arXiv research

Generative models help analyze complex travel behavior data.

Improved density estimation for mixed discrete-continuous data.

This article provides the role of big idea statisticians in future of Big Data Science. We describe the `United Statistical Algorithms' framework for comprehensive unification of traditional and novel statistical methods for modeling Small Data and Big Data, especially mixed data (discrete, continuous).

A new framework handles hybrid action spaces in reinforcement learning.

Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (S&E) applications where risk management is a core component, well-characterized uncertainty estimates must accompany predictions. However, S…

We consider a square-integrable semimartingale and investigate the convex order relations between its discrete, continuous and predictable quadratic variation. As the main results, we show that if the semimartingale has conditionally independent increments and symmetric jump measure, then its discrete realized variance…

Hybrid Policy Optimization tackles reinforcement learning in hybrid spaces, improving performance over PPO.

Hybrid SAC improves RL for video games with discrete, continuous actions.

Tabular GAN generates synthetic tables from datasets.

This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. First…

New algorithms tackle multi-agent problems with hybrid action spaces.

This paper presents the R package gRapHD for efficient selection of high-dimensional undirected graphical models. The package provides tools for selecting trees, forests and decomposable models minimizing information criteria such as AIC or BIC, and for displaying the independence graphs of the models. It has also some…

Bayesian optimization tackles mixed discrete-continuous problems with Gaussian processes.

XGBoostLSS extends XGBoost to predict entire conditional distributions.

CatBoostLSS predicts entire conditional distributions for probabilistic forecasting.

AdaCat improves density estimation and planning in autoregressive models.

Causal normalizing flows recover causal models from observational data.

New SGMCMC method controls bias in SSMs for long time series.

Generative models learn manifold structure; new approach uses atlas and geodesic interpolation.

We study the problem of learning sparse structure changes between two Markov networks $P$ and $Q$. Rather than fitting two Markov networks separately to two sets of data and figuring out their differences, a recent work proposed to learn changes \emph{directly} via estimating the ratio between two Markov network models…

Study proposes ATS algorithm to solve market clearing problem in Turkish day-ahead market.

We study a problem of optimal investment/consumption over an infinite horizon in a market consisting of a liquid and an illiquid asset. The liquid asset is observed and can be traded continuously, while the illiquid one can only be traded and observed at discrete random times corresponding to the jumps of a Poisson pro…

Latent feature modeling allows capturing the latent structure responsible for generating the observed properties of a set of objects. It is often used to make predictions either for new values of interest or missing information in the original data, as well as to perform data exploratory analysis. However, although the…

Method estimates joint distribution of bivariate outcomes.

AQL uses amortized inference to handle high-dimensional action spaces in Q-learning.

Co-training improves sequential decision-making policies from multiple views.

Proposes a new algorithm for efficient probabilistic reconciliation of forecasts.

Differentiable segmented models for non-stationary data.

The field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilistic logic programming: the enabling of stochastic primitives in logic programming, which is now increasingly seen to provide a declarative bac…

CANDI solves the gap between continuous and discrete diffusion models for text generation.

Kronos improves financial time series analysis with a pre-trained model.

Estimates treatment effects in randomized experiments with non-compliance.

New method for causal discovery using peeling algorithms for various data types.

A novel method optimizes variable-stiffness structures for better strength and weight.

New methods for Bayesian inference using mean shift particle systems.

Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly from data is a deep challenge, the main computational bottleneck being inference that is intractable. Tractable learning is a powerful new p…

AI learns to design chemical processes efficiently.

DRCD identifies causal direction between continuous and discrete variables using density ratio monotonicity.

We study a problem of optimal investment/consumption over an infinite horizon in a market consisting of two possibly correlated assets: one liquid and one illiquid. The liquid asset is observed and can be traded continuously, while the illiquid one can be traded only at discrete random times corresponding to the jumps …

Branching Flows generates sequences of varying lengths using binary trees.

New algorithm improves online learning with reduced discretization.

Given independent samples generated from the joint distribution $p(\mathbf{x},\mathbf{y},\mathbf{z})$, we study the problem of Conditional Independence (CI-Testing), i.e., whether the joint equals the CI distribution $p^{CI}(\mathbf{x},\mathbf{y},\mathbf{z})= p(\mathbf{z}) p(\mathbf{y}|\mathbf{z})p(\mathbf{x}|\mathbf{z…

CeCNN predicts SE and AL from UWF images, improving myopia screening.

Survey of automated algorithm selection for various problems.

Data preprocessing improves data quality for robust data mining.

Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks,…

A new method for handling imbalanced big data using ensembles and smart data.

Prevents sensitive data generation in diffusion models using labeled and unlabeled data.