This study evaluates subgroup analysis methods for time-to-event outcomes in randomized controlled trials.
problem Identifying subgroups of good responders in non-significant randomized controlled trials.
method Evaluation of several subgroup analysis algorithms for time-to-event outcomes using synthetic and semi-synthetic data.
result Provides a new synthetic and semi-synthetic data generation process and an open-source Python package for benchmarking.
Paper analyzes D-SGD convergence with heterogeneous data and proposes topology learning.
problem Efficiently dealing with data heterogeneity in decentralized learning.
method Revisits D-SGD analysis, introduces neighborhood heterogeneity, and proposes topology learning.
result Formulates topology learning as a tractable optimization problem and demonstrates its effectiveness.
FedSARSA converges with heterogeneous agents, achieving linear speed-up.
problem Convergence analysis of Federated SARSA with heterogeneous agents.
method Linear function approximation, local training, multi-step error expansion.
result FedSARSA achieves linear speed-up with respect to the number of agents.
New method identifies subgroups in censored data.
problem Identifying meaningful patterns in heterogeneous populations.
method Combining inverse probability weighting, M-estimation, and concave pairwise fusion penalization.
result Robust approach for censored data under heterogeneous AFT models.
Proposes a new method to handle data heterogeneity in causal inference.
problem Challenges of collaborating between different data centers due to heterogeneity.
method Collaborative inverse propensity score weighting estimator to adjust for distribution shift.
result Significant improvements over traditional meta-analysis methods when dealing with increased heterogeneity.
New robust discriminant analysis for non-Gaussian data.
problem Classical discriminant analysis struggles with non-Gaussian distributions and contaminated datasets.
method Each data point follows its own ES distribution with arbitrary scale, leading to robust classification.
result Maximum-likelihood estimation and classification are simple, fast, and robust.
The paper tackles personalized policy learning from diverse data sources in a federated setting.
problem Learning personalized decision policies from observational bandit feedback across multiple heterogeneous data sources.
method Introduces a novel regret analysis for distinguishing global and local regret, and presents a federated policy learning algorithm using local policies trained with doubly robust offline policy evaluation strategies.
result Establishes finite-sample upper bounds on global and local regret, characterizing them by source heterogeneity and distribution shift.
The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate selection of the probabilities and priors of these bayesian models allows the model to better adapt to the data nature (i.e. heterogeneity, s…
Bayesian approach groups observations with similar effects for better inference.
problem Estimating heterogeneity across observations in political science.
method Structured sparsity framework integrated into Bayesian regression analysis.
result Method outperforms state-of-the-art methods for heterogeneous effects estimation.
A federated minimax framework for heterogeneous clients.
problem Training with edge devices having different datasets and capabilities.
method Proposes a federated minimax optimization framework with normalized updates.
result Improves convergence and communication complexity for nonconvex functions.
A distributed SGD method for heterogeneous networks with hubs and workers.
problem Learning in heterogeneous multi-level networks with worker heterogeneity and varying communication.
method Multi-Level Local SGD: distributed SGD with hub-and-spoke paradigm and hub averaging.
result The method converges with error dependent on worker heterogeneity, hub network topology, and iterations.
Federated survival analysis outperforms local and centralized training, with RSF offering the best balance of discrimination, calibration, and robustness.
problem Survival analysis models require large, diverse cohorts but are limited by privacy regulations and lack of centralized data.
method Federated learning (FL) is used to train shared models without exchanging raw data.
result FL consistently outperforms local training and approaches, and occasionally exceeds centralized performance.
Paper analyzes \FedAvg's convergence and introduces a new algorithm to reduce bias.
problem Analyzing convergence and bias in Federated Averaging.
method Markov property, first-order bias expansion, Richardson-Romberg extrapolation.
result Bias in \FedAvg can be decomposed into noise and client heterogeneity components.
New functional ME models for predicting heterogeneous functional data.
problem Statistical analysis of heterogeneous functional data for prediction.
method Functional Mixtures-of-Experts (FME) models with Lasso-like regularization for sparsity.
result Accurate capture of complex nonlinear relationships and clustering of heterogeneous regression data.
Bayesian approach clusters survival data for better risk prediction.
problem Identifying subpopulations with distinct risk profiles in survival analysis.
method Bayesian nonparametric approach in a clustered latent space.
result Consistent improvements in predictive performance and interpretability.
We study the fundamental tradeoffs between statistical accuracy and computational tractability in the analysis of high dimensional heterogeneous data. As examples, we study sparse Gaussian mixture model, mixture of sparse linear regressions, and sparse phase retrieval model. For these models, we exploit an oracle-based…
Unified analysis of federated learning with compression for various data distributions.
problem Communication overhead in federated learning with unreliable or limited communication.
method Periodic compressed communication and local gradient tracking schemes.
result Sharp convergence rates for various objective functions and data distributions.
This research tackles sample complexity in causal graph recovery with temporal heterogeneity.
problem Recovering a unique causal graph from observational data with temporal heterogeneity.
method Integrates time-series dynamics and multi-environment heterogeneity to constrain the problem, enabling a rigorous analysis of statistical limits.
result Unified necessary identifiability conditions and explicit information-theoretic bounds quantify the sample complexity under different noise distributions.
We propose the online machine learning for big data analysis with heterogeneity. We performed an experiment to compare the accuracy of each iteration between batch one and online one. It is possible to converge quickly with the same accuracy as the batch one.
Paper extends FFT-based differential privacy method to heterogeneous compositions.
problem Computing accurate differential privacy guarantees for mixed mechanisms.
method Uses Fast Fourier Transform (FFT) for error analysis and parameter selection.
result Provides tighter bounds for heterogeneous compositions compared to homogeneous cases.
This study analyzes decision-making in diverse environments where past data may not predict future outcomes.
problem How to make decisions when past data is not indicative of future outcomes due to unobserved confounders.
method Developed a framework to analyze and bound the performance of data-driven policies in heterogeneous environments.
result Established a method to upper bound the asymptotic worst-case regret of policies and analyzed the performance of Sample Average Approximation (SAA).
SOLVAR efficiently analyzes cryo-EM data's structural variability.
problem Analyzing continuous heterogeneity in cryo-EM data.
method Low-rank assumption on covariance matrix for tractable estimation.
result Accurately captures dominant components of structural variability.
Minibatch SGD outperforms Local SGD in heterogeneous distributed learning.
problem Optimizing a combined convex objective with stochastic gradient estimates from different machines.
method Analysis of Minibatch SGD and Local SGD in a heterogeneous distributed setting.
result Minibatch SGD dominates Local SGD in the heterogeneous distributed setting.
Generative models unify heterogeneous data for multimodal fusion.
problem Learning effective representations of mixed numerical and categorical data.
method Bayesian approach with exponential family distributions and Laplace-Bernstein approximation.
result Generative models enable fusion of multimodal data for various machine learning tasks.
Estimates population mean from user-level data with privacy, accounting for heterogeneity.
problem Heterogeneous user data with varying numbers of data points and distributions.
method Simple model of heterogeneous user data, differential privacy mechanism for estimation.
result Asymptotic optimality of the proposed estimator and general lower bounds on error.
Paper addresses data heterogeneity in federated learning for CoxPH models in healthcare.
problem Data heterogeneity in federated learning of CoxPH models for healthcare.
method Feature-based clustering and event-based reporting strategy.
result Enhanced model accuracy and performance in federated survival analysis.
We study heterogeneity in the effect of a mindset intervention on student-level performance through an observational dataset from the National Study of Learning Mindsets (NSLM). Our analysis uses machine learning (ML) to address the following associated problems: assessing treatment group overlap and covariate balance,…
FedSplit improves federated learning for heterogeneous data.
problem Data heterogeneity in federated learning degrades convergence and performance.
method FedSplit splits data into shared and personalized groups, optimizing a novel objective function.
result FedSplit converges faster and performs better than standard federated learning.
SUOD accelerates OD for large, diverse models.
problem Training and scoring new samples with many unsupervised, heterogeneous OD models.
method Data reduction, model approximation, and taskload optimization.
result SUOD accelerates OD for over 20 benchmark datasets and a real-world case.
DRFLM improves federated learning by handling data heterogeneity and noise.
problem Data heterogeneity and noise in federated learning.
method Distributionally robust optimization and mixup techniques.
result Enhanced global model prediction accuracy through robust optimization and local mixup.
We provide the first convergence analysis of local gradient descent for minimizing the average of smooth and convex but otherwise arbitrary functions. Problems of this form and local gradient descent as a solution method are of importance in federated learning, where each function is based on private data stored by a u…
The thesis clarifies when local updates outperform centralized methods in heterogeneous data environments.
problem Understanding when local updates are more effective than centralized or mini-batch methods in distributed optimization.
method Fine-grained consensus-error-based analysis framework, focusing on bounded second-order heterogeneity and third-order smoothness.
result Local updates outperform centralized or mini-batch methods under realistic models of data heterogeneity.
Proposes EDM algorithm to accelerate model training in distributed networks.
problem Hindered effectiveness of distributed stochastic optimization algorithms due to data heterogeneity and network sparsity.
method Introduces Exact-Diffusion with Momentum (EDM) algorithm, incorporating momentum techniques to mitigate bias and enhance convergence rate.
result EDM algorithm converges sub-linearly to the optimal solution, radius independent of data heterogeneity, for non-convex objective functions.
The paper introduces a new volatility model for state heterogeneous financial markets using high-frequency data.
problem State heterogeneity in financial volatility processes.
method Developed a state heterogeneous GARCH-Ito (SG-Ito) model based on continuous Ito diffusion process.
result Empirical studies reveal various state heterogeneities in S&P 500 index volatility.
Paper proposes a robust method for federated ICA with geometric median aggregation.
problem Federated ICA with permutation ambiguity in client estimations.
method Geometric median aggregation with k-means clustering to resolve permutation ambiguity.
result The method provably remains effective in highly heterogeneous scenarios.
We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical. To borrow strength across such heterogeneous datasets, we introduce a global parameter that emphasizes interpretability and statistical efficiency in the presence of heterogeneity. We also propose…
UNTIE learns representations of coupled categorical data.
problem Challenges in learning from unlabeled categorical data with complex couplings.
method UNTIE approach for unsupervised representation learning of heterogeneous couplings.
result UNTIE significantly improves categorical data representations on 25 diverse datasets.
New protocol reduces communication costs for heterogeneous bandits over complex networks.
problem Minimizing group regret in a multi-agent, heterogeneous bandit setting over complex networks.
method Flooding with Absorption (FwA) protocol for heterogeneous bandits over complex networks.
result FwA protocol significantly reduces communication costs compared to flooding while maintaining similar regret performance.
Develops efficient inference for noise heterogeneity in machine learning models.
problem Downstream procedures based on residuals can be biased in additive noise models.
method Semiparametrically efficient inference using a novel Hilbert-valued one-step estimator.
result Constructs tests and confidence intervals for residual independence and goodness of fit.
DyHATR learns dynamic heterogeneous networks for better link prediction.
problem Learning effective representations of dynamic heterogeneous networks for link prediction.
method Hierarchical attention for heterogeneous information and temporal RNN for evolutionary patterns.
result DyHATR significantly outperforms state-of-the-art baselines on link prediction tasks.
R2P method identifies homogeneous and heterogeneous subgroups for better treatment effect estimation.
problem Current subgroup analysis methods are weak in identifying homogeneous and heterogeneous subgroups and lack confidence estimates.
method R2P uses an arbitrary ITE estimator and quantifies uncertainty robustly.
result R2P produces more homogeneous and heterogeneous partitions than other methods.
MoReL models multi-omics data to find hidden molecular interactions.
problem Heterogeneous multi-omics data with varying quality and structure.
method Fused Gromov-Wasserstein (FGW) regularization in a deep Bayesian generative model.
result Enhanced performance in inferring meaningful interactions from real-world datasets.
New method calibrates heterogeneous treatment effect models.
problem Difficulty in estimating and calibrating heterogeneous treatment effects.
method Defined and proposed a robust estimator for HTE calibration, based on doubly robust treatment effect estimators.
result Proposed method evaluates calibration of learned HTE models, addressing overfitting and high-dimensionality.
Ringleader ASGD optimizes SGD for diverse edge devices with varying data and computation speeds.
problem Scalable distributed optimization with heterogeneous devices and data.
method Ringleader ASGD, an asynchronous SGD algorithm.
result Achieves optimal time complexity under data heterogeneity and arbitrary computation speeds.
Improves decentralized learning by optimizing graph mixing for data heterogeneity.
problem Data heterogeneity impacts convergence in decentralized learning, but existing methods ignore this.
method Characterized and quantified the relationship between graph mixing and data heterogeneity. Proposed an optimization approach to improve convergence.
result Our approach leads to improved test performance across various tasks.
Unified analysis of asynchronous-SGD algorithms for distributed learning.
problem Analyzing asynchronous-SGD in heterogeneous settings with varying speeds and data distributions.
method Unified convergence theory for non-convex smooth functions, including pure asynchronous SGD and its modifications.
result Unified convergence rates for various asynchronous algorithms, including novel methods.
This paper tackles objective inconsistency in federated optimization with heterogeneous clients.
problem Objective inconsistency due to heterogeneity in clients' datasets and computation speeds.
method General framework for analyzing federated heterogeneous optimization algorithms, including FedAvg and FedProx, and proposing FedNova.
result FedNova eliminates objective inconsistency while preserving fast error convergence.
New method uses untrusted data for more precise causal analysis.
problem Causal questions with limited trusted data.
method Incorporates untrusted data and trains richer models.
result Tighter, sounder prediction intervals.