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.
Characterizes uncertainty in low-rank matrix completion with noisy data.
problem Uncertainty quantification in low-rank matrix completion with heterogeneous sub-exponential noise.
method Characterizes the distribution of estimated matrix entries under low-rank estimators with heterogeneous sub-exponential noise.
result Explicit formulas for the distribution of estimated matrix entries under Poisson and Binary noise.
Matrix decomposition is a popular and fundamental approach in machine learning and data mining. It has been successfully applied into various fields. Most matrix decomposition methods focus on decomposing a data matrix from one single source. However, it is common that data are from different sources with heterogeneous…
The paper investigates how dataset quality and heterogeneity affect model confidence in machine learning.
problem Understanding how dataset quality and heterogeneity impact model confidence in machine learning.
method The study uses theoretical explanations and experimental demonstrations to investigate the effects of dataset size, label noise, and class heterogeneity on model confidence.
result Label noise reduces model confidence, while reduced dataset size increases it, and class heterogeneity leads to inconsistent confidence across classes.
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.
New model shows neural networks can use noise to improve long-tailed data classification.
problem Understanding overfitting in neural networks with long-tailed data.
method Refined feature-noise data model incorporating class-dependent heterogeneous noise.
result Neural networks can leverage data noise to learn implicit features improving long-tailed data classification.
MindFlayer SGD improves parallel SGD for heterogeneous, random compute times.
problem Minimizing nonconvex functions with heterogeneous, random compute times.
method MindFlayer SGD, designed for stochastic and heterogeneous delays.
result MindFlayer SGD outperforms existing methods in environments with heavy-tailed noise.
Introduces a new system for modeling bank solvency contagion with heterogeneous impacts and exposures.
problem Modeling bank solvency contagion with asymmetric interactions and heterogeneous exposures.
method Develops a heterogeneous McKean-Vlasov system to characterize solvency contagion in interbank markets.
result Derives a unique solution for the system under certain conditions, resolving instability issues.
EM algorithm speeds up convergence in federated learning with heterogenous data.
problem Understanding convergence rates of federated learning algorithms under data heterogeneity.
method Characterized convergence rate of EM algorithm for FMLR model under various regimes.
result EM algorithm converges to ground truth with SNR ≥ √K in all regimes.
New estimator robust to adversarial noise and data heterogeneity.
problem Sensitive to adversarial noise and poor performance with heterogeneous data.
method Distributionally robust estimator minimizing worst-case conditional expected loss over adversarial distributions.
result Efficiently finds non-parametric local estimates via convex optimization.
Estimates shared linear subspace from noisy data with multiple users.
problem Recovering shared linear subspace from noisy data with non-isotropic noise.
method Estimates shared subspace using at least two data points per user, avoiding restrictive assumptions.
result Upper and lower bounds for estimation error match, showing no additional error due to noise irregularity.
VSAE learns from missing heterogeneous data by modeling latent dependencies.
problem Learning from partially-observed heterogeneous data with missingness.
method Variational selective autoencoder (VSAE) models joint distribution of observed, unobserved, and missing data.
result VSAE improves over state-of-the-art models in data generation and imputation tasks.
Study on sparse recovery with mixed-quality data, establishing sample-size conditions.
problem Sparse recovery with heterogeneous noise from high- and low-quality sources.
method Establishes linear trade-off for sufficient conditions, analyzes LASSO algorithm.
result Linear trade-off for sufficient conditions, robustness of LASSO to data heterogeneity.
Method identifies unknown intervention targets in structural causal models from diverse data.
problem Identifying unknown intervention targets in structural causal models from heterogeneous data.
method Two-phase approach: first recovers exogenous noises, second matches with endogenous variables.
result Proposed method uniquely identifies intervention targets under causal sufficiency assumption.
Paper improves differential privacy SGD by considering data heterogeneity.
problem Improving differential privacy in machine learning with varying data contributions.
method Introducing influence function to quantify data contributions, designing PIDP-SGD algorithm.
result PIDP-SGD significantly improves machine learning model performance.
Paper proposes GPM for heteroscedastic PCA estimation.
problem Estimating ground truth from heterogeneous data.
method Generalized power method (GPM) for HQPOC.
result GPM achieves geometrically decreasing distances to ground truth.
KHGRec tackles noisy and incomplete KG-enhanced recommendations by modeling complex interactions.
problem Challenges in integrating KGs for accurate recommendations, especially in complex higher-order interactions and heterogeneous modalities.
method KHGRec uses a collaborative knowledge heterogeneous hypergraph (CKHG) to model group-wise interdependencies, employing two hypergraph encoders and attention mechanisms.
result KHGRec achieves an average 5.18% relative improvement over state-of-the-art baselines on four real-world datasets.
New method identifies nonstationary causal structures in time series data.
problem Identifying causal relationships in time series data that change over time.
method High-order Markov Switching Models for regime-dependent causal discovery.
result Scalable approach for estimating high-order regime-dependent causal structures.
HeMPPCAT improves PCA for data with varying noise.
problem PCA's suboptimal performance on data with heterogeneous noise.
method HeMPPCAT uses a GEM algorithm to estimate factors, means, and noise variances.
result Improved factor estimates and clustering accuracy compared to MPPCA.
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.
Theory and method for reducing prediction variance in noisy feature-subsampled ridge ensembles.
problem Reduction of prediction variance in noisy data with feature bagging.
method Developed analytical learning curves for noisy ridge ensembles, introduced heterogeneous feature ensembling.
result Subsampling shifts the double-descent peak, leading to improved performance over a single linear predictor.
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.
Paper develops efficient recursive learning for multi-channel systems with heterogeneous dynamics.
problem Accurately learning system dynamics in complex, multi-channel systems with nonlinear and noisy data.
method Formulates system as Gaussian process state-space models (GPSSMs), introduces heterogeneous multi-output kernel, and develops recursive inference framework.
result Matches SOTA offline GPSSMs in accuracy with 1/100 runtime, and outperforms SOTA online GPSSMs by 70% in accuracy under noise with 1/20 runtime.
Proposes a progressive label correction method for feature-dependent label noise.
problem Real-world large-scale datasets often suffer from heterogeneous, feature-dependent label noise.
method A progressive label correction algorithm that iteratively refines the model.
result A classifier trained with this strategy converges to be consistent with the Bayes classifier for various noise patterns.
A federated model learns shared archetypes from heterogeneous clients in continual learning.
problem Federated learning struggles with client heterogeneity and streaming distribution shifts.
method Clients encode their data as low-rank Hebbian operators, which are sent to a central server for aggregation and factorization into global archetypes.
result Improved global archetype reconstruction and associative retrieval in heterogeneous clients, drift, and novelty settings.
This paper studies how label noise affects Federated Learning.
problem The impact of label noise on Federated Learning.
method The paper derives an upper bound for the generalization error and conducts experiments on MNIST and CIFAR-10 datasets.
result The global model accuracy decreases linearly with increasing label noise, consistent with theoretical analysis.
In this paper, we present a multi-period trading model by assuming that traders face not only asymmetric information but also heterogenous prior beliefs, under the requirement that the insider publicly disclose his stock trades after the fact. We show that there is an equilibrium in which the irrational insider camoufl…
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.
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.
Framework for joint inference of network topology and interaction types in heterogeneous systems.
problem Joint inference of network topology, multi-type interaction kernels, and latent type assignments in heterogeneous interacting particle systems.
method Three-stage approach: shared structure recovery, discrete interaction type identification, and matrix factorization.
result The method yields accurate reconstruction of underlying dynamics and is robust to noise.
GeoERM learns shared representations on Riemannian manifolds for multi-task learning.
problem Heterogeneous and adversarial tasks in MTL.
method Geometry-aware MTL framework embedding shared representation on Riemannian manifold, optimizing via manifold operations.
result GeoERM improves estimation accuracy and stability, outperforming alternatives.
CAP adapts optimization to class attributes for better fairness.
problem Heterogeneities across classes impede classification performance.
method CAP generates class-specific learning strategies based on attributes.
result CAP improves over naive approach and is competitive with prior art.
PIE-PINN estimates elastic properties from noisy, low-res displacement data.
problem Estimating heterogeneous elastic properties from low-resolution, noisy data.
method Probabilistic Physics-Informed Neural Network (PIE-PINN) framework combining B-spline and hierarchical scale model.
result Robust estimation of Young's modulus and Poisson's ratio from noisy, low-resolution displacement data.
Drone optimizes collaborative learning for neural networks.
problem Training a shared neural network with heterogeneous data from multiple devices.
method Drone collects and aggregates model parameters from devices, optimizing trajectory for best accuracy.
result Significant improvement in final accuracy and speedup in training time.
A new method for designing accurate emulators using deep learning with interval calibration.
problem Designing accurate emulators for scientific processes with modern machine learning methods.
method Learn-by-Calibrating (LbC) approach based on interval calibration.
result Significant improvements in generalization error over widely-used loss functions.
New method removes hidden confounders for unbiased treatment effect estimation.
problem Bias in treatment effect estimation due to unobserved confounders.
method Proposes a new debiased estimation approach via SVD to handle heterogeneous confounding.
result Established rate of convergence for the estimator under different noise conditions.
New method uses neural networks for accurate angle estimation in noisy conditions.
problem Accurately estimate angles from noisy measurements in various applications.
method Directed Graph Neural Networks (GNNSync) for end-to-end trainable framework.
result GNNSync achieves competitive performance, even at high noise levels.
Our main task is to study the effect of corporate governance on the market liquidity of listed companies' stocks. We establish a theoretical model that contains the heterogeneity of investors' beliefs to explain the mechanisms by which corporate governance improves liquidity of the corporate stocks. In this process we …
We consider the matrix completion problem of recovering a structured matrix from noisy and partial measurements. Recent works have proposed tractable estimators with strong statistical guarantees for the case where the underlying matrix is low--rank, and the measurements consist of a subset, either of the exact individ…
Proposes a method to compare noisy high-dimensional datasets with low-dimensional manifolds.
problem Comparing distributions on manifolds in noisy high-dimensional datasets.
method Linking low-rank structure to manifold geometry, developing a scale-invariant distance measure.
result Superior robustness and statistical power compared to existing methods.
A new method estimates parameters in heavy-tailed corrupted regression with unknown covariance and heterogeneous noise.
problem Estimating parameters in regression with heavy-tailed errors and unknown covariance.
method Near-optimal computationally tractable estimator based on power method and Multiplicative Weight Update algorithm.
result The estimator achieves the optimal statistical rate and breakdown-point under near-optimal sample size.
We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Lu…
Heterogeneity is often natural in many contemporary applications involving massive data. While posing new challenges to effective learning, it can play a crucial role in powering meaningful scientific discoveries through the understanding of important differences among subpopulations of interest. In this paper, we expl…
New method stabilizes private LASSO for high-dimensional data with diverse covariate scales.
problem Privacy constraints and heterogeneity in covariate scales degrade LASSO stability and accuracy.
method Gram-based anisotropic objective perturbation to counteract covariate structure.
result Significantly improves convergence and statistical efficiency of private LASSO estimators.
Study resolves the Korean LVRP puzzle by showing HVRP exists but is masked by investor heterogeneity and improper intensity normalization.
problem Puzzling Low Volume Return Premium (LVRP) in Korea, contradicting global High Volume Return Premium (HVRP) evidence.
method Used Korean market data (2020-2024) to demonstrate HVRP exists but is masked by investor heterogeneity and improper intensity normalization. Normalized institutional buying intensity by market capitalization rather than trading value.
result Demonstrated a perfect monotonic relationship between highest-conviction institutional buying and positive cumulative abnormal returns, while lowest-intensity trades yield modest returns.
Develops robust learning methods for datasets with sub-populations.
problem Robust performance and generalization to unseen testing populations in datasets with sub-populations.
method Min-max-regret (MMR) formulation for distribution-free robust hierarchical model.
result Empirical MMR enjoys regret guarantees on training and unseen testing populations.
New model reveals balance crucial for robust neural coding.
problem Efficient neural coding in noisy, chaotic networks.
method Analytical model of balanced predictive coding with dissociated balance and weight disorder.
result Superclassical scaling in coding accuracy, independent of balance and weight disorder.
The paper tackles efficient exploration in MDPs to learn accurate models.
problem Efficient exploration in MDPs to learn accurate models.
method Formalizes the problem, introduces an algorithm for ε-accurate model estimation, and proposes a heuristic-based algorithm. result Heuristic-based algorithm outperforms original algorithm in small sample regime.