arXiv research

DFNNs predict non-Euclidean responses from Euclidean predictors.

New algorithms for regression with adversarial responses on various metric spaces.

We propose and analyze sequential design methods for the problem of ranking several response surfaces. Namely, given $L \ge 2$ response surfaces over a continuous input space $\cal X$, the aim is to efficiently find the index of the minimal response across the entire $\cal X$. The response surfaces are not known and ha…

The paper provides risk bounds for learning many response functions using linear regression.

The paper establishes concentration bounds for embeddings of generative models.

Improves regression efficiency by separating material and immaterial parts of responses.

Interactive learning with hindsight instruction feedback achieves better performance than traditional methods.

The paper provides concentration bounds for embeddings of generative models.

Proposes estimators for complex dose-response curves using kernel methods.

A new method for unfolding histograms without matrix inversion.

Motivation: Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions, and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms…

The paper enhances preference learning by incorporating response time data.

New mathematical foundations for stable RKHSs improve system identification.

Deep learning model approximates stochastic responses.

Aims to optimize complex multivariate systems with constraints.

New method models covariates and responses without parametric assumptions using manifold learning.

We propose an adversarial learning approach for generating multi-turn dialogue responses. Our proposed framework, hredGAN, is based on conditional generative adversarial networks (GANs). The GAN's generator is a modified hierarchical recurrent encoder-decoder network (HRED) and the discriminator is a word-level bidirec…

New geometric theory explains nonuniform origami responses.

Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the human body has a number of physiological response patterns to hemorrhage which escalate as blood loss continues, however the exact etiology a…

The study optimizes sampling in complex systems with probabilistic response distributions.

The study uses response theory to understand RNNs processing input signals.

A new test validates ensemble models against the null hypothesis.

A new model for complex cells accounts for insensitivity to image shifts.

Deployment-complete benchmarking assesses if evidence leads to consistent deployment actions.

There are non-vanishing price responses across different stocks in correlated financial markets. We further study this issue by performing different averages, which identify active and passive cross-responses. The two average cross-responses show different characteristic dependences on the time lag. The passive cross-r…

DFR models dynamic distributional data with weighted Fréchet means.

Novel framework for uncertainty quantification in metric spaces.

Automated scoring engines are increasingly being used to score the free-form text responses that students give to questions. Such engines are not designed to appropriately deal with responses that a human reader would find alarming such as those that indicate an intention to self-harm or harm others, responses that all…

Neural network predicts functional responses from scalar inputs.

An increasing array of biomedical and computer vision applications requires the predictive modeling of complex data, for example images and shapes. The main challenge when predicting such objects lies in the fact that they do not comply to the assumptions of Euclidean geometry. Rather, they occupy non-linear spaces, a.…

Previous studies of the stock price response to individual trades focused on single stocks. We empirically investigate the price response of one stock to the trades of other stocks. How large is the impact of one stock on others and vice versa? -- This impact of trades on the price change across stocks appears to be tr…

Human decision-makers often receive assistance from data-driven algorithmic systems that provide a score for evaluating objects, including individuals. The scores are generated by a function (mechanism) that takes a set of features as input and generates a score.The scoring functions are either machine-learned or human…

New kernel methods estimate complex causal relationships.

Neural operators approximate Stackelberg game solutions.

The paper predicts responses on out-of-sample nodes using latent positions on unknown curves.

Proves existence of optimal shallow neural networks with ReLU activation.

Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world --- contrast and luminance for vision, pitch and intensity for sound --- and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis o…

This paper presents Sparse Partitioning, a Bayesian method for identifying predictors that either individually or in combination with others affect a response variable. The method is designed for regression problems involving binary or tertiary predictors and allows the number of predictors to exceed the size of the sa…

Paper introduces SDM for detecting LLM hallucinations, improving on entropy tests.

Study evaluates different price response definitions for NASDAQ stocks.

New algorithms achieve decision calibration without sample complexity dependent on feature dimension.

Study optimizes CANN for actuarial tasks using RSM.

Deep RL improves Diplomacy performance, outperforming previous methods.

Enhances Gaussian process models for handling variable error variances and multiple responses.

In this paper, we propose deep learning algorithms for ranking response surfaces, with applications to optimal stopping problems in financial mathematics. The problem of ranking response surfaces is motivated by estimating optimal feedback policy maps in stochastic control problems, aiming to efficiently find the index…

Interpretable text-response modelling for structured outcomes

Study explores kinematics of surfaces under metric restrictions.

New method for reducing dimensions of distributional data.