coverage and fidelity, particularly in multi-class settings.
Effective and accurate model selection is an important problem in modern data analysis. One of the major challenges is the computational burden required to handle large data sets that cannot be stored or processed on one machine. Another challenge one may encounter is the presence of outliers and contaminations that da…
The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directl…
We propose a communication-efficient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size N into m machines, and estimates a local sparse LDA estimator on each machine using the data subset of size N/m. After the distri…
One of the classical problems in machine learning and data mining is feature selection. A feature selection algorithm is expected to be quick, and at the same time it should show high performance. MeLiF algorithm effectively solves this problem using ensembles of ranking filters. This article describes two different wa…
Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and r…
The discovery of clinical biomarkers requires large patient cohorts and is aided by a pooled data approach across institutions. In many countries, data protection constraints, especially in the clinical environment, forbid the exchange of individual-level data between different research institutes, impeding the conduct…
In this paper, we consider the problem of "hyper-sparse aggregation". Namely, given a dictionary F={f1,...,fM} of functions, we look for an optimal aggregation algorithm that writes f~=∑j=1Mθjfj with as many zero coefficients θj as possible. This problem is of particular interest when…
Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction. Existing representation learning methods …
The paper establishes principles for initializing and designing GNNs with ReLU activations to avoid oversmoothing and correlation collapse.
problem Oversmoothing and correlation collapse in deep ReLU GNNs.
method The paper derives and validates three principles for initialization and architecture selection in finite width graph neural networks with ReLU activations.
result Correct initialization, residual aggregation operators, and residual connections significantly improve early training dynamics in deep ReLU GNNs.
Bayesian method learns from aggregated quantile data.
problem Limited access to sensitive personal data.
method Bayesian quantile matching estimation based on order statistics.
result Correctly reflects uncertainty of empirical quantiles.
Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states. Dynamic network configuration in convolutional ne…
Time series data in the retail world are particularly rich in terms of dimensionality, and these dimensions can be aggregated in groups or hierarchies. Valuable information is nested in these complex structures, which helps to predict the aggregated time series data. From a portfolio of brands under HUUB's monitoring, …