This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends t…
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48 results for “Extensible Framework”
Paper studies how to combine regret minimizers for solving complex games.
problem Solving large-scale extensive-form games with constraints.
method Derives a calculus for constructing regret minimizers for composite convex sets.
result Local regret minimizers for simpler sets can be combined into an aggregate for composite sets.
DELIMIT is a framework extension for deep learning in diffusion imaging, which extends the basic framework PyTorch towards spherical signals. Based on several novel layers, deep learning can be applied to spherical diffusion imaging data in a very convenient way. First, two spherical harmonic interpolation layers are a…
A new machine learning approach for generating high-quality chordal extensions.
problem Defining the definitive relation between chordal extension and optimization algorithm performance.
method On-policy imitation learning scheme mimicking the minimum degree rule to generate high-quality chordal extensions.
result On-policy imitation learning approach effectively learns the minimum degree policy and produces graphs with desirable fill-in characteristics.
We review the production function and the hypothesis of equilibrium in the neoclassical framework. We notify that in a soup of sectors in economy, while capital and labor resemble extensive variables, wage and rate of return on capital act as intensive variables. As a result, Baumol and Bowen's statement of equal wages…
Unified framework for intermittent demand forecasting using renewal processes.
problem Intermittency in demand forecasting.
method Unified framework based on extensions of discrete-time renewal processes.
result Efficacy demonstrated in forecasting practice with favorable predictive accuracy.
Extensions of Lie Bracketsmath.DG
We provide a framework for extensions of Lie algebroids, including non-abelian extensions and Lie algebroids over different bases. Our approach involves Ehresmann connections, which allows straight generalizations of classical constructions. We exhibit a filtration in cohomology and explain the associated spectral sequ…
TorchGAN simplifies GAN training and evaluation with a flexible framework.
problem Training and evaluating GANs can be complex and time-consuming.
method Modular design, extensibility, built-in support for popular models and metrics.
result TorchGAN achieves near-zero overhead compared to vanilla PyTorch.
New framework for RL with opponents, improving learning outcomes.
problem Learning in RL with potential adversaries.
method Threatened Markov Decision Processes (TMDPs) and level-k thinking.
result Improved RL performance by accounting for adversaries.
This paper provides a generic framework of component analysis (CA) methods introducing a new expression for scatter matrices and Gram matrices, called Generalized Pairwise Expression (GPE). This expression is quite compact but highly powerful: The framework includes not only (1) the standard CA methods but also (2) sev…
In many learning tasks, structural models usually lead to better interpretability and higher generalization performance. In recent years, however, the simple structural models such as lasso are frequently proved to be insufficient. Accordingly, there has been a lot of work on "superposition-structured" models where mul…
Enhances exploration in hierarchical networks using mutual information.
problem Limited exploration in hierarchical Deep Q-Networks.
method Adversarial Soft Actor-Critic with mutual information optimization.
result Improves hierarchical network exploration through mutual information maximization.
ATHENA builds flexible defenses against adversarial attacks.
problem Extensive research on adversarial attacks is domain-specific and cannot be easily extended.
method Designing an extensible framework based on diverse weak defenses.
result Comprehensive empirical study demonstrates the effectiveness of ATHENA.
Extends DeTEcT framework for token economies with dynamic and probabilistic parameters.
problem Modeling wealth distribution in token economies with dynamic and probabilistic parameters.
method Introduces four parametrization techniques: dynamic vs static, probabilistic vs non-probabilistic.
result Derives existing wealth distribution models from DeTEcT framework with added restrictions.
Unified multi-view learning framework using OPLS with regularization and deep extensions.
problem Improving multi-view learning for classification and feature extraction.
method Orthonormalized Partial Least Squares (OPLS) with regularization and deep extensions.
result Unified multi-view learning framework with improved performance.
modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler. Its distinguishing features are (i) clear and modular object oriented design (ii) full compatibility with scikit-learn models and workflows. These features make fast prototyping and easy extensibility p…
We develop a framework for understanding the existence of asymptotically flat solutions to the static vacuum Einstein equations with prescribed boundary data consisting of the induced metric and mean curvature on a 2-sphere. A partial existence result is obtained, giving a partial resolution of a conjecture of Bartnik …
This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization. Jensen implements a framework of convex (or loss) functions, convex optimization algorithms (including Gradient Descent, L-BFGS, Stochastic Gradient Descent, Conjugate Gradient, etc.), …
A novel online framework for analyzing multidimensional functional data.
problem Analysis of multidimensional functional data streams poses significant challenges.
method Online functional principal component analysis using tensor product splines on a Stiefel manifold with Riemannian stochastic gradient descent.
result Efficient and scalable modeling of multidimensional functional data.
The aim of this paper is two-fold: first, we look at the fractional Laplacian and the conformal fractional Laplacian from the general framework of representation theory on symmetric spaces and, second, we construct new boundary operators with good conformal properties that generalize the fractional Laplacian using an e…
Framework for neural network inference on encrypted data.
problem Privacy-preserving machine learning with homomorphic encryption.
method Extensible framework using TensorFlow and SEAL, exposing algebra and deep learning primitives.
result Framework allows training and inference of neural networks on encrypted data.
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed norm regularizers, including those that induce sparsity, to be imposed on a dictionary of vector-valued Reproducing Kernel Hilbert Spaces. W…
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed norm regularizers, including those that induce sparsity, to be imposed on a dictionary of vector-valued Reproducing Kernel Hilbert Spaces. W…
Cardea automates machine learning for EHRs, improving model building efficiency.
problem Lack of a trusted, open-source framework for automated machine learning in EHRs.
method Uses FHIR for data structure, AUTOML frameworks for feature engineering, model selection, and tuning, and an adaptive data assembler.
result Demonstrates framework's effectiveness on 5 prediction tasks, highlighting its flexibility and human competitiveness.
A novel regression method using Kirszbraun extension with improved runtime and performance.
problem Regression between Hilbert spaces.
method Framework based on Kirszbraun's extension theorem, decomposed into training and prediction stages solved via MWU scheme.
result Empirical results show a significant improvement over standard solvers.
New model explains how concepts grow based on experience.
problem Existing models assume fixed representation; new model allows for growth.
method Geometric framework with MDL criterion for basis extension.
result Conceptual growth is selective and conservative, exposing or amplifying residual error.
Framework extends neural operators to handle functions outside training set.
problem Robust handling of functions beyond the training set.
method Kernel approximation techniques and Reproducing Kernel Hilbert Spaces (RKHSs) theory.
result Theoretical framework and empirical validation for reliable function extension.
We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction. Our framework is general and it subsumes several well-known SMF formulations in the literature. We perform a non-asymptotic convergence analysis of our framework and derive computational and …
Study of Anosov flows using microlocal analysis for ergodicity and mixing properties.
problem Ergodicity and mixing properties of Anosov flows and their isometric extensions.
method Microlocal analysis of Pollicott-Ruelle resonances to study isometric extensions of Anosov flows.
result Ergodicity of frame flow on negatively-curved Riemannian manifolds under specific curvature assumptions.
Interpreting a nonparametric regression model with many predictors is known to be a challenging problem. There has been renewed interest in this topic due to the extensive use of machine learning algorithms and the difficulty in understanding and explaining their input-output relationships. This paper develops a unifie…
We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights by providing through the data perturbations we propose a multi-view of the assets' clustering behaviour. The perturbation framework is illust…
DEHRL extends HRL to handle multiple levels with diverse subpolicies.
problem Handling multiple levels of hierarchical reinforcement learning with diverse subpolicies.
method Extensible and scalable framework built levelwise, focusing on diversity of subpolicies.
result DEHRL outperforms state-of-the-art baselines in multiple domains.
Defines and extends flat pseudo-Riemannian F-Lie algebras.
problem Generating weakly flat Lorentzian non-abelian bi-nilpotent F-Lie algebras.
method Constructs double extensions of flat pseudo-Riemannian F-Lie algebras.
result Provides a framework for generating all weakly flat Lorentzian non-abelian bi-nilpotent F-Lie algebras.
Unified framework improves model compression while maintaining robustness.
problem Achieving high compression ratios without sacrificing adversarial robustness.
method Adversarially Trained Model Compression (ATMC) framework integrating multiple compression techniques.
result ATMC achieves better trade-off between model size, accuracy, and robustness.
Extends growth properties of hyperbolic groups to their extensions.
problem Quantifying subgroup alternatives in group laws.
method Develops a framework for preserving exponential growth in extensions of hyperbolic groups.
result Automorphism groups of certain hyperbolic and Artin groups have locally uniform exponential growth.
Proposes a new framework for uncertainty evaluation in ML classification models.
problem Uncertainty evaluation for ML classification models not addressed by existing metrological guidelines.
method Develops a metrological framework based on probability mass functions and summary statistics.
result Extends the GUM to uncertainty for nominal properties, applicable to ML classification models.
We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a gamma process. We derive a posterior characterization and a simple and effective Gibbs…
We propose a framework for training GANs on composed data, improving model modularity and interpretability.
problem Training GANs on complex, composed data.
method Composition/decomposition framework for adversarially training GANs on composed data.
result Improves modularity, extensibility, and interpretability of GANs.
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate various types of nonlinearities within a unified framework, including sigmoid, tanh and ReLU, the most commonly used nonlinearities in neural…
Paper analyzes Bartnik's quasi-local mass conjectures and their validity.
problem Understanding the validity of Bartnik's quasi-local mass conjectures.
method Developed a framework to analyze Bartnik's static vacuum extension conjecture.
result Proved the static vacuum extension conjecture is not true in general.
New framework SEU solves lifelong learning's catastrophic forgetting issue.
problem Catastrophic forgetting in lifelong learning.
method Introduces Neural Architecture Search into lifelong learning to dynamically adapt model structures for different tasks.
result Achieves higher accuracy with significantly smaller model size (25-33% of state-of-the-art methods).
Improved sparse Gaussian processes using structured scaling matrices and Power-EP framework.
problem Scaling Gaussian processes for large datasets.
method Structured diagonal scaling matrix and Power-EP framework.
result Structured approximations improve performance without increasing computational cost.
Relay simplifies deep learning compilation across diverse hardware.
problem Challenges in extending DL frameworks to diverse models and hardware.
method Relay introduces a unified, expressive intermediate representation and extension mechanisms for hardware portability.
result Relay achieves competitive performance across various hardware platforms.
Local well-posedness proved for Bartnik static extension near Schwarzschild spheres.
problem Proving well-posedness for the Bartnik static extension problem near Schwarzschild spheres.
method Introduced a geodesic gauge to formulate governing equations as coupled elliptic and transport equations; used Bochner-measurable functions for transport equations.
result Established local well-posedness for arbitrary Bartnik data near Schwarzschild spheres, including those with small mean curvature.
The SO(3) Kauffman polynomial and the chromatic polynomial of planar graphs are categorified by a unique extension of the Khovanov homology framework. Many structural observations and computations of homologies of knots and spin networks are included.
New smooth 2-group extensions from bundle gerbes on manifolds.
problem Classifying equivariant structures on bundle gerbes.
method Global approach to parallel transport, homotopy-coherent associated bundle construction.
result New models for the string group of compact simply-connected Lie groups.
In an -framework, we present a few extension theorems for linear operators. We focus the attention on majorant preserving and sandwich preserving types of extensions. These results are then applied to the study of price systems derived by a reasonable restriction of the class of equivalent martingale measures…
In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process. In this paper, we introduce Threatened Markov Decision Processes (TMDPs), which provide a framework to support a decision maker against a potential adversary in…