Holistic Generalized Linear Models
- URL: http://arxiv.org/abs/2205.15447v1
- Date: Mon, 30 May 2022 22:08:47 GMT
- Title: Holistic Generalized Linear Models
- Authors: Benjamin Schwendinger, Florian Schwendinger, Laura Vana
- Abstract summary: The $textsfR$ package $textttholiglm$ provides functionality to model and fit holistic generalized linear models.
The high-level interface simplifies the constraint specification and can be used as a drop-in replacement for the $textttstats::glm()$ function.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Holistic linear regression extends the classical best subset selection
problem by adding additional constraints designed to improve the model quality.
These constraints include sparsity-inducing constraints, sign-coherence
constraints and linear constraints. The $\textsf{R}$ package $\texttt{holiglm}$
provides functionality to model and fit holistic generalized linear models. By
making use of state-of-the-art conic mixed-integer solvers, the package can
reliably solve GLMs for Gaussian, binomial and Poisson responses with a
multitude of holistic constraints. The high-level interface simplifies the
constraint specification and can be used as a drop-in replacement for the
$\texttt{stats::glm()}$ function.
Related papers
- Scalable Approximate Algorithms for Optimal Transport Linear Models [0.769672852567215]
We propose a novel framework for solving a general class of non-negative linear regression models with an entropy-regularized OT datafit term.
We derive simple multiplicative updates for common penalty and datafit terms.
This method is suitable for large-scale problems due to its simplicity of implementation and straightforward parallelization.
arXiv Detail & Related papers (2025-04-06T20:37:25Z) - Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators [78.64101336150419]
Predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling.
An alternative approach to such a full-resolved simulation is using a coarse grid and then correcting its errors through a temporalittext model.
We propose an alternative end-to-end learning approach using a physics-informed neural operator (PINO) that overcomes this limitation.
arXiv Detail & Related papers (2024-08-09T17:05:45Z) - Automated Model Selection for Generalized Linear Models [0.0]
We show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models.
We propose a novel pairwise correlation constraint that combines the sign coherence constraint with ideas from classical statistical models.
arXiv Detail & Related papers (2024-04-25T12:16:58Z) - Symmetric Tensor Networks for Generative Modeling and Constrained
Combinatorial Optimization [72.41480594026815]
Constrained optimization problems abound in industry, from portfolio optimization to logistics.
One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space.
In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric networks (TNs) and leverage their applicability as quantum-inspired generative models.
arXiv Detail & Related papers (2022-11-16T18:59:54Z) - Oracle Inequalities for Model Selection in Offline Reinforcement
Learning [105.74139523696284]
We study the problem of model selection in offline RL with value function approximation.
We propose the first model selection algorithm for offline RL that achieves minimax rate-optimal inequalities up to logarithmic factors.
We conclude with several numerical simulations showing it is capable of reliably selecting a good model class.
arXiv Detail & Related papers (2022-11-03T17:32:34Z) - Provably Efficient Model-Free Constrained RL with Linear Function
Approximation [4.060731229044571]
We develop the first model-free, simulator-free algorithm that achieves a sublinear regret and a sublinear constraint violation even in large-scale systems.
Our results are achieved via novel adaptations of the standard LSVI-UCB algorithms.
arXiv Detail & Related papers (2022-06-23T17:54:31Z) - $p$-Generalized Probit Regression and Scalable Maximum Likelihood
Estimation via Sketching and Coresets [74.37849422071206]
We study the $p$-generalized probit regression model, which is a generalized linear model for binary responses.
We show how the maximum likelihood estimator for $p$-generalized probit regression can be approximated efficiently up to a factor of $(1+varepsilon)$ on large data.
arXiv Detail & Related papers (2022-03-25T10:54:41Z) - Universal and data-adaptive algorithms for model selection in linear
contextual bandits [52.47796554359261]
We consider the simplest non-trivial instance of model-selection: distinguishing a simple multi-armed bandit problem from a linear contextual bandit problem.
We introduce new algorithms that explore in a data-adaptive manner and provide guarantees of the form $mathcalO(dalpha T1- alpha)$.
Our approach extends to model selection among nested linear contextual bandits under some additional assumptions.
arXiv Detail & Related papers (2021-11-08T18:05:35Z) - Solving weakly supervised regression problem using low-rank manifold
regularization [77.34726150561087]
We solve a weakly supervised regression problem.
Under "weakly" we understand that for some training points the labels are known, for some unknown, and for others uncertain due to the presence of random noise or other reasons such as lack of resources.
In the numerical section, we applied the suggested method to artificial and real datasets using Monte-Carlo modeling.
arXiv Detail & Related papers (2021-04-13T23:21:01Z) - Bilinear Classes: A Structural Framework for Provable Generalization in
RL [119.42509700822484]
Bilinear Classes is a new structural framework which permits generalization in reinforcement learning.
The framework incorporates nearly all existing models in which a sample complexity is achievable.
Our main result provides an RL algorithm which has sample complexity for Bilinear Classes.
arXiv Detail & Related papers (2021-03-19T16:34:20Z) - Ridge regression with adaptive additive rectangles and other piecewise
functional templates [0.0]
We propose an $L_2$-based penalization algorithm for functional linear regression models.
We show how our algorithm alternates between approximating a suitable template and solving a convex ridge-like problem.
arXiv Detail & Related papers (2020-11-02T15:28:54Z) - On the Adversarial Robustness of LASSO Based Feature Selection [72.54211869067979]
In the considered model, there is a malicious adversary who can observe the whole dataset, and then will carefully modify the response values or the feature matrix.
We formulate the modification strategy of the adversary as a bi-level optimization problem.
Numerical examples with synthetic and real data illustrate that our method is efficient and effective.
arXiv Detail & Related papers (2020-10-20T05:51:26Z) - An Integer Linear Programming Framework for Mining Constraints from Data [81.60135973848125]
We present a general framework for mining constraints from data.
In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem.
We show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules.
arXiv Detail & Related papers (2020-06-18T20:09:53Z) - Online DR-Submodular Maximization with Stochastic Cumulative Constraints [17.660958043781154]
We consider online continuous DR-submodular with linear long-term constraints.
Online Lagrangian Frank-Wolfe (OLFW) algorithm to solve this class of online problems.
arXiv Detail & Related papers (2020-05-29T17:55:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.