arXiv research

Study binary choice with asymmetric loss, offering simple solutions.

Machine learning disciplines shift values, not just model types.

New estimator for causal effects in large datasets.

Boosting improves trend detection in financial data.

TSML tackles anomaly detection and pattern discovery in industrial time series data.

Generating user interpretable multi-class predictions in data rich environments with many classes and explanatory covariates is a daunting task. We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised topic model for multi-class classification that can handle both many classes as well as many co…

Estimates linear model from noisy covariates and instruments using spectral regularization.

A new online bootstrap method for time series data.

The paper evaluates various forecasting methods for inflation, finding ML models superior.

Machine Learning improves macroeconomic forecasting by capturing nonlinearities.

We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within …

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects,…

A large number of online services provide automated recommendations to help users to navigate through a large collection of items. New items (products, videos, songs, advertisements) are suggested on the basis of the user's past history and --when available-- her demographic profile. Recommendations have to satisfy the…

Automating the customer analytics process is crucial for companies that manage distinct customer bases. In such data-rich and dynamic environments, visualization plays a key role in understanding events of interest. These ideas have led to the popularity of analytics dashboards, yet academic research has paid scant att…

ML methods improve planetary science data analysis.

Study evaluates machine learning methods for large-scale network reliability, revealing ANN's and PR's performance.

Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environments, the SOEC objective and constraints contain expectations defined with respect to large datasets. Therefore, efficient algorithms for solvi…

The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. Like all nonparametric methods, the HP filter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these…

An Artificial Intelligence (AI) system is an autonomous system which emulates human mental and physical activities such as Observe, Orient, Decide, and Act, called the OODA process. An AI system performing the OODA process requires a semantically rich representation to handle a complex real world situation and ability …

We consider a firm that sells products over $T$ periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each ti…

Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative big-sample datasets on microanatomy, syn…

Sparse alpha-norm regularization has many data-rich applications in Marketing and Economics. Alpha-norm, in contrast to lasso and ridge regularization, jumps to a sparse solution. This feature is attractive for ultra high-dimensional problems that occur in demand estimation and forecasting. The alpha-norm objective is …

Develops methods to simulate rare transitions in molecular systems.

While the volume of electronic health records (EHR) data continues to grow, it remains rare for hospital systems to capture dense physiological data streams, even in the data-rich intensive care unit setting. Instead, typical EHR records consist of sparse and irregularly observed multivariate time series, which are wel…

Evaluates transfer learning methods in dynamic data availability scenarios.

Reconstructs graph structure from noisy data samples.

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especi…

Optimal feature transfer identified through bias-variance analysis.

New bounds for high-dimensional sparse linear bandits, balancing information and regret.

CoreFlow models matrix-valued distributions efficiently, preserving shared low-rank structure.

Unified framework improves NLP tasks by converting diverse problems into text-to-text format.

Study improves predictive models for ICU data across hospitals.

New algorithm computes Schrödinger Bridge for unpaired data translation.

In compressed sensing, a small number of linear measurements can be used to reconstruct an unknown signal. Existing approaches leverage assumptions on the structure of these signals, such as sparsity or the availability of a generative model. A domain-specific generative model can provide a stronger prior and thus allo…

New ASR system handles multiple languages without needing language-specific encoding.

Two algorithms improve Federated RL in diverse environments.

OBSER framework infers sub-environments from objects, outperforming scene-based methods.

Many real-world datasets are labeled with natural orders, i.e., ordinal labels. Ordinal regression is a method to predict ordinal labels that finds a wide range of applications in data-rich domains, such as natural, health and social sciences. Most existing ordinal regression approaches work well for independent and id…

UAED discovers adaptive environments for robust learning.

New approach handles stochastic and partially-observable environments using discrete autoencoders and Monte Carlo tree search.

Self-supervised policy adapts after deployment without rewards.

Bayesian model for multi-environment prediction with latent variable changes.

This paper introduces CENIE to quantify environment novelty for better UED.

Convolutional neural networks learn effective summary statistics for ABC inference.

We consider apprenticeship learning, i.e., having an agent learn a task by observing an expert demonstrating the task in a partially observable environment when the model of the environment is uncertain. This setting is useful in applications where the explicit modeling of the environment is difficult, such as a dialog…

Infinite hierarchical contrastive clustering identifies personal environments linked to health outcomes.

Research shows collective learning across diverse environments is hard due to privacy and security concerns.

LEADS improves model generalization across different environments.