arXiv research

Proposes a partially linear structure to capture nonlinear relationships in mixture of experts models.

Introduces a new model for mapping matrices to matrices, subsuming linear regression.

IDS algorithm optimizes sequential decisions in various monitoring settings.

Flexible DNN for survival data, avoiding proportional hazards assumption.

Study uses deep neural networks for inference in partially linear models with dependent data.

Linear recurrent networks explain reinforcement learning performance in partially observable settings.

Differentiable relaxation for inferring partial orders from noisy linear data.

This paper provides estimation and inference methods for an identified set's boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample s…

AdaptOn achieves logarithmic regret in adaptive control of unknown partially observable linear systems.

We study the problem of regret minimization in partially observable linear quadratic control systems when the model dynamics are unknown a priori. We propose ExpCommit, an explore-then-commit algorithm that learns the model Markov parameters and then follows the principle of optimism in the face of uncertainty to desig…

New method controls linear systems with partial info and disturbances.

New method disentangles perceptual uncertainty and behavioral costs in partially observable systems.

KalmanNet uses neural networks to improve state estimation in systems with unknown dynamics.

The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing each to have either a linear or nonlinear effect on the response. However, the choice of which features to treat as linear or nonlinear is …

A new method combines machine learning with mixed-effects models for better repeated measurement analysis.

PCBM improves neural network generalization by partially observing concepts.

Partial monitoring is a rich framework for sequential decision making under uncertainty that generalizes many well known bandit models, including linear, combinatorial and dueling bandits. We introduce information directed sampling (IDS) for stochastic partial monitoring with a linear reward and observation structure. …

We propose a partially linear additive Gaussian graphical model (PLA-GGM) for the estimation of associations between random variables distorted by observed confounders. Model parameters are estimated using an $L_1$-regularized maximal pseudo-profile likelihood estimator (MaPPLE) for which we prove $\sqrt{n}$-sparsisten…

New algorithms solve robust MDPs efficiently, significantly faster than existing methods.

On any space-like W-surface in the three-dimensional Minkowski space we introduce locally natural principal parameters and prove that such a surface is determined uniquely up to motion by a special invariant function, which satisfies a natural non-linear partial differential equation. This result can be interpreted as …

The Trek Separation Theorem (Sullivant et al. 2010) states necessary and sufficient conditions for a linear directed acyclic graphical model to entail for all possible values of its linear coefficients that the rank of various sub-matrices of the covariance matrix is less than or equal to n, for any given n. In this pa…

Although various distributed machine learning schemes have been proposed recently for pure linear models and fully nonparametric models, little attention has been paid on distributed optimization for semi-paramemetric models with multiple-level structures (e.g. sparsity, linearity and nonlinearity). To address these is…

The paper addresses nonconvex penalized LAD estimation in partial linear models using DNNs.

This paper studies the question of filtering and maximizing terminal wealth from expected utility in a partially information stochastic volatility models. The special features is that the only information available to the investor is the one generated by the asset prices, and the unobservable processes will be modeled …

Paper addresses linear regression with partially mismatched data using local search with theoretical guarantees.

Paper develops a method for estimating PFLM with minimized rates in high dimensions.

New Thompson sampling algorithm for stochastic partial monitoring achieves logarithmic regret.

A one-factor asset pricing model with an Ornstein--Uhlenbeck process as its state variable is studied under partial information: the mean-reverting level and the mean-reverting speed parameters are modeled as hidden/unobservable stochastic variables. No-arbitrage pricing formulas for derivative securities written on a …

The paper uses belief propagation to analyze rankings and partial orders from partial information.

Study of transitivity in partially hyperbolic maps with expanding linear part.

Study efficient reinforcement learning for partially observed systems with linear structure.

Bayesian inverse problems solved with Gaussian models for PDEs.

Proposes a new model to analyze CT scans for lung cancer patients.

The paper tackles causal disentanglement with linear models and interventions.

In the context of sparse recovery, it is known that most of existing regularizers such as $\ell_1$ suffer from some bias incurred by some leading entries (in magnitude) of the associated vector. To neutralize this bias, we propose a class of models with partial regularizers for recovering a sparse solution of a linear …

Variable selection plays an important role in the high-dimensional data analysis. However the high-dimensional data often induces the strongly correlated variables problem. In this paper, we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate. By a simulation study, we s…

New algorithm for partially observable contexts in finance.

AR model forecasts partially observed dynamical time series by estimating evolution function and imputing missing variables.

We tackle linear bandits with partially observable features, achieving sublinear regret.

Paper develops efficient Bayesian inference for enzymatic SRNs with LNA metamodel.

We formulate stochastic partial differential equations on Riemannian manifolds, moving surfaces, general evolving Riemannian manifolds (with appropriate assumptions) and Riemannian manifolds with random metrics, in the variational setting of the analysis to stochastic partial differential equations. Considering mainly …

Study optimizes prediction error for growing-dimensional PFLM models.

It is illustrated a methodology to compute the pure premium for the automobile insurance (claim frequency and severity) using generalized linear models. It is obtained the pure premium for the partial damage loss cover (PPD) using a set of automobile insurance policies with an exposition of a year. It is found that the…

DPLS improves asset pricing by capturing non-linear risk factor structures.

Absolutely partially hyperbolic surface endomorphisms have a coherent center foliation.

TOFU-POV tackles partially observed linear bandits, achieving sublinear regret with low-dimensional action vectors.

New method identifies causal variables from partially observed data.

E-LMC improves spatial field prediction accuracy by linearizing complex fields.