arXiv research

Dual-decoder model generates responses with targeted sentiment.

New method for explaining dialogue response generation models.

New method predicts nonfactuality in LLM responses using semantic isotropy.

New framework for managing medical risks using convex responses.

A new model for latent class analysis with weighted responses.

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…

Private machine learning framework using randomised response.

Active learning suffers from biased non-response, which this paper addresses.

Proposes a method for generating prediction intervals in dose-response models using conformal prediction.

A new model estimates mixed memberships for categorical data with weighted responses.

Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper, we present…

Enhances traditional MV model for socially responsible investors.

ESRLCM clusters similar responses, more broadly than traditional models.

Previous work on recommender systems mainly focus on fitting the ratings provided by users. However, the response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performanc…

The paper provides concentration bounds for embeddings of generative models.

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 establishes concentration bounds for embeddings of generative models.

The paper proposes a new method to evaluate LLM agent responses using ECDF clustering.

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

DLGNet improves dialogue response generation by leveraging transformer architecture.

New geometric theory explains nonuniform origami responses.

Improved accuracy in dynamic response variation analysis using multi-fidelity data fusion.

DFNNs predict non-Euclidean responses from Euclidean predictors.

Study analyzes non-Markovian effects in financial markets over multiple years.

BART is extended to handle various response variables.

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…

A central challenge in neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such as synaptic transmission and spiking dynamics present a significant obstacle to the creation of…

Framework predicts nonlinear system responses using GFDT and generative models.

We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by applications where response varies by individuals and therefore, estimating resp…

Paper proposes using generalized lambda distributions for stochastic simulators.

Automates U.S. visa petition document classification and RFE response generation.

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…

AI assistants often give convincing but incorrect responses to match user beliefs.

New method calculates sensitivity of system failure probability.

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

The paper enhances preference learning by incorporating response time data.

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…

We consider interactive learning and covering problems, in a setting where actions may incur different costs, depending on the response to the action. We propose a natural greedy algorithm for response-dependent costs. We bound the approximation factor of this greedy algorithm in active learning settings as well as in …

Multivariate regression model is a natural generalization of the classical univari- ate regression model for fitting multiple responses. In this paper, we propose a high- dimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accoun…

We consider the problem of estimating a sparse multi-response regression function, with an application to expression quantitative trait locus (eQTL) mapping, where the goal is to discover genetic variations that influence gene-expression levels. In particular, we investigate a shrinkage technique capable of capturing a…

Study of SK-N-AS cells' response to methamidophos using transcriptomics.

Paper estimates AI hallucinations in conditional generation tasks.

Novel framework predicts cell responses to perturbations using GRNs.

This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.

We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction. Our models, Perturbation Variational Autoencoder and its semi-supervised extension, Drug Response Variational Autoencoder (Dr.VAE), learn latent representation of the underlying gene states befor…

Study develops efficient algorithm for probabilistic penetration response of composite plates.

We consider learning high-dimensional multi-response linear models with structured parameters. By exploiting the noise correlations among responses, we propose an alternating estimation (AltEst) procedure to estimate the model parameters based on the generalized Dantzig selector. Under suitable sample size and resampli…

Study evaluates different price response definitions for NASDAQ stocks.