Study evaluates different price response definitions for NASDAQ stocks.
problem Understanding the long-lasting effects of trading activity on stock prices.
method Examined two different price response implementations for NASDAQ Trades and Quotes (TAQ) data.
result Results are qualitatively the same for two different time scale definitions, but response can vary by up to a factor of two.
A new model for latent class analysis with weighted responses.
problem Limitation of latent class model for real-world data with continuous or negative responses.
method Proposed a novel generative model, the weighted latent class model (WLCM).
result The proposed WLCM is more realistic and general than the latent class model.
A new model estimates mixed memberships for categorical data with weighted responses.
problem Limited applicability of existing GoM model to weighted categorical data.
method Proposes Weighted Grade of Membership (WGoM) model, relaxing distribution constraints.
result WGoM can describe any response matrix with finite distinct elements.
Interpretable text-response modelling for structured outcomes
problem Predicting structured responses alongside textual data
method Joint non-negative matrix factorisation and binomial regression
result Recovering stable response-relevant textual signals
ESRLCM clusters similar responses, more broadly than traditional models.
problem Clustering multivariate categorical data with common response patterns.
method Bayesian Equivalence Set Restricted Latent Class Model (ESRLCM).
result ESRLCM identifies clusters with similar item response probabilities.
Study analyzes price response and spread impact in foreign exchange markets.
problem Understanding deviations from Markovian behavior in foreign exchange markets.
method Detailed large-scale data analysis of price response functions for different years and time scales, using pip bid-ask spread definition.
result Large pip spreads significantly impact price response in foreign exchange markets.
Improved accuracy in dynamic response variation analysis using multi-fidelity data fusion.
problem Inefficient characterization of dynamic response variation due to limited high-fidelity data.
method Composite Neural Network fusion approach for multi-level, heterogeneous datasets.
result Improved accuracy in frequency response variation characterization.
An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality…
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…
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.
The paper enhances preference learning by incorporating response time data.
problem Lack of temporal information in user decision-making for reward model learning.
method Integrates response time alongside binary choice data using the EZ model and Neyman-orthogonal loss functions.
result Response time-augmented approach reduces error rates from exponential to polynomial scaling, improving sample efficiency.
A simplified model for brain activity measurement.
problem Estimating the BOLD response in fMRI data.
method Proposes a lightweight model for HRF with fewer parameters.
result Reduces optimization complexity and facilitates applications.
A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially va…
The paper provides risk bounds for learning many response functions using linear regression.
problem Learning many response functions from a single dataset.
method Ordinary least squares regression in a high-dimensional feature space.
result Convergence guarantees on worst-case excess prediction risk for infinite response functions with finite VC dimension.
Active learning suffers from biased non-response, which this paper addresses.
problem Active learning's effectiveness is compromised by biased non-response in real-world contexts.
method Proposes a cost-based correction to the sampling strategy, UCB-EU, to mitigate the impact of biased non-response.
result UCB-EU successfully reduces the harm from labelling non-response in many settings.
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.
Faster, more accurate IRT model for large datasets.
problem Speed and accuracy challenges in fitting IRT models to large datasets.
method Variational Bayesian inference for IRT models.
result Higher log likelihoods and improved missing data imputation.
Item response theory (IRT) models for categorical response data are widely used in the analysis of educational data, computerized adaptive testing, and psychological surveys. However, most IRT models rely on both the assumption that categories are strictly ordered and the assumption that this ordering is known a priori…
New method handles correlated responses and interaction effects in multi-response regression.
problem Handling correlated responses and interaction effects in multi-response regression.
method MADMMplasso, an ADMM-based approach for multi-response regression with overlapping groups and interaction effects.
result The proposed method outperforms in prediction and variable selection for correlated responses and interaction effects.
Model predicts anti-cancer drug responses using gene and molecular data.
problem Expensive and time-consuming cancer drug discovery and tailoring.
method Uses variational autoencoders and multi-layer perceptrons to encode gene expression and drug data.
result High average R2 of 0.83 and 0.845 in predicting drug responses for breast and pan-cancer cell lines, respectively. PEER tackles multi-response regression with incomplete outcomes efficiently.
problem Challenges in estimating, predicting, and computing with large-scale multi-response regression and incomplete outcomes.
method PEER converts multi-response regression into parallel univariate-response regressions.
result PEER achieves consistency in estimation, prediction, and variable selection.
Unified framework for binary responses using AUC loss and low-rank constraint.
problem Statistical inefficiency and shared structure in fitting multiple binary responses.
method Pairwise AUC loss aggregation with low-rank constraint, scalable projected gradient descent.
result Unified framework outperforms likelihood-based approaches in challenging settings.
The vast majority of current machine learning algorithms are designed to predict single responses or a vector of responses, yet many types of response are more naturally organized as matrices or higher-order tensor objects where characteristics are shared across modes. We present a new machine learning algorithm BaTFLE…
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often mo…
We propose and analyze sequential design methods for the problem of ranking several response surfaces. Namely, given L≥2 response surfaces over a continuous input space X, the aim is to efficiently find the index of the minimal response across the entire X. The response surfaces are not known and ha…
POTD estimates SDR subspace using optimal transport for binary response.
problem Insufficient performance of existing SDR methods for categorical responses.
method Principal optimal transport direction (POTD) using optimal transport coupling.
result POTD exclusively estimates SDR subspace for error-free class labels.
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…
Transcriptomics response of SK-N-AS cells to methamidophos (an acetylcholine esterase inhibitor) exposure was measured at 10 time points between 0.5 and 48 h. The data was analyzed using a combination of traditional statistical methods and novel machine learning algorithms for detecting anomalous behavior and infer cau…
Study presents MMC model for better fitting multiple choice data.
problem Improving accuracy of latent trait estimates in IRT models.
method Fit autoencoders to MMC model, demonstrating better fit than nominal response model.
result MMC model outperforms traditional IRT models in fit.
A hybrid ML method improves ship response predictions across different sea conditions.
problem Improving accuracy and generalizability of ML methods for ship response predictions.
method A hybrid machine learning method that corrects forces in a low-fidelity equation of motion.
result The hybrid method offers improved prediction accuracy and generalizability compared to benchmarks.
How to generate human like response is one of the most challenging tasks for artificial intelligence. In a real application, after reading the same post different people might write responses with positive or negative sentiment according to their own experiences and attitudes. To simulate this procedure, we propose a s…
Deep Claim predicts payer responses from claims data using deep learning.
problem Predicting payer responses from claims data to improve healthcare performance.
method Learning complex dependencies in claim inputs to create a compact representation, then using deep learning to predict responses.
result Deep Claim improves claim denial prediction by 22.21%.
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.
LaRT models LLMs' response accuracy and CoT length to evaluate reasoning ability and speed.
problem Valid evaluation of Large Language Models (LLMs) via response accuracy and chain-of-thought length.
method Introduces Latency-Response Theory (LaRT) to jointly model response accuracy and CoT length using latent ability and latent speed.
result LaRT yields higher estimation accuracy and shorter confidence intervals for latent traits compared to IRT.
Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative filtering applications. Hierarchical Poisson factorization (HPF) in particular has proved successful for scala…
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are ei…
Study analyzes non-Markovian effects in financial markets over multiple years.
problem Understanding non-Markovian dynamics and trader interactions in financial markets.
method Empirical analysis of self-response functions and trade sign correlators for different stocks over multiple years.
result Significant variations in traders' interactions over time, indicating changes in market mechanisms.
Paper presents a method for imputing and forecasting structural response from incomplete sensor data.
problem Missing sensor data in structural health monitoring (SHM).
method Incremental Bayesian tensor learning for spatiotemporal missing data reconstruction and forecasting.
result The proposed method achieves accurate and robust imputation and prediction even with high rates of missing data.
The paper explores strong identifiability and parameter learning in regression models with heterogeneous responses.
problem Understanding heterogeneity in data populations through conditional distributions of a response variable.
method Investigation of strong identifiability, convergence rates, and posterior contraction behavior in finite mixture of regression models.
result Theoretical findings on conditions for strong identifiability and rates of convergence in regression mixture models.
Proposes a method to identify subgroup structure and estimate covariate effects for multivariate response data.
problem Identifying subgroup structure and estimating covariate effects in multivariate response data.
method Joint heterogeneity and reduced-rank learning framework using rank-constrained pairwise fusion penalization.
result Established the asymptotic properties of the estimators and proposed a predictive information criterion for rank selection.
When simulating a complex stochastic system, the behavior of output response depends on input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty into the system. The quantification of the impact of input uncertainty on output response has been extensively studied. Most…
The paper establishes concentration bounds for embeddings of generative models.
problem Analyzing statistical properties of generative models.
method High probability concentration bounds on sample vector embeddings using Data Kernel Perspective Space.
result Determines the number of samples needed for accurate approximation of generative model embeddings.
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.
Visualizes board connections for socially responsible investing insights.
problem Understanding corporate governance and sustainability through board connections.
method Data Visualization tool to reveal connections between Directors and Executives.
result Strength of tool in investigating corporate governance and sustainability.
Meta-Router optimizes LLM selection using gold-standard and preference-based data.
problem Training a high-quality LLM router with combined data sources is challenging due to bias and scarcity.
method Developed an integrative causal router training framework to correct bias and improve routing accuracy.
result Our approach delivers more accurate routing and improves the trade-off between cost and quality.
Projected random forests improve circular data prediction with adaptive arc length and finite-sample coverage.
problem Regression with circular responses.
method Adapting linear-response models to circular data using projection and random forest out-of-bag mechanism.
result Projected random forest out-of-bag conformal prediction sets are more efficient and shorter than alternative methods.
New method predicts nonfactuality in LLM responses using semantic isotropy.
problem Assessing trustworthiness of long-form LLM responses efficiently.
method Semantic isotropy of text embeddings on the unit sphere.
result Higher semantic isotropy correlates with lower factual consistency.
ContiVAE estimates individual dose-response curves from unobserved confounders using observational data.
problem Estimating causal effects of continuous treatments considering unobserved confounders.
method Variational auto-encoder with a Tilted Gaussian prior distribution modeling hidden confounders as latent variables.
result ContiVAE outperforms existing methods by up to 62% in predicting individual dose-response curves.