Aims to describe neural network training dynamics using two-time-scale models.
problem Lack of a general mathematical description of neural network training.
method Introduces a theoretical framework based on two-time-scale population dynamics.
result Derives selection-mutation equations and effective fitness for hyperparameters.
Approach for recovering shared structure from multiple networks with unknown noise.
problem Recovering shared structure from multiple networks with unknown edge distributions.
method Exploits shared mean structure to denoise edge-level measurements and estimate population-level parameters.
result Established a finite-sample concentration inequality for low-rank eigenvalue truncation of a random weighted adjacency matrix.
Machine learning helps infer dark matter substructure from strong lensing images.
problem Extracting information about dark matter substructure from strong lensing images is challenging.
method Simulation-based inference techniques and neural networks trained on simulator data.
result Efficiently trained neural networks can estimate likelihood ratios for substructure parameters.
Integrates neural encoders into GLMMs for multimodal data analysis.
problem Scalable Bayesian inference for GLMMs assumes low-dimensional tabular predictors and does not handle high-dimensional modalities.
method Jointly learns modality-specific neural encoders with GLMM objective, performs variance-corrected stochastic-gradient MCMC.
result Preserves interpretable fixed and random effects while scaling to large longitudinal datasets.
CLSB models system dynamics from cross-sectional data with population-level regularization.
problem Challenges in modeling system dynamics from limited cross-sectional samples and heterogeneous individual behaviors.
method Introduces CLSB framework for learning dynamics, regularized for population-level temporal variations.
result Empirically superior in single-cell sequencing data analyses, e.g., simulating cell development and drug response.
TILT improves target domain performance by penalizing an auxiliary component on unlabeled target inputs.
problem Improving performance on target domain under covariate shift.
method TILT uses a novel objective function to decompose the source predictor and penalize an auxiliary component on unlabeled target inputs.
result TILT improves target domain performance over source-only training and other baselines.
New method pools labels from similar data items to improve learning from small samples.
problem Learning from small, human-annotated samples with potential disagreement among annotators.
method Proposes neighborhood-based pooling for sharing labels across similar data items.
result Improves learning from small, noisy samples by pooling labels from similar items.
Optimizes subgroup selection in clinical trials.
problem Identifying regions in feature space where a regression function exceeds a threshold.
method Formulates subgroup selection as constrained optimisation, determining minimax optimal rate for regret.
result Determines the minimax optimal rate for regret in sample size and Type I error probability.
Study shows fast rates for inverse reinforcement learning with linear rewards.
problem Entropy-regularized min-max inverse reinforcement learning in finite-horizon MDPs.
method Structural and statistical analysis of Min-Max-IRL with pseudo-self-concordance.
result Both trajectory-level KL divergence and parameter error decay at O ( n − 1 ) \mathcal{O}(n^{-1}) O ( n − 1 ) . Proposes φ φ φ -balancing for more balanced expert utilization in MoE models.
problem Balanced expert utilization in MoE models to avoid bias.
method Directly targets population-level balance by minimizing a convex potential function.
result Consistently outperforms prior methods in stability and effectiveness.
In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the id…
This work learns models for population dynamics using variational methods and higher-order quadrature.
problem Modeling population dynamics of physical systems with stochastic and mean-field effects.
method Variational problem to infer gradient fields, combining Monte Carlo sampling with higher-order quadrature rules.
result Accurate prediction of population dynamics over a wide range of parameters.
Proves EM algorithm guarantees for hierarchical imitation learning.
problem Learning hierarchical policies from expert demonstrations.
method Converts hierarchical imitation learning to latent variable model inference, analyzes EM algorithm.
result Proves EM algorithm converges to true parameter under regular conditions.
New algorithm balances personalization and statistical validity in MRTs.
problem Optimizing decisions in nonstationary settings with habituation and recovery.
method ROGUE-TS Thompson Sampling with probability clipping.
result Achieves lower regret and maintains high statistical power.
Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the …
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.
dLDS models neural dynamics as sparse combinations of simpler components.
problem Understanding complex neural dynamics at a population level.
method Proposes a decomposed dynamical system model trained through dictionary learning.
result Model efficiently captures and demix diverse neural dynamics.
Simulation-based inference speeds up gravitational wave data analysis.
problem High-dimensional parameter spaces and complex noise in gravitational wave data.
method Simulation-based inference methods using machine learning techniques.
result Simulation-based inference methods improve speed over traditional methods.
Efficiently learns exponential family distributions with i.i.d. samples.
problem Learning natural parameters of truncated exponential families efficiently.
method Proposes a novel loss function and computationally efficient estimator.
result Achieves optimal sample complexity and asymptotic normality.
Efficiently estimates variable importance in prediction tasks using Shapley values.
problem Valid statistical inference on the importance of variables in prediction tasks.
method Randomly sampling feature subsets to estimate Shapley Population Variable Importance Measure (SPVIM) efficiently.
result The proposed estimator converges at an asymptotically optimal rate and can construct valid confidence intervals and hypothesis tests.
The paper analyzes the generalization performance of spectral clustering algorithms and proposes new methods to improve their effectiveness.
problem Theoretical analysis of spectral clustering's generalization performance.
method Theoretical analysis and development of new spectral clustering algorithms.
result The excess risk bounds of spectral clustering algorithms have a O ( 1 / n ) \mathcal{O}(1/\sqrt{n}) O ( 1/ n ) convergence rate. Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimation process, where correspondence-based representation offers the most flexibility and ease-of-computation for population-level shape statisti…
New AI-block models for clustering high-dimensional variables based on maxima of random processes.
problem Clustering high-dimensional variables with weakly dependent maxima of random processes.
method Asymptotic Independent block (AI-block) models and an algorithm for variable clustering.
result The proposed AI-block models and algorithm can effectively identify clusters in high-dimensional data.
Models predict probabilities of causation from limited data.
problem Estimating probabilities of causation requires unreliable or impractical experimental and observational data.
method Proposed Exact-MLP and Mask-MLP models trained on reliable subpopulations.
result Models achieve average MAEs of roughly 0.03, reducing MAE by 80%.
This paper analyzes EM algorithm for softmax mixture models in high dimensions.
problem Modeling heterogeneous populations choosing from multiple attributes.
method Comprehensive analysis of the EM algorithm for softmax mixture models (SMMs), proving identifiability and convergence.
result EM algorithm recovers mixture atoms at near-parametric rate under suitable initialization.
SPIRE enables efficient federated learning for diffusion models by separating client-specific embeddings from a shared backbone.
problem Large diffusion models are impractical for federated learning due to their size.
method SPIRE separates the network into a global backbone and client-specific embeddings, enabling efficient finetuning.
result SPIRE achieves parameter-efficient finetuning, updating only a small fraction of weights.
Study finds conformity bias drives music sampling traditions.
problem How frequency-based bias drives cultural diversity in music sampling.
method Agent-based simulations in approximate Bayesian computation framework.
result Sampling patterns at population-level consistent with conformity bias.
Multi-party machine learning leaks global dataset properties even with black-box access.
problem Leakage of global dataset properties in multi-party machine learning.
method Demonstrated leakage of sensitive attribute distributions in pooled data.
result A curious party can infer sensitive attribute distributions in other parties' data with high accuracy.
We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by applications where response varies by individuals and therefore, estimating resp…
A new method combines synthetic data analysis and DP generation to produce accurate uncertainty estimates.
problem Invalid inferences from DP synthetic data analysis.
method Combining synthetic data analysis techniques from MI and NA Bayesian modeling with a novel noise-aware synthetic data generation algorithm.
result Accurate confidence intervals from DP synthetic data are produced, wider with tighter privacy.
We address the problem of estimating the parameters of a time-homogeneous Markov chain given only noisy, aggregate data. This arises when a population of individuals behave independently according to a Markov chain, but individual sample paths cannot be observed due to limitations of the observation process or the need…
o1Neuro neural network approximates complex functions and converges quickly.
problem Approximating complex functions and ensuring convergence in neural networks.
method Sparse indicator activation neurons, population and sample level convergence properties.
result o1Neuro achieves optimal model approximation and convergence with high probability.
The study analyzes the performance of statistical estimators under stability and computational efficiency.
problem Understanding the performance of statistical estimators in relation to stability and computational efficiency.
method Developed a framework to bound statistical accuracy based on the interplay between algorithm convergence rates and stability.
result Unstable algorithms can achieve the same statistical accuracy as stable ones in fewer steps.
We provide novel theoretical results regarding local optima of regularized M M M -estimators, allowing for nonconvexity in both loss and penalty functions. Under restricted strong convexity on the loss and suitable regularity conditions on the penalty, we prove that \emph{any stationary point} of the composite objective f…
Bayesian model transfers knowledge across different engineering fleets.
problem Data sparsity in predictive models for engineering infrastructure.
method Hierarchical Bayesian approach with multitask learning.
result Improves survival analysis and power prediction in truck fleets and wind farms.
New method detects change-points in population genetics.
problem Identifying homozygosity islands in a population.
method Penalized maximum likelihood approach with dynamic programming and greedy algorithms.
result Consistent detection of homozygosity islands in population genetics.
Paper proposes robust method to detect risk heterogeneity across ethnic groups.
problem Detecting risk heterogeneity across ethnic groups in ICU studies.
method Proposes a robust framework using Neyman orthogonality for inference.
result Demonstrates improved inferential stability and reduced bias compared to standard methods.
The paper explores various forms of calibration scores and their implications for fairness.
problem The evaluation of probabilistic predictions through calibration.
method The authors organize three grouping choices and one agglomeration of group errors, providing a framework for comparing and creating new calibration scores.
result The study demonstrates that appropriate choices of grouping can provide notions of (sub-)group or individual fairness.
Framework sharpens causal effect estimates without external assumptions.
problem Estimating causal effects under unmeasured confounding.
method Information-theoretic divergence bounds, Neyman orthogonality, machine learning.
result Sharp partial identification of conditional causal effects from observational data.
Study shows MDA's effectiveness even when more components are assumed than in actual data.
problem Classification error in overspecified Mixture Discriminant Analysis.
method Two-component Gaussian mixture model, EM algorithm, theoretical analysis of convergence and error rates.
result EM algorithm converges exponentially fast to Bayes risk with suitable initialization.
We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on n n n i.i.d. samples are known to have lower accuracy than the classical n − 1 2 n^{- \frac{1}{2}} n − 2 1 error. We investigate whether the Expectation-Maximization (EM) algorithm also converges slowly for these m…
New algorithms improve robust estimation in contaminated Gaussian models.
problem Simultaneous estimation of location and variance matrix in contaminated Gaussian models.
method Tractable adversarial algorithms with spline discriminators for robust estimation.
result Achieve minimax optimal rates or near-optimal rates under Huber's contamination model.
New algorithms achieve optimal robustness in stochastic convex optimization under contamination.
problem Determining optimal rates for robust stochastic convex optimization under ε ε ε -contamination. method Developed novel algorithms achieving minimax-optimal excess risk under ε ε ε -contamination model without stringent assumptions. result Achieved minimax-optimal excess risk (up to logarithmic factors) under ε ε ε -contamination model. New method reduces bias in estimating causal effects from discretized variables.
problem Bias in estimating causal effects from discretized continuous variables.
method Proposes a bias-reduced functional that evaluates outcome regression at within-bin conditional means.
result Demonstrates substantial bias reduction and near-nominal confidence interval coverage.
The paper develops a method to infer model parameters and shared dynamics from related physical systems using data.
problem Calibrating models to match data when detailed system properties and laws are unknown.
method Hierarchical Bayesian framework, adaptive surrogate models, bilevel optimization.
result Joint estimation of individual model parameters and shared dynamics using data from related systems.
Kernel discriminant analysis uses nonlinear embeddings to improve classification.
problem Limited effectiveness of linear discriminant analysis in capturing nonlinear features.
method Study of nonlinear embeddings in kernel discriminant analysis using polynomial and Gaussian kernels, solving generalized eigenvalue problems.
result Polynomial and Gaussian discriminants capture class differences through population moments and randomized projections.
Two methods preserve tensor structure for reduced dimensionality in tensor regression.
problem Reducing dimensionality of tensor predictors for improved interpretation and accuracy.
method Developed two tensor dimension reduction methods using Tucker and CP decompositions.
result Substantial improvement in accuracy over existing methods in simulations and applications.
Framework for online resource allocation using social welfare functions.
problem Optimal allocation of resources over time steps in a population.
method Confidence sequence framework for SWF-based online learning and inference, valid for any monotonic, concave, and Lipschitz-continuous SWF.
result Achieves near-optimal regret of i l d e O ( n + n k T ) ilde{O}(n+\sqrt{nkT}) i l d e O ( n + nk T ) for SWF-agnostic algorithm SWF-UCB.