Proposes a method for interpreting time-varying causal effect moderation in high-dimensional data.
problem Interpreting causal effect moderation in high-dimensional data with interpretability and avoiding false positives.
method Two-step method: 1) Selects a smaller model for linear causal effect moderation using Gaussian randomization, 2) Conditions on selection to construct a pivot for uniformly asymptotic semi-parametric inference.
result Consistently achieves valid coverage rates and shorter, bounded intervals in time-varying causal effect moderation.
Enhances content moderation with culturally-aware models.
problem Global content moderation policies miss local cultural nuances.
method Fine-tuning encoder-decoder models on media-diet data.
result Improved accuracy in local violation detection and cultural alignment.
New lower bounds show challenges in clustering in moderate dimensions.
problem Clustering points from mixtures of isotropic Gaussians in moderate dimensions.
method Established low-degree polynomial lower bounds and developed a novel non-spectral algorithm.
result New lower bounds reveal a 'non-parametric rate' in moderate dimensions.
Optimizes variance reduction in Heston model using large and moderate deviations.
problem Improving variance reduction in stochastic volatility models.
method Large and moderate deviations theory applied to Heston model.
result Derives closed-form solutions for optimal change of measure.
Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the wei…
We extend previous large deviations results for the randomised Heston model to the case of moderate deviations. The proofs involve the Gärtner-Ellis theorem and sharp large deviations tools.
Optimal learning via moderate deviations theory improves statistical accuracy.
problem Statistical estimation of expected loss in various models.
method Develops confidence intervals using moderate deviation principle.
result Proposed confidence intervals are statistically optimal.
Computes invariants distinguishing between immersions and embeddings of doodles and blobs on surfaces.
problem Distinguishing between immersions and embeddings of doodles and blobs on surfaces.
method Regular embeddings, bordisms, and exact sequences of abelian groups.
result Exact sequence describing bordisms of immersions and embeddings of doodles on A=RimesI. New method interprets deep learning for causal effects, separating prognostic and moderating covariates.
problem Estimating individual causal/treatment effects under confounders.
method Deep counterfactual learning architecture for estimating CATE with interpretable score functions.
result Demonstrated improved interpretability and quantification of uncertainty in CATE estimation.
We provide a unifying treatment of pathwise moderate deviations for models commonly used in financial applications, and for related integrated functionals. Suitable scaling allows us to transfer these results into small-time, large-time and tail asymptotics for diffusions, as well as for option prices and realised vari…
Unified approach to stochastic Volterra systems' deviations.
problem Large and moderate deviations for stochastic Volterra systems.
method Weak convergence approach by Budhijara, Dupuis and Ellis.
result Unified treatment of deviations for a broad class of stochastic Volterra equations.
Stochastic Gradient Descent shows directional bias with moderate learning rates, impacting optimization outcomes.
problem Understanding the bias of SGD with moderate learning rates in practical scenarios.
method Analyzing SGD and GD on an overparameterized linear regression problem.
result SGD converges along large eigenvalue directions, GD along small ones, affecting early stopping outcomes.
We consider call option prices in diffusion models close to expiry, in an asymptotic regime ("moderately out of the money") that interpolates between the well-studied cases of at-the-money options and out-of-the-money fixed-strike options. First and higher order small-time moderate deviation estimates of call prices an…
A new graph-based clustering method for moderate-dimensional data.
problem Performance degradation of existing graph-based clustering methods in high dimensions.
method Introduces UN-CCDs using NND-based MC-SRT for covering radii determination.
result UN-CCDs provide stable and competitive performance in moderate-sized datasets.
The paper analyzes the dynamics of tokens in transformer models at moderate interaction levels.
problem Understanding the evolution of tokens in transformer models at moderate interaction levels.
method Modeling transformer models as a system of particles interacting in a mean-field way and studying the corresponding dynamics.
result Characterization and convergence of the limiting dynamics in different phases of the system.
Paper optimizes change-point detection using learned distributions from training sequences.
problem Optimal change-point detection with unknown pre- and post-change distributions.
method Designs a change-point estimator using training sequences and test sequences.
result Optimal confidence width characterized as a function of undetected error.
Q-learning with cSMART data assesses cAI tailoring variables.
problem Evaluating moderators in cAI construction.
method Clustered Q-learning with M-out-of-N Cluster Bootstrap.
result Constructs confidence intervals for causal effect moderation.
A framework for optimizing prompt selection in generative language models.
problem Efficiently selecting prompts for generative language models.
method Two-stage framework using simulation optimization to maximize a pre-defined score.
result Consistency of the sequential evaluation procedure in the proposed framework.
Cluster analysis of very high dimensional data can benefit from the properties of such high dimensionality. Informally expressed, in this work, our focus is on the analogous situation when the dimensionality is moderate to small, relative to a massively sized set of observations. Mathematically expressed, these are the…
A framework uses proxies to prioritize treatment without estimating causal effects.
problem Prioritizing treatment when causal effects are hard to estimate.
method Decision-focused framework identifying conditions for proxy usefulness.
result Proxies can recover correct effect ordering under specific conditions.
We discuss a general dynamic replication approach to counterparty credit risk modeling. This leads to a fundamental jump-process backward stochastic differential equation (BSDE) for the credit risk adjusted portfolio value. We then reduce the fundamental BSDE to a continuous BSDE. Depending on the close out value conve…
Houdini finds high-dimensional saddle points under few constraints.
problem Escaping from saddle points in high-dimensional spaces with constraints.
method Gradient descent methods under logarithmic inequality constraints.
result Polynomial time algorithms for escaping saddle points under constraints.
Study describes frequencies of geodesics on hyperbolic surfaces as genus grows.
problem Large genus asymptotic behaviors of geodesic frequencies on hyperbolic surfaces.
method Proof of conjecture involving separating and nonseparating geodesics.
result Explicit function $f( rac{n}{g})$ for frequency ratio given.
The reproducing kernel Hilbert space (RKHS) embedding of distributions offers a general and flexible framework for testing problems in arbitrary domains and has attracted considerable amount of attention in recent years. To gain insights into their operating characteristics, we study here the statistical performance of…
The Lax-Hopf formula simplifies the value function of an intertemporal optimization (infinite dimensional) problem associated with a convex transaction-cost function which depends only on the transactions (velocities) of a commodity evolution: it states that the value function is equal to the marginal fonction of a fin…
Gradient descent learns ReLU functions with non-zero bias efficiently.
problem Learning ReLU functions with non-zero bias under Gaussian distributions.
method Gradient descent starting from random initialization.
result Gradient descent achieves near-optimal error with high probability.
In this paper, we study the price responsiveness of electricity consumption from empirical commercial and industrial load data obtained from Texas. Employing a dynamical system perspective, we show that price responsive demand can be modeled as a hybrid of a Hammerstein model with delay following a price surge, and a l…
The paper extends a link criterion for Lipschitz normal embeddings to definable sets in o-minimal structures.
problem Characterizing Lipschitz normal embeddings of definable sets.
method Extending a known result about subanalytic germs to definable germs in any o-minimal structure.
result The link criterion holds for definable germs in o-minimal structures, but is not sufficient for all homomorphisms.
We consider rough stochastic volatility models where the driving noise of volatility has fractional scaling, in the "rough" regime of Hurst parameter H<1/2. This regime recently attracted a lot of attention both from the statistical and option pricing point of view. With focus on the latter, we sharpen the large de…
Machine learning models detect COVID-19 from routine blood tests.
problem Separating COVID-19 from other viral pneumonias using blood tests.
method Employed random forests and support vector machines on blood data.
result SVM-based classifier achieves 84% accuracy in detecting COVID-19.
Community moderation drifts towards majority, study finds.
problem How to ensure crowd-sourced moderation systems trust and reward accurate evaluations.
method Consensus-based auditing with a two-stage algorithm that weights contributors by the stability of their past residuals.
result Minority contributors' evaluations drift towards the majority, and their participation share falls on controversial topics.
Improves accuracy and fairness in prediction systems with multiple domain experts.
problem Designing unbiased and accurate deferral systems with multiple experts.
method Proposes a framework for learning a classifier and deferral system that chooses to defer to multiple human experts.
result Significantly improves accuracy and fairness of final predictions compared to baselines.
Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction …
Research shows that information asymmetry affects how quickly companies adjust their capital structure and expected returns.
problem The relationship between capital structure adjustment speed and expected returns is influenced by information asymmetry.
method A hybrid data regression model was used to test the hypotheses based on data from 120 companies in the Tehran Stock Exchange.
result Information asymmetry positively affects the relationship between capital structure adjustment speed and expected returns.
The study analyzes how large language models form and express investor risk profiles.
problem Understanding how large language models (LLMs) form and express investor risk profiles.
method Examined three LLMs (GPT, Gemini, and Llama) and assessed their responses to a standardized risk questionnaire under varying prompts.
result LLMs generally form long-term investment profiles, but they exhibit different risk tolerance levels.
SGD benefits from a directional bias in kernel regression models.
problem Improving generalization in kernel regression models.
method Generalized directional bias property of SGD in kernel regression.
result SGD converges along the eigenvector of the largest eigenvalue of the Gram matrix.
We study Hölder continuity of solutions to the Monge-Ampère equations on compact Kähler manifolds. In [DNS] the authors have shown that the measure ωun is moderate if u is Hölder continuous. We prove a theorem which is a partial converse to this result.
Study measures investment funds' climate transition risk, finds moderate losses.
problem Measuring the impact of climate transition on investment portfolios.
method Comprehensive framework using geographical, sectoral, company and ISIN-level data.
result Investment funds suffer a moderate 5.7% loss in high transition risk scenario.
Algorithm identifies fractal system's scaling exponents in high dimensions.
problem Statistical identification of Hurst distribution in high-dimensional fractal systems.
method Wavelet random matrices, modified spectral clustering, model selection.
result Algorithm consistently estimates Hurst distribution in moderately high dimensions.
Simple curves enclose two small disks if they're wide and bend moderately.
problem Bounding the diameter of a curve to enclose two disjoint unit disks.
method Analyzing curvature and diameter constraints of a simple closed curve.
result A curve with curvature ≤1 and diameter ≥4 encloses two disjoint open unit disks.
We consider estimating a piecewise-constant image, or a gradient-sparse signal on a general graph, from noisy linear measurements. We propose and study an iterative algorithm to minimize a penalized least-squares objective, with a penalty given by the "l_0-norm" of the signal's discrete graph gradient. The method proce…
For spherically symmetric distributions, efficient quantisation can be achieved with moderate sample sizes.
problem Optimal quantisation in high dimensions requires large sample sizes, making it impractical.
method Uniformly distributed random quantisers on a sphere of suitable radius achieve exceptional performance.
result For moderate sample sizes, quantisation error can be efficiently computed and approximated.
The paper models financial markets and real economy interactions using a large agent framework.
problem Understanding capital allocation and accumulation in financial markets and real economy interactions.
method Developed a field-formalism model to analyze interactions between financial markets and real economy with a large number of heterogeneous agents.
result The number of firms in each sector depends on the aggregate financial capital invested and expected long-term returns.
Study develops smart contract framework for procurement under demand variability.
problem Operational and economic implications of smart contract adoption under moderate uncertainty.
method Multi-supplier model with endogenized adoption costs, supplier readiness, and inventory penalties; analytical and numerical results.
result Partial adoption strategies support moderate demand variability, while excessive digital investment reduces profitability.
Method for understanding heterogeneous treatment effects in complex causal graphs.
problem Heterogeneity and comorbidity in healthcare problems.
method Developed a new approach to characterize heterogeneous causal effects (HCEs) in graphical contexts, including heterogeneous causal graphs (HCGs) with confounders and mediators.
result Established theoretical forms and properties of HCEs in linear and nonlinear models, and developed interactive structural learning for estimation.
PQMass assesses generative model quality using chi-squared tests.
problem Assessing the quality of generative models without density assumptions.
method Divides sample space into regions, applies chi-squared tests to p-values.
result Effectively assesses generative model quality, novelty, and diversity.
Risk estimation is at the core of many learning systems. The importance of this problem has motivated researchers to propose different schemes, such as cross validation, generalized cross validation, and Bootstrap. The theoretical properties of such estimates have been extensively studied in the low-dimensional setting…
We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features.We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions.We present four cas…