A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Steerable E(3) Graph Neural Networks incorporate geometric and physical covariant information.
A new model DKMPP integrates covariates and uses an integration-free method for spatio-temporal point processes.
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…
Paper proposes a new method for SP with covariates using PADR and ERM.
New method prevents posterior collapse in iVAE models.
Proposes a method to recover sparse tensors with covariate info.
Flexible framework integrates machine learning and DRO for uncertain parameter prediction.
New criterion improves predictive evaluation in weighted inference scenarios.
Semi-supervised method boosts two-sample testing with covariate data.
The paper improves ranking by integrating covariates and sparse intrinsic scores.
Paper proposes CARE model for ranking with covariates, improving MLE accuracy.
Contextual information helps identify the best arm more efficiently.
We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas. The use of collective dynamics eliminates multiplicative noise and stabilizes the dynamics thus providing a practical approach to difficult anisotr…
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…
Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.
Study integrates machine learning with SAA for optimizing decisions based on uncertain parameters and covariates.
Paper uses machine learning in EM framework for better nowcasting.
Chronos-2 forecasts multivariate and covariate data without task-specific training.
The paper infers multiple graphs from stationary signals on them.
Novel network model estimates mixed-membership structure with covariate information.
New methods rank players using covariates and comparisons, outperforming existing algorithms.
Classification is an important tool with many useful applications. Among the many classification methods, Fisher's Linear Discriminant Analysis (LDA) is a traditional model-based approach which makes use of the covariance information. However, in the high-dimensional, low-sample size setting, LDA cannot be directly dep…
We extend multi-way, multivariate ANOVA-type analysis to cases where one covariate is the view, with features of each view coming from different, high-dimensional domains. The different views are assumed to be connected by having paired samples; this is a common setup in recent bioinformatics experiments, of which we a…
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 …
Develops model-free methods for event history analysis and efficient covariate adjustment.
The literature provides strong evidence that stock prices can be predicted from past price data. Principal component analysis (PCA) is a widely used mathematical technique for dimensionality reduction and analysis of data by identifying a small number of principal components to explain the variation found in a data set…
We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information, and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings …
Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technology advance has made it possible to collect a huge amount of covariate information such as microarray, proteomic and SNP data via bioimaging technology while ob…
In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for regularization, we develop an approach re-fitting the results of standard methods towards the input d…
Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective descent directions. For models with many parameters, the covariance matrix they are based on becomes gigantic, making them inapplicable in thei…
A new method for efficient portfolio optimization using graph structures.
Proposes a method to represent high-dimensional covariates for causal inference.
AUASE embeds dynamic networks with stability guarantees for node comparison.
Deep neural networks enjoy a powerful representation and have proven effective in a number of applications. However, recent advances show that deep neural networks are vulnerable to adversarial attacks incurred by the so-called adversarial examples. Although the adversarial example is only slightly different from the i…
Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics. Expert knowledge elicitation can help, and a strong line of work focuses on directly eliciting informative prior distributions for parameters. This either requires considerable statistical expertise or is l…
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been recently shown to be effective. However, previous works either assume that intra-class variances can always be generalized to new classes, or e…
Method detects errors in numerical data using regression models.
DeepHazard uses neural networks to predict time-varying survival risks.
The paper develops exact and approximate conformal inference methods for multi-output regression.
Proposes L-VAE for longitudinal data analysis.
Develops intrinsic Gaussian process regression for manifold-valued data.
Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporat…
A classical problem in causal inference is that of matching, where treatment units need to be matched to control units based on covariate information. In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets. This method, called FLAME (Fast Large-scale …
Timer-XL predicts multidimensional time series using a unified Transformer approach.
Method estimates CATE using RCT data to handle hidden confounders.
New linear algorithms improve wSVMs for multiclass probability estimation.
This work develops a model to distinguish network and covariate information.
In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes. This distribution is discussed in the framework of the exponential family, and its statistical properties regarding independence of the nodes are demonstrated. Importantly the model can e…