The paper quantifies risk in stochastic systems under uncertain inputs.
problem Risk quantification of mean response under input uncertainty in stochastic systems.
method Nested Monte Carlo simulation approach to estimate risk measures.
result Asymptotical properties of risk estimators, including consistency and asymptotic normality.
Enhances traditional MV model for socially responsible investors.
problem Traditional MV models ignore ESG scores relevant to socially responsible investors.
method Implemented an amended MV model considering ESG scores.
result SR investors can achieve competitive SR portfolios with a trade-off between Sharpe Ratio and ESG scores.
DFNNs predict non-Euclidean responses from Euclidean predictors.
problem Regression with non-Euclidean responses.
method Deep Fréchet neural networks (DFNNs) approximating conditional Fréchet means.
result DFNNs consistently outperform existing methods in empirical studies.
LRVB improves MFVB's uncertainty estimates and covariance.
problem Underestimates uncertainty and lack of covariance information in MFVB.
method Generalizes linear response methods from statistical physics.
result Accurate uncertainty estimates for model variables, including covariance.
Study of portfolio management under relative performance concerns using mean field games.
problem Portfolio management problems under relative performance concerns.
method Forward utilities of CARA type, mean field games, best response and equilibrium strategies.
result Solve forward-utility finite player game and mean-field game under asset specialization.
A new method improves SSVEP BCI to recognize responses in sub-second time with high accuracy.
problem Achieving high accuracy in SSVEP BCI with short response times.
method CSTA (Combining Spatial-Filtering and Temporal Alignment) method.
result CSTA achieves maximum mean accuracy of 97.43% in sub-second response time.
Net2Vec maps filters to vectors to reveal complex concept encoding.
problem Understanding how deep neural networks encode semantic concepts.
method Net2Vec framework that maps semantic concepts to vectorial embeddings based on filter responses.
result Multiple filters are often required to code for a concept, and filters help encode multiple concepts.
Bayesian model captures mean and variance of response variables.
problem Complex, predictor-dependent relationships and heteroscedastic patterns in data.
method Sum-of-tessellations for mean, product-of-tessellations for variance.
result Model captures nuanced variance structures and provides reliable predictive uncertainty.
New algorithms for regression with adversarial responses on various metric spaces.
problem Regression with adversarial responses under non-i.i.d. sequences.
method Proves universal consistency for a wide range of non-stationary processes.
result Achieves universal consistency for a broader class of sequences than stationary processes.
New methods for private statistical inference under local differential privacy.
problem Private statistical inference for population means with bounded observations.
method Nonparametric, nonasymptotic statistical inference using a generalized randomized response mechanism.
result Private confidence intervals and sequences for population means under LDP constraints.
Deep learning model approximates stochastic responses.
problem Approximating stochastic responses using neural networks.
method Generative neural network with conditional maximum mean discrepancy (CMMD) loss.
result Excellent performance on benchmark problems.
Recent studies have revealed a number of striking dependence patterns in high frequency stock price dynamics characterizing probabilistic interrelation between two consequent price increments x (push) and y (response) as described by the bivariate probability distribution P(x,y) [1,2,3,4]. There are two properties, the…
New framework for regression trees with multivariate response and dynamic mean vectors.
problem Characterizing and implementing regression trees for multivariate responses.
method High dimensional model with dynamic mean vectors over multi-dimensional change axes.
result Optimal rate of convergence and asymptotic valid confidence intervals for change points.
New random forest criteria improve splitting for non-location structured data.
problem Improving random forest splitting for non-location structured data.
method Implement and compare various distributional splitting criteria inside a single honest-forest implementation.
result Distributional splitting criteria, especially sliced-Wasserstein, improve performance on multivariate responses.
The paper uses deep neural networks to estimate and infer ATE without needing to know the dimension of the data.
problem Estimating and inferring the average treatment effect (ATE) in complex data settings.
method The paper uses deep neural networks to estimate the mean regression function and then calculates the ATE. It establishes consistency and asymptotic normality of the estimators.
result The deep neural network estimates of ATE are consistent and asymptotically normal, providing dimension-free rates.
The study examines statistical properties of market price and liquidity responses.
problem Understanding the statistical properties of market price and liquidity responses.
method Utilized singular value decomposition to analyze interconnections and statistical characteristics of responses.
result Traded volumes play a critical role in price changes induced by liquidity changes.
New algorithm for solving minimax problems over distributions converges to Nash equilibrium.
problem Solving minimax problems over probability distributions.
method Symmetric Mean-field Langevin Dynamics (MFL-AG and MFL-ABR) with weighted averaging and best response dynamics.
result Converges to mixed Nash equilibrium with average-iterate and last-iterate convergence.
Mean Field Variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is its (sometimes severe) underestimates of the uncertainty of model variables and lack of information about model variable covariance.…
Study robust linear regression without distributional assumptions for heavy-tailed responses.
problem Linear regression with heavy-tailed responses and no distributional assumptions.
method Combining truncated least squares, median-of-means, and aggregation theory to construct a non-linear estimator.
result Achieves excess risk of order d/n with optimal sub-exponential tail. Predicts individual septic shock children's vasoactive response using RNN.
problem Personalized physiologic responses to vasoactive titrations in septic shock children.
method Retrospective analysis of EMR data using a Recurrent Neural Network (RNN).
result RNN model predicted physiologic responses more accurately than a linear model.
New method tests CMI using deep neural networks for high-dimensional data.
problem Testing conditional mean independence in high-dimensional settings.
method Population CMI measure and bootstrap-based testing with deep generative neural networks.
result Strong empirical performance and versatility in various scenarios.
Boosting trees can test necessary conditions for regression model calibration.
problem Testing calibration and auto-calibration in regression models.
method Using boosting trees to test calibration and auto-calibration.
result Boosting trees prove to be very powerful in testing calibration and auto-calibration in large insurance datasets.
We bridge statistical and worst-case approaches to experimental design for linear regression.
problem Designing efficient experiments for linear regression models with arbitrary responses.
method Propose a new experimental design framework for arbitrary response distributions, combining statistical and worst-case approaches.
result Develop efficient randomized design procedures achieving strong variance bounds for unbiased estimators using few responses.
kNNSampler imputes missing values from their distributions using kNN.
problem Impute missing values from their distributions.
method Randomly samples from the observed responses of the k most similar units.
result Estimates the conditional distribution of missing values.
New method for estimating and testing impulse responses in high-dimensional VAR systems.
problem Statistical inference for impulse responses in sparse, high-dimensional vector autoregressions.
method Local projection equations and de-sparsified estimators combined with a non-regularized contemporaneous impact matrix.
result Valid inference procedures for structural impulse responses in high-dimensional systems.
FEA-Net uses physics knowledge to predict material responses efficiently.
problem Predicting material mechanical responses accurately and efficiently.
method Physics-guided deep learning with FEA integration.
result FEA-Net accurately predicts mechanical responses under external loading.
Estimates personalized treatment response curves using covariates.
problem Flexible estimation of personalized treatment response curves.
method Sieve based nonparametric estimator of smoothed regimen-response curve function.
result Asymptotic linearity and undersmoothing criteria for efficient estimation.
Paper introduces a new estimator for Rasch model with exact error analysis.
problem Estimating parameters of the Rasch model with performance guarantees.
method Develops a novel L-MMSE estimator for the Rasch model with nonasymptotic analysis.
result The L-MMSE estimator provides exact error analysis and performs similarly to state-of-the-art estimators.
Flexible modeling of continuous treatment-response curves for observational data.
problem Estimating treatment effects with varying continuous-time, continuous-valued interventions and irregularly measured outcomes.
method Representing treatment response curves with linear time-invariant dynamical systems and multiple-output Gaussian Processes.
result Significant gains in accuracy over state-of-the-art models on simulated and clinical datasets.
Cost-efficient distributed learning via combinatorial bandits.
problem Trade-off between error and runtime in distributed SGD.
method Combinatorial multi-armed bandit model to learn fastest workers.
result Significantly lower errors with same computational efforts and less communication.
A hybrid method combines model-based and data-driven approaches for multiscale constitutive responses.
problem High computational costs and inaccuracies in nonlinear multiscale methods.
method Hybrid methodology combining model-based constitutive laws, data-driven corrections, and computational multiscale approaches.
result Model-data-driven approach improves macroscale simulations with similar accuracy and computational cost.
Proposes a method to classify with matrix-valued predictors using penalized likelihood.
problem Classification with matrix-valued predictors.
method Penalized likelihood method with Kronecker product decomposition for precision matrix estimation.
result Outperforms competitors in classification accuracy, even when assumptions are violated.
Improves regression efficiency by separating material and immaterial parts of responses.
problem Improving estimation efficiency in nonlinear multivariate regressions.
method Kernel envelope (KENV) estimator for nonparametric response envelopes in reproducing kernel Hilbert space.
result KENV achieves lower in-sample prediction risk than kernel ridge regression in non-trivial immaterial components.
This paper finds an MFE in large stochastic games using reinforcement learning.
problem Finding an equilibrium in large stochastic games is difficult.
method Lower-myopic best response dynamics and posterior sampling for reinforcement learning.
result Policy and action distributions converge to optimal strategies in an MFE.
Bayesian models improve robustness measurement of microcredit effectiveness.
problem Measuring the robustness of posterior expectations in hierarchical models.
method Variational Bayes and linear response methods.
result Fast, accurate posterior means and robustness measures for microcredit effectiveness.
The study optimizes sampling in complex systems with probabilistic response distributions.
problem Calibrating and optimizing complex systems with probabilistic response distributions.
method Non-parametric Bayesian approach to modeling spatial fields of probability distributions, introducing adaptive sampling strategies.
result Adaptive sampling strategies improve system evaluations by guiding focus towards key features.
ROD reconstructs conditional mean surfaces from observational data, invariant to term order.
problem Regression estimates from observational data can be biased by multicollinearity and term order.
method Retrospective Orthogonal Design (ROD) reconstructs surfaces on a probability-balanced lattice, preserving observed and completing unsupported cells.
result ROD outperformed polynomial regression across various data-generating processes, achieving high out-of-sample R2. DFR models dynamic distributional data with weighted Fréchet means.
problem Regression of distribution-valued responses over time.
method Dynamic Fréchet Regression (DFR) with index-aware weighting and feature selection.
result Improved predictive accuracy and feature recovery over existing methods.
Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is that it underestimates the uncertainty of model variables (sometimes severely) and provides no information about model variable cova…
Training models to prefer certain responses can unintentionally shift probability to harmful ones.
problem Likelihood displacement in DPO models, leading to unintended unalignment.
method Characterized and mitigated likelihood displacement using CHES score.
result Training models to prefer certain responses can unintentionally shift probability mass to harmful responses.
We investigate a class of hierarchical mixtures-of-experts (HME) models where exponential family regression models with generalized linear mean functions of the form psi(ga+fx^Tfgb) are mixed. Here psi(...) is the inverse link function. Suppose the true response y follows an exponential family regression model with mea…
Using Trades and Quotes data from the Paris stock market, we show that the random walk nature of traded prices results from a very delicate interplay between two opposite tendencies: long-range correlated market orders that lead to super-diffusion (or persistence), and mean reverting limit orders that lead to sub-diffu…
New method outperforms standard procedures in heavy-tailed problems.
problem Regression function estimation under heavy-tailed conditions.
method Regularized risk minimization procedure based on median-of-means tournaments.
result The new procedure achieves near optimal accuracy and confidence in heavy-tailed problems.
The study compares how deletions and trades affect stock prices and spread changes.
problem Understanding the impact of deletions and trades on stock prices and spread changes.
method Examined the frequencies of relative amounts of price changing events due to trades, deletions, and order placements.
result Deletions of orders open the bid-ask spread more often than trades and have a similar effect on prices as trades.
New method identifies shared topics in LLM inputs and outputs for better detection of hallucinations.
problem Detecting semantic drift in LLM responses from context.
method Transformed Deterministic Information Bottleneck (DIB) into UDIB for high-dimensional data.
result UDIB generates more informative topic representations for SDM, improving hallucination detection.
New method models covariates and responses without parametric assumptions using manifold learning.
problem Losing explanatory power for responses in standard factor models applied to covariates alone.
method Anisotropic diffusion maps for learning low-dimensional embeddings.
result Kalman filtering in diffusion-map coordinates improves joint covariate-response prediction.
Paper develops methods to estimate derivative of dose-response curve for continuous treatments.
problem Estimating the derivative of the dose-response curve for continuous treatments.
method Doubly robust (DR) inference method using kernel smoothing, bias-corrected IPW and DR estimators.
result Proposes novel bias-corrected IPW and DR estimators for continuous treatments.
Framework predicts nonlinear system responses using GFDT and generative models.
problem Predicting higher-order moments of nonlinear stochastic systems to small perturbations.
method Combining GFDT with generative modeling to estimate score function directly from data.
result Accurately captures nonlinear and non-Gaussian features of system responses.