arXiv research

Diffusion models adapt to low-dimensional data regardless of coefficient choices.

Let $\,Ξ\,$ be the crown domain associated with a non-compact irreducible hermitian symmetric space $\,G/K$. We give an explicit description of the unique $\,G$-invariant adapted hyper-Kähler structure on $\,Ξ$,$\$i.$\,$e.$\$compatible with the adapted complex structure $\,J_{ad}\,$ and with the $\,G$-invariant Kähle…

Learn to automatically plug domain-specific modules into a common network.

New methods using natural gradient for structured optimization.

The paper studies hyperkähler structures and adapted complex structures using the Monge-Ampère equation.

Given a closed real analytic Riemannian manifold, we construct and study a one parameter family of adapted complex structures on the manifold of its geodesics.

Two new methods improve graph embedding without needing a complete graph structure.

This study explores complex structures on Lie algebras from graph perspectives.

The Newman-Penrose-Perjes formalism is applied to smooth contact structures on riemannian 3-manifolds. In particular it is shown that a contact 3-manifold admits an adapted riemannian metric if and only if it admits a metric with a divergence-free, constantly twisting, geodesic congruence. The shear of this congruence …

A novel unsupervised domain adaptation method using hierarchical optimal transport.

Adapts manifold structure for better clustering performance.

Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of lear…

Recent breakthrough results in compressed sensing (CS) have established that many high dimensional objects can be accurately recovered from a relatively small number of non- adaptive linear projection observations, provided that the objects possess a sparse representation in some basis. Subsequent efforts have shown th…

We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in ord…

This paper proposes a new method to improve domain adaptation by distinguishing between marginal and dependence structure differences.

Adaptive tensor modeling preserves continuity in multidimensional data.

Study integrability of specific geometric structures on odd Courant algebroids.

Spatial Adapter adds structured spatial representation to frozen predictors.

Graded bundles are a particularly nice class of graded manifolds and represent a natural generalisation of vector bundles. By exploiting the formalism of supermanifolds to describe Lie algebroids we define the notion of a weighted $A$-connection on a graded bundle. In a natural sense weighted $A$-connections are adapte…

Graph-Relational Domain Adaptation (GRDA) adapts domains based on their graph structure.

Nash integrates covariate-specific side info into sparse regression via neural networks.

Anomaly detection in networks often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on. Financial fraud schemes are one such example, where more or less intricate schemes are employed in order to elude transaction security protocols. We investigate the problem of learning …

We consider the problems of detection and localization of a contiguous block of weak activation in a large matrix, from a small number of noisy, possibly adaptive, compressive (linear) measurements. This is closely related to the problem of compressed sensing, where the task is to estimate a sparse vector using a small…

Recent breakthrough results in compressive sensing (CS) have established that many high dimensional signals can be accurately recovered from a relatively small number of non-adaptive linear observations, provided that the signals possess a sparse representation in some basis. Subsequent efforts have shown that the perf…

Geometric methods solve sampling, optimisation, inference, and adaptive decision-making.

Adaptive framework improves nonparametric dimensionality reduction.

Adapts Bartnik method to Hilbert manifold structure for vacuum constraint equations.

Paper proposes SCQ and P-TAMS for structured OOD testing in high-stakes ML.

Flag manifolds are in general not symmetric spaces. But they are provided with a structure of $\mathbb{Z}_2^k$-symmetric space. We describe the Riemannian metrics adapted to this structure and some properties of reducibility. We detail for the flag manifold $SO(5)/SO(2)\times SO(2) \times SO(1)$ what are the conditions…

Paper proposes a new method to optimize robot body structure and control policy.

In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously. We first cast the graph structure learning problem as a similarity metric learning problem and…

New method uses limited labeled data and multiple starts to adapt models across domains.

In this paper we formally analyse the use of sparse filtering algorithms to perform covariate shift adaptation. We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation. We prove that sparse filtering can perform adaptation only if the conditional…

Modular neural causal models outperform other models in generalization and adaptation.

Solvable structures, likewise solvable algebras of local symmetries, can be used to integrate scalar ODEs by quadratures. Solvable structures, however, are particularly suitable for the integration of ODEs with a lack of local symmetries. In fact, under regularity assumptions, any given ODE always admits solvable struc…

Optimal Transport has recently gained interest in machine learning for applications ranging from domain adaptation, sentence similarities to deep learning. Yet, its ability to capture frequently occurring structure beyond the "ground metric" is limited. In this work, we develop a nonlinear generalization of (discrete) …

cKAM improves adaptive sampling by incorporating a cyclical stepsize scheme.

StrADiff separates sources from mixtures without labels, using structured priors.

Adaptive algorithm learns tensor network structures from data.

SPARTAN learns sparse interaction graphs between objects in scenes.

New algorithm optimizes MCMC sampling for structural dynamic models.

In this paper, we give a new construction of the adapted complex structure on a neighborhood of the zero section in the tangent bundle of a compact, real-analytic Riemannian manifold. Motivated by the "complexifier" approach of T. Thiemann as well as certain formulas of V. Guillemin and M. Stenzel, we obtain the polari…

New connections defined for a specific geometric structure.

The Random Projection Tree structures proposed in [Freund-Dasgupta STOC08] are space partitioning data structures that automatically adapt to various notions of intrinsic dimensionality of data. We prove new results for both the RPTreeMax and the RPTreeMean data structures. Our result for RPTreeMax gives a near-optimal…

Oracle inequality for sparse neural nets adapts to unknown structure.

This work explores adaptive strategies for multi-armed bandits with causal structure, achieving optimal regret bounds.

Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain adaptation (DA) tasks based on deep neural networks. Until very recently, existing adversarial domain adaptation (ADA) methods ignore the usefu…