arXiv research

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships. The model allows straightforward variatio…

This paper analyzes a simplified strategy for nonlinear control using local linear models and iLQR updates.

New insights into continual learning for deep models, showing convergence issues but local linear solutions.

Local model for Poisson manifolds around submanifolds.

The paper extends a variance gamma model to quadratic functions, reducing arbitrage and computational costs.

The network Lasso (nLasso) has been proposed recently as an efficient learning algorithm for massive networked data sets (big data over networks). It extends the well-known least absolute shrinkage and selection operator (Lasso) from learning sparse (generalized) linear models to network models. Efficient implementatio…

New GP model estimates piecewise continuous functions.

Locally adaptive interpretable regression improves linear regression's predictability.

This paper simplifies deep ReLU networks into local linear models for better interpretability.

This paper improves Bayesian neural nets by using local linearization.

Existing works on "black-box" model interpretation use local-linear approximations to explain the predictions made for each data instance in terms of the importance assigned to the different features for arriving at the prediction. These works provide instancewise explanations and thus give a local view of the model. T…

Study proposes Local Linear Encoding for better feature discretization.

This paper achieves optimal regret bounds for locally private linear contextual bandit.

For their ability to capture non-linearities in the data and to scale to large training sets, local Support Vector Machines (SVMs) have received a special attention during the past decade. In this paper, we introduce a new local SVM method, called L$^3$-SVMs, which clusters the input space, carries out dimensionality r…

NGSLL combines DNN accuracy with linear model interpretability.

PatternLocal improves XAI for non-linear models by suppressing suppressor variables.

We show LLMs can be locally linear, enabling better control of activations.

New classifier combines locally linear kernels for fast and accurate non-linear classification.

The Bass model is calibrated to vanilla options using a fixed-point equation.

New method converts LVAs into linear projections for better understanding of complex models.

Local SGD proves efficient in overparameterized linear regression.

In this paper, we propose and study random maxout features, which are constructed by first projecting the input data onto sets of randomly generated vectors with Gaussian elements, and then outputing the maximum projection value for each set. We show that the resulting random feature map, when used in conjunction with …

BART and MOTR-BART improve tree-based predictions with local linear models.

Cyclic coordinate descent identifies models in finite time and converges linearly.

SyMPLER improves time series forecasting in nonstationary environments with explainable models.

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

This paper develops a new method to model treatment effects that are heterogeneous across different quantiles.

Estimates LLC for deep linear networks up to 100M parameters.

Gradient descent converges linearly for overparameterized linear networks.

We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constr…

This paper describes another extension of the Local Variance Gamma model originally proposed by P. Carr in 2008, and then further elaborated on by Carr and Nadtochiy, 2017 (CN2017), and Carr and Itkin, 2018 (CI2018). As compared with the latest version of the model developed in CI2018 and called the ELVG (the Expanded …

This paper introduces a new method for semi-supervised learning on high dimensional nonlinear manifolds, which includes a phase of unsupervised basis learning and a phase of supervised function learning. The learned bases provide a set of anchor points to form a local coordinate system, such that each data point $x$ on…

The paper proposes an expanded version of the Local Variance Gamma model of Carr and Nadtochiy by adding drift to the governing underlying process. Still in this new model it is possible to derive an ordinary differential equation for the option price which plays a role of Dupire's equation for the standard local volat…

Discriminative latent-variable models are typically learned using EM or gradient-based optimization, which suffer from local optima. In this paper, we develop a new computationally efficient and provably consistent estimator for a mixture of linear regressions, a simple instance of a discriminative latent-variable mode…

In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create non-convex loss surfaces. Then, does depth alone create bad local minima? In this paper, we prove that without nonlinearity, depth alone does not create bad local minima, although it induces non-convex loss surface. Using this insight, we greatl…

Non-linear Hopf manifolds can be embedded into linear ones and admit LCK metrics.

Improves local model explanations using GANs and Linear Model Trees.

Study shows TAP free energy minimization provides better posterior inference in high-dimensional linear models.

A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional probability tables. Recent research reduces this complexity by modeling local structure in the probability tables. We examine the use of log-linear local models. While log-linear models in this con…

Regression is an important task in machine learning and data mining. It has several applications in various domains, including finance, biomedical, and computer vision. Recently, network Lasso, which estimates local models by making clusters using the network information, was proposed and its superior performance was d…

Local approach learns causal structure of linear Gaussian polytree models from interventional data.

In this paper we describe the local Ricci and Bianchi identities for an h-normal N-linear connection DΓ(N) on the dual 1-jet space J^{1*}(T,M). To reach this aim, we firstly give the expressions of the local distinguished (d-) adapted components of torsion and curvature tensors produced by DΓ(N), and then we analyze th…

We present ARU, an Adaptive Recurrent Unit for streaming adaptation of deep globally trained time-series forecasting models. The ARU combines the advantages of learning complex data transformations across multiple time series from deep global models, with per-series localization offered by closed-form linear models. Un…

Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are the most popular non-linear predictive models used in practice today, yet comparatively little attention has been paid to explaining their predictions. Here we significantly improve the interpretability of tree-bas…

Gradient method converges locally linearly for overparameterized Gaussian mixtures.

Study local properties of Chern-scalar curvature through linearization stability.

Calibration of stochastic local volatility (SLV) models to their underlying local volatility model is often performed by numerically solving a two-dimensional non-linear forward Kolmogorov equation. We propose a novel finite volume (FV) discretization in the numerical solution of general 1D and 2D forward Kolmogorov eq…

This paper discusses topological and locally linear actions of finite groups on $S^4$. Local linearity of the orientation preserving actions on $S^4$ forces the group to be a subgroup of $SO(5)$. On the other hand, orientation reversing topological actions of "exotic" groups $G$ (i.e. $G\not\subset O(5)$) on $S^4$ are …