arXiv research

Abstract: Generalizes multisymplectic forms to vector-valued versions.

Let G be a Lie group and Q a quiver with relations. In this paper, we define G-valued representations of Q which directly generalize G-valued representations of finitely generated groups. Although as G-spaces, the G-valued quiver representations are more general than G-valued representations of finitely generated group…

IFGAN uses feature-specific GANs for missing value imputation.

Based on Colombeau's theory of algebras of generalized functions we introduce the concepts of generalized functions taking values in differentiable manifolds as well as of generalized vector bundle homomorphisms. We study their basic properties, in particular with respect to some new point value concepts for generalize…

In~\cite{Kim} the author generalized the Conway algebra and constructed the invariant valued in the generalized Conway algebra defined by applying two skein relations to crossings, which is called a generalized Conway type invariant. The generalized Conway type invariant is a generalization of Homflypt polynomial. In t…

Dyna is an architecture for model-based reinforcement learning (RL), where simulated experience from a model is used to update policies or value functions. A key component of Dyna is search-control, the mechanism to generate the state and action from which the agent queries the model, which remains largely unexplored. …

We propose randomized least-squares value iteration (RLSVI) -- a new reinforcement learning algorithm designed to explore and generalize efficiently via linearly parameterized value functions. We explain why versions of least-squares value iteration that use Boltzmann or epsilon-greedy exploration can be highly ineffic…

CASP selects reliable policies for two-stage recommender systems by considering both value and support.

The paper defines and analyzes set-valued stochastic integrals for Lévy processes.

Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this pape…

This paper proves a generalization bound for complex-valued neural networks scaling with spectral complexity.

Paper develops efficient RL algorithm for general value function approximation.

Analyzes premium data of Indian non-life insurers, finding GEV distribution best fits Lognormal and GEV extremes.

A new framework for generative modeling using value-driven transport.

Devoted to multi-task learning and structured output learning, operator-valued kernels provide a flexible tool to build vector-valued functions in the context of Reproducing Kernel Hilbert Spaces. To scale up these methods, we extend the celebrated Random Fourier Feature methodology to get an approximation of operator-…

Study validates ML-UQ calibration statistics using simulated reference values.

Boosting framework for vector-valued prediction with geometric stability.

Extends Tanimoto kernel to real-valued functions.

A new imputation method MissARF uses adversarial random forests for fast and accurate missing value imputation.

Infinite dimensional measure-valued processes modeled as polynomial diffusions.

Generalizes mean-value inequality to orbifold setting.

New versions of the set-valued average value at risk for multivariate risks are introduced by generalizing the well-known certainty equivalent representation to the set-valued case. The first "regulator" version is independent from any market model whereas the second version, called the market extension, takes trading …

e-values rank features for model performance.

Develops hierarchical reinforcement learning value function approximators.

Real valued homomorphisms on the algebra of smooth functions on a differential space are described. The concept of generators of this algebra is emphasized in this description.

We use noncommutative localization to construct a chain complex which counts the critical points of a circle-valued Morse function on a manifold, generalizing the Novikov complex. As a consequence we obtain new topological lower bounds on the minimum number of critical points of a circle-valued Morse function within a …

A new method for estimating joint value functions in multi-scene reinforcement learning.

Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.

This paper studies a recent proposal to use randomized value functions to drive exploration in reinforcement learning. These randomized value functions are generated by injecting random noise into the training data, making the approach compatible with many popular methods for estimating parameterized value functions. B…

Estimates peeking effects in p-values to correct bias.

We propose a simple yet powerful framework for modeling integer-valued data, such as counts, scores, and rounded data. The data-generating process is defined by Simultaneously Transforming and Rounding (STAR) a continuous-valued process, which produces a flexible family of integer-valued distributions capable of modeli…

Paper generalizes kernel mean embedding to von Neumann-algebra-valued measures.

Develops Hodge theory for boundary-value problems on general geometric structures.

The paper develops bounds for predictive values in binary classification.

A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with complex-valued vectors followed by (2) taking the absolute value of every entry…

We analyze a notion of multiple valued sections of a vector bundle over an abstract smooth Riemannian manifold, which was suggested by W. Allard in the unpublished note "Some useful techniques for dealing with multiple valued functions" and generalizes Almgren's $Q$-valued functions. We study some relevant properties o…

Framework analyzes physical metrics in soccer to link performance with value.

New method finds unseen states for RL, improving performance.

Study optimality conditions for interval-valued optimization problems on Riemannian manifolds.

Constructs unique bases for CY varieties over valued fields.

Proposes adaptive method for classifying interval-valued time series.

It has been shown recently that deep convolutional generative adversarial networks (GANs) can learn to generate music in the form of piano-rolls, which represent music by binary-valued time-pitch matrices. However, existing models can only generate real-valued piano-rolls and require further post-processing, such as ha…

Paper proves a discrete Schwarz-Pick lemma for generalized circle packings.

Overparameterized models generalize well in offline contextual bandits, but policy-based algorithms struggle.

Shapley value improves model interpretation but not causal inference.

The paper explores dualities in differential equations and their applications in Riemannian geometry.

We introduce the concept of singular values for the Riemann curvature tensor, a central mathematical tool in Einstein's theory of general relativity. We study the properties related to the singular values, and investigate five typical cases to show its relationship to the Ricci scalar and other invariants.

RDIS fills missing values in time series data explicitly.