This work develops a model to distinguish network and covariate information.
problem Identifying unique network and covariate information.
method Low-rank model with two-step estimation: spectral method followed by refinement.
result The method accurately recovers joint and individual components.
Study connects covariance cleaning theory to information theory for heavy-tailed distributions.
problem Optimizing covariance matrices for heavy-tailed distributions using information theory.
method Minimizing Frobenius norm and information loss between true and estimated covariance matrices.
result Asymptotic regime of large matrices minimizes information loss for Student's t distributions.
Measuring information value in markets using covariance of price changes and order flow.
problem Determining the value of information in financial markets.
method Using high-frequency data on US equities, the covariance between price changes and order flow is estimated to measure information value.
result The aggregate value of information is about 0.04% of market cap, significantly lower than fees investors pay.
Bayesian optimization improves with nonstationary covariance functions.
problem Stationary covariance functions fail to capture prior information in high dimensions.
method Proposes nonstationary covariance functions to encode prior information and adaptively promote local exploration.
result Nonstationary covariance functions increase sample efficiency in high dimensions.
New criterion improves predictive evaluation in weighted inference scenarios.
problem Improving predictive evaluation in scenarios with different likelihoods for estimation and evaluation.
method Developed the posterior covariance information criterion (PCIC) to handle weighted likelihood inference.
result PCIC is asymptotically unbiased for quasi-Bayesian generalization error in weighted inference.
This paper improves error estimation in covariate shift by incorporating target information.
problem Error estimation is inaccurate in covariate shift scenarios.
method Proposes a redefinition of importance using target information for better error estimation.
result Incorporating target information leads to more accurate error estimation, especially with KLIEP.
Semi-supervised method boosts two-sample testing with covariate data.
problem Two-sample testing with covariate information.
method Semi-supervised kernel test with asymptotic normality.
result Higher asymptotic power compared to existing methods.
New method prevents posterior collapse in iVAE models.
problem Posterior collapse in iVAE models where observations and ICs are independent given covariates.
method Developed CI-iVAE by considering a mixture of encoder and posterior distributions in the objective function.
result Prevents posterior collapse, resulting in latent representations with more information of the observations.
Improved robust regression with clean covariates achieves better rates than Huber's model.
problem Robust regression under adaptive contamination of responses with clean covariates.
method Exploiting clean covariates to construct an estimator achieving better rates than Huber's model.
result Improved estimation rate even with constant contamination, achieving consistency.
VEC-SBM detects communities using side information like texts and images.
problem Community detection in social networks with side information.
method Proposes a novel algorithm based on iterative refinement techniques.
result Optimally recovers latent communities with side information.
Optimizes SGLD noise structure for better generalization bounds.
problem Improving generalization bounds for large models trained with SGLD.
method Manipulates the noise structure in SGLD to optimize information-theoretical bounds.
result Optimal noise covariance is the square root of the expected gradient covariance under certain constraints.
Fast covariance calculation is required both for SLAM (e.g.~in order to solve data association) and for evaluating the information-theoretic term for different candidate actions in belief space planning (BSP). In this paper we make two primary contributions. First, we develop a novel general-purpose incremental covaria…
New approach for semi-supervised learning under covariate shifts.
problem Semi-supervised learning under covariate shifts where labeled and unlabeled data distributions differ.
method Information-theoretical approach, addressing covariate shifts.
result Improved performance compared to previous methods.
A new model DKMPP integrates covariates and uses an integration-free method for spatio-temporal point processes.
problem Training intractable deep spatio-temporal point processes with multimodal covariates.
method DKMPP uses a deep kernel to model complex relationships and an integration-free score matching method.
result DKMPP and score-based estimators outperform baseline models in spatio-temporal point processes.
Bayesian PROCOVA uses AI to adjust for covariates in RCTs.
problem Unbiased and precise treatment effect inferences from RCTs.
method Generative AI constructs digital twins for covariate adjustment, using an additive mixture prior.
result Efficiency gains in smaller RCTs compared to frequentist methods.
Two spectral algorithms for community detection in graphs with covariates are compared.
problem Detecting community structure in graphs with covariates.
method Two model-based spectral algorithms are presented and compared.
result The second algorithm often better estimates block assignments by accounting for vertex covariates.
Alpha-based performance evaluation may fail to capture correlated residuals due to model errors. This paper proposes using the Generalized Information Ratio (GIR) to measure performance under misspecified benchmarks. Motivated by the theoretical link between abnormal returns and residual covariance matrix, GIR is deriv…
Proposes a method to recover sparse tensors with covariate info.
problem Sparse tensor with high missing entries and many zeros.
method Covariate-assisted Sparse Tensor Completion (COSTCO) using latent components.
result 23% accuracy improvement over baseline in advertisement dataset.
FIRE method improves model performance in federated learning by penalizing fragmentation-induced covariate shifts.
problem Performance degradation in federated learning due to data fragmentation and covariate shift.
method FIRE method accumulates fragmentation-induced covariate shift divergences via approximate Fisher information and uses it as a per-fragment loss penalty.
result FIRE outperforms importance weighting and federated learning benchmarks by up to 5.3% on shifted validation sets.
We provide the first information theoretic tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities. Our work bridges recent theoretical breakthroughs in the detection of latent community structure without no…
CONCERT improves transfer learning by borrowing partial information from auxiliary datasets.
problem Inefficiency of global similarity measures in transfer learning for high-dimensional data.
method Conditional spike-and-slab prior with covariate-specific priors for robust partial information transfer.
result CONCERT achieves variable selection and information transfer simultaneously, improving performance on the target.
Clustering, like covariate selection for classification, is an important step to compress and interpret the data. However, clustering of covariates is often performed independently of the classification step, which can lead to undesirable clustering results that harm interpretability and compression rate. Therefore, we…
We study the problem of community detection when there is covariate information about the node labels and one observes multiple correlated networks. We provide an asymptotic upper bound on the per-node mutual information as well as a heuristic analysis of a multivariate performance measure called the MMSE matrix. These…
Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding. The task is often complicated by the fact that we may have a systematic missingness in our data at test time. Our approach uses the information bottleneck to perform a low-dimension…
The paper improves ranking by integrating covariates and sparse intrinsic scores.
problem Ranking items with incomplete preference scores explained by covariates.
method Extends BTL model with covariate information and sparse intrinsic scores, using penalized MLE.
result Developed debiased estimator for penalized MLE with distributional properties.
The paper explores tail diversification in financial markets using entropy and mutual information.
problem Tail diversification in financial time series.
method Statistical independence through differential entropy and mutual information, using moments as contrast functions.
result Tail covariance matrix is a key driver of tail diversification.
Steerable E(3) Graph Neural Networks incorporate geometric and physical covariant information.
problem Incorporating covariant information like position, force, velocity, or spin in graph neural networks.
method Steerable E(3) Equivariant Graph Neural Networks (SEGNNs) that use steerable MLPs to incorporate geometric and physical covariant information.
result SEGNNs improve upon classic linear point convolutions and recent equivariant graph networks that send invariant messages.
We study the value of information in sequential compressed sensing by characterizing the performance of sequential information guided sensing in practical scenarios when information is inaccurate. In particular, we assume the signal distribution is parameterized through Gaussian or Gaussian mixtures with estimated mean…
The paper refines classical covariance asymptotics using geometric information geometry.
problem Deviation of finite-sample behavior from classical predictions in curved models.
method Develops a curvature-aware refinement by viewing parametric families as Riemannian manifolds with Fisher-Rao metric.
result Derives an \(n^{-2}\) correction to the leading \(n^{-1}I(θ)^{-1}\) covariance term for score-root estimators.
Paper explores geometry of covariance matrices using associated bundles.
problem Geometry of fixed-rank covariance matrices.
method Associated bundle approach to Bures--Wasserstein geometry.
result Established a one-to-one correspondence between geodesics.
CSTs improve stability in covariance spectrum analysis without training.
problem Stability and expressiveness in covariance spectrum analysis.
method Sequential application of covariance wavelet filters to input data.
result Stable and expressive hierarchical representations in low-data settings.
Paper introduces a novel measure to analyze excess error in classification under covariate shift.
problem Analyzing excess error in classification under covariate shift.
method Utilizes vicinity information to characterize excess error.
result Faster or competitive convergence rates compared to previous techniques.
Paper tackles CATE estimation with missing treatment info.
problem Challenges in estimating CATE with missing treatment information.
method Developed MTRNet, a novel CATE estimation algorithm using domain adaptation.
result Improves CATE estimation over state-of-the-art methods.
Chronos-2 forecasts multivariate and covariate data without task-specific training.
problem Limited applicability of existing time series forecasting models to real-world multivariate and covariate data.
method Chronos-2 uses a group attention mechanism for in-context learning across multiple time series.
result Chronos-2 achieves state-of-the-art performance across comprehensive benchmarks.
New methods rank players using covariates and comparisons, outperforming existing algorithms.
problem Ranking players based on incomplete and noisy pairwise comparisons.
method Three spectral ranking methods incorporating player covariates.
result Proposed methods outperform existing algorithms in simulations.
New method needed for class prior estimation when covariates are reduced.
problem Class prior estimation fails under covariate shift when covariates are reduced.
method Propose a probing algorithm for class prior estimation.
result Provable transformations preserving covariate shift are necessary for class prior estimation.
We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information. The goal of the analyst is to optimally estimate the true effect underlying each experiment. Both the noisy experimental results and the auxiliary covariates are useful for this purpose, but neither data …
Paper proposes CARE model for ranking with covariates, improving MLE accuracy.
problem Statistical estimation and inference for ranking with covariate information.
method Covariate-Assisted Ranking Estimation (CARE) model, extending Bradley-Terry-Luce (BTL) model.
result Derives optimal rates and asymptotic distributions for MLE of latent scores and covariates.
Deep learning extracts terrain texture covariates for geostatistical modeling.
problem Improving prediction accuracy in geostatistical modeling using terrain texture data.
method Deep learning approach to automatically derive optimal terrain texture covariates from SRTM 90m DEM.
result Deep learning-derived covariates have strong explanatory power (R-squared around 0.6) for geochemical data.
GWIB improves counterfactual regression by balancing latent distributions and reducing selection bias.
problem Selection bias between control and treatment groups negatively impacts counterfactual regression performance.
method GWIB uses Gromov-Wasserstein information bottleneck to maximize mutual information between covariates and outcomes while penalizing kernelized mutual information between latent representations and covariates.
result GWIB consistently outperforms state-of-the-art CFR methods in ITE estimation tasks.
Batch Active Learning uses derivative information for Gaussian Process regression.
problem Efficiently selecting data batches in Gaussian Process regression models.
method Proposes using the predictive covariance matrix to select data batches, exploiting full correlation.
result Demonstrates the effectiveness of incorporating derivative information across diverse applications.
NeurT-FDR controls FDR by incorporating feature hierarchy.
problem Controlling FDR in complex, large-scale hypothesis testing problems.
method NeurT-FDR uses a neural network to parametrize test-level covariates and a regression framework to adjust feature hierarchy.
result NeurT-FDR makes substantially more discoveries than competitive baselines.
New method improves experimental design under model misspecification.
problem Improving experimental design under time and budget constraints with model misspecification.
method Developed a new acquisition function (R-IDeA) that targets representativeness, informativeness, and de-amplification.
result The new method outperforms methods focusing on representativeness or informativeness alone.
The application of standard sufficient dimension reduction methods for reducing the dimension space of predictors without losing regression information requires inverting the covariance matrix of the predictors. This has posed a number of challenges especially when analyzing high-dimensional data sets in which the numb…
One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is a widely used model, for which various estimation methods have been developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all no…
A new model integrates covariates with grade of membership analysis for better latent structure recovery.
problem Improving latent structure recovery in multivariate categorical data analysis.
method Covariate-assisted grade of membership model exploiting shared low-rank simplex geometry.
result Auxiliary covariates can provably improve latent structure recovery, leading to faster convergence rates.
Novel network model estimates mixed-membership structure with covariate information.
problem Estimating latent mixed-membership structure in networks with covariate information.
method Proposes a novel network model that incorporates both community information and node covariate similarities.
result Achieves optimal estimation accuracy for similarity matrix and mixed-membership.
Proposes a method to represent high-dimensional covariates for causal inference.
problem Inefficient and unreliable causal inference with high-dimensional covariates.
method Machine-learning-assisted covariate representation approach.
result Statistical reliability and performance guarantees for proposed methods.