Algorithm aligns correlated Erdős-Rényi graphs efficiently.
problem Graph alignment in correlated Erdős-Rényi graphs.
method A canonical labeling algorithm with two steps: degree-based matching and bipartite graph alignment.
result The algorithm succeeds in aligning correlated Erdős-Rényi graphs in a specific time complexity region.
New schemes improve vertex nomination in stochastic block models.
problem Ordering vertices with unknown labels in a network.
method Canonical sampling and extended spectral nomination schemes.
result Improved precision and scalability of vertex nomination schemes.
GraphGen generates large labeled graphs efficiently and accurately.
problem Scalability and comprehensive evaluation of graph generation techniques.
method Converts graphs to sequences using minimum DFS codes and learns joint distributions with an LSTM.
result Significantly faster and better quality than state-of-the-art techniques.
End-to-end deep learning for multi-view clustering improves accuracy across various data types.
problem Limited multi-view clustering methods for general data types and suboptimal two-stage process.
method Permutation-based canonical correlation objective for fused representations; pseudo-labels for clustering; theoretical error bound.
result Proposed model provides meaningful fused representations and effective clustering across multiple views.
This paper provides an overview of CCA-based multi-view learning approaches.
problem Fusing data from different sources or subsets.
method Canonical correlation analysis (CCA) for mapping data onto a common space with maximum correlation.
result Overview of many representative CCA-based multi-view learning approaches.
End-to-end CCA optimizes both discriminative and latent space projections for multi-view learning.
problem Lack of class label information in CCA for multi-view learning tasks.
method Simultaneously optimizes a CCA-based and a task objective in an end-to-end manner to learn a non-linear CCA projection.
result Significant improvement in cross-view classification, regularization with a second view, and semi-supervised learning.
Efficient algorithm for CCA on Riemannian manifolds with fast convergence.
problem Efficiently computing canonical correlation components on Riemannian manifolds.
method Reparametrization of projection matrices for stochastic optimization on Riemannian manifolds.
result Achieves $O( rac{1}{t})$ convergence rate for top k k k components with O ( d 2 k ) O(d^2k) O ( d 2 k ) runtime complexity. The abstract discusses homological stability in topological moduli spaces.
problem Homological stability in topological moduli spaces.
method Constructing canonical resolutions and introducing coefficient systems.
result Homological stability for various moduli spaces.
The paper studies Weyl-minimal surfaces and their adjunction inequality.
problem Investigating Weyl-minimal surfaces in conformal manifolds.
method Analyzes Weyl-minimal surfaces and their relation to Eells-Salamon curves.
result Branched Weyl-minimal surfaces satisfy the adjunction inequality.
New method uses unlabeled data to improve generalization bounds for deep learning.
problem Vacuous guarantees and shrinking holdout sets for overparameterized models.
method Augmenting labeled training set with unlabeled data and training as usual.
result Proves tight upper bounds on true risk for 0-1 empirical risk minimization.
This study proposes a method to generate biosignals with controlled characteristics using GANs and latent variable analysis.
problem Unclear relationship between input and generated data from GANs, inability to control generated data characteristics.
method Recurrent GANs with latent variable analysis using CCA to control generated biosignals.
result Effective control of biosignal characteristics using proposed GAN method and latent variable analysis.
Proposes a new calibration error estimator for deep neural networks.
problem Improves calibration of deep neural networks, especially for canonical calibration.
method Uses a Dirichlet kernel density estimate to create a low-bias, trainable calibration error estimator.
result Asymptotically converges to true L p L_p L p calibration error, enabling efficient estimation and mini-batch updates. Efficient algorithm for orthogonal canonical correlation analysis (OCCA).
problem Solving the OCCA problem with orthogonality constraints.
method Sub-maximization problem with self-consistent-field (SCF) iteration for trace-fractional structure and orthogonal linear projections.
result Proposed algorithm converges globally to a KKT point and is more efficient.
Let X X X be a simplicial complex with a piecewise linear function f : X → R f:X\to\mathbb{R} f : X → R . The Reeb graph R e e b ( f , X ) Reeb(f,X) R ee b ( f , X ) is the quotient of X X X , where we collapse each connected component of f − 1 ( t ) f^{-1}(t) f − 1 ( t ) to a single point. Let the nodes of R e e b ( f , X ) Reeb(f,X) R ee b ( f , X ) be all homologically critical points where any homology of the corresponding c…
New algorithms detect communities in sparse graphs with labeled data.
problem Detecting communities in sparse graphs with limited labeled data.
method Introduces two algorithms: combinatorial and optimization-based, to integrate labeled data with graph structures.
result Detection of communities is feasible throughout the parameter domain with arbitrary labeled data.
New method uses limited labeled data and multiple starts to adapt models across domains.
problem Accurate predictions in target domain with few labeled data.
method Fine-tuning from multiple adaptive starts, extending UDA methods.
result Minimax-optimal target performance with limited labeled target data.
Study shows harmful overfitting in Sobolev spaces even as training data grows.
problem Harmful overfitting in Sobolev spaces under noisy conditions.
method Geometric argument using Sobolev inequalities.
result Approximately norm-minimizing interpolators exhibit harmful overfitting.
This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phra…
Paper shows semi-supervised clustering is equivalent to a type of source coding.
problem Multiclass labeling of unlabeled elements with noisy binary queries.
method Locally encodable source coding approach, using pairwise queries.
result Lower bounds on the number of queries required for correct labeling.
The paper finds features correlated with each other for better data inference.
problem Finding relevant features for statistical inference between two data views.
method Deep canonical correlation analysis (DCCA) to find correlated features, constructing non-parametric joint probability distribution.
result The method provides better inference and regularization in supervised learning.
The purpose of this paper is twofold. On one hand, we introduce a modification of the dual canonical basis for invariant tensors of the 3-dimensional irreducible representation of U q ( s l 2 ) U_q(sl_2) U q ( s l 2 ) , given in terms of Jacobi diagrams, a central tool in quantum topology. On the other hand, we use this modified basis to study t…
This research improves representation learning for new domains with limited new supervision.
problem Learning representations that generalize well to new domains with minimal new data.
method Encourages linearity of factors of variation through learned linear transformations called latent canonicalizers.
result Reduces the number of observations needed to generalize to a similar target domain compared to supervised baselines.
A new method learns meaningful distances between samples using optimal transport.
problem Learning meaningful distances between samples in datasets without labeled data.
method Computes OT distances between samples and features using singular vectors of a function mapping ground metrics to OT distances.
result Wasserstein Singular Vectors provide a scalable solution for unsupervised ground metric learning.
We show that the Kahler-Ricci flow on an algebraic manifold of positive Kodaira dimension and semi-ample canonical line bundle converges to a unique canonical metric on its canonical model. It is also shown that there exists a canonical measure of analytic Zariski decomposition on an algebraic manifold of positive Koda…
In this article we construct a canonical Kähler-Einstein current on a LC (log canonical) pairs of log general type as the limit of a sequence of canonical Kähler-Einstein currents on KLT(Kawamata log terminal) pairs of log general type. We call the volume form associated with the canonical Kähler-Einstein current the c…
New Hamiltonian Monte Carlo method for non-canonical dynamics.
problem Incompatibility of canonical symplectic structure with non-canonical dynamics.
method Developed a framework for Hamiltonian Monte Carlo using non-canonical symplectic structures with implicit integration.
result Non-canonical Hamiltonian Monte Carlo provides sampling advantages.
This paper studies the problem of learning clusters which are consistently present in different (continuously valued) representations of observed data. Our setup differs slightly from the standard approach of (co-) clustering as we use the fact that some form of `labeling' becomes available in this setup: a cluster is …
The study of Euclidean submanifolds with incompressible canonical vector fields.
problem Characterizing Euclidean submanifolds with incompressible canonical vector fields.
method Analyzing the canonical vector field properties and conditions for incompressibility.
result Necessary and sufficient conditions for the canonical vector field of a Euclidean submanifold to be incompressible.
Improved linear regression with privacy and robustness guarantees.
problem Private and robust linear regression with adversarial corruption.
method Differentially private stochastic gradient descent with full-batch gradient descent and adaptive clipping.
result Near optimal sample complexity for both private and robust linear regression.
The paper finds canonical triangulations for specific 3-manifolds.
problem Finding canonical decompositions for cusped hyperbolic 3-manifolds.
method Showed local convexity at every face of the geometric triangulation.
result Found canonical triangulations for Dehn fillings of the Borromean rings link complement and related manifolds.
This paper rethinks confidence calibration under covariate shifts.
problem Calibration methods struggle with covariate shifts and unstable importance weighting.
method Derives Expectation consistency condition and proposes Expectation consistency loss (ECL).
result ECL loss is compatible with various types of calibration and has the same sample complexity as ECE.
The paper defines and studies canonical parameters on surfaces in 4D space.
problem Understanding surfaces in 4D space without minimal points.
method Defining and proving existence of canonical principal parameters.
result Surfaces in 4D space are uniquely determined by four functions satisfying partial differential equations.
Counterexample disproves log canonical Beauville--Bogomolov decomposition.
problem Disproving the log canonical Beauville--Bogomolov decomposition.
method Constructing a specific log canonical, K-trivial variety with non-birational fibers.
result Provides a counterexample to the Beauville--Bogomolov decomposition in the log canonical setting.
Canonical correlation analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables. In high dimensional setting, the classical canonical correlation analysis breaks down. We propose a sparse canonical correlation analysis by adding l1 constraints on the canonical vec…
Tutorial explains various canonical correlation methods.
problem Analyzing paired sets of variables.
method Regularized, kernel, sparse, and Bayesian CCA.
result Comprehensive overview of canonical correlation methods.
Paper classifies compact symmetric triads using double Satake diagrams and canonical forms.
problem Classifying compact symmetric triads.
method Introducing double Satake diagrams and canonical forms, proving their existence and properties.
result Existence and properties of canonical forms for compact simple symmetric triads.
The paper classifies solitons on specific Lie groups.
problem Classifying solitons on three-dimensional Lorentzian Lie groups.
method Computing Wanas tensor and defining algebraic Wanas solitons.
result Classification of algebraic Wanas solitons on specific Lie groups.
The covariant canonical formalism is a covariant extension of the traditional canonical formalism of fields. In contrast to the traditional canonical theory, it has a remarkable feature that canonical equations of gauge theories or gravity are not only manifestly Lorentz covariant but also gauge covariant or diffeomorp…
Study orders of canonical bundles over graph configuration spaces.
problem Determining bundle orders for planar and nonplanar graphs.
method Analyzing configuration spaces of graphs to find bundle orders.
result Bundle orders are 2 for planar and 4 for nonplanar graphs.
Characterizes uncertainty in high-dimensional linear classification models.
problem Assessing uncertainty in high-dimensional linear classification models.
method Approximate message passing algorithm for posterior marginals, closed-form formula for joint statistics.
result Closed-form formula for joint statistics between logistic classifier, Bayesian uncertainty, and ground-truth probit uncertainty.
Using the fact that any minimal strongly regular surface carries locally canonical principal parameters, we obtain a canonical representation of these surfaces, which makes more precise the Weierstrass representation in canonical principal parameters. This allows us to describe locally the solutions of the natural part…
Establishes K"ahler-Ricci flow on log canonical varieties.
problem Existence and convergence of K"ahler-Ricci flow on varieties with log canonical singularities.
method Generalizes previous results for klt singularities, proves convergence, and constructs solutions with flips.
result Existence and convergence of K"ahler-Ricci flow on semi-log canonical models.
Analyzes canonical bundle formula in algebraic geometry.
problem Analyzes the canonical bundle formula in algebraic geometry.
method Uses L 2 L^2 L 2 metrics and valuative equivalence of plurisubharmonic singularities. result Identifies the singularity of the Ohsawa measure and gives a partial answer to a semipositivity question.
In this paper many classes of sets of matrices with entries in F (F=R, F=C, F=H) are introduced. Each class with the corresponding topology determines a real analytical, complex or symplectic manifold for F=R, F=C or F=H respectively. Any such family is called to be a set of canonical forms of matrices. The constructio…
Characterizes canonical elements in compact Lie algebras.
problem Characterizing canonical elements in compact Lie algebras.
method Analyzing Lie algebras and correcting errors in previous work.
result Corrected two errors in Burstall et al. (2004).
Introduces a new canonical connection for Riemannian manifolds and proves Frobenius theorem geometrically.
problem Geometric proof of the Frobenius theorem on Riemannian manifolds.
method Introduces a new canonical connection and applies it to prove the Frobenius theorem.
result Geometric proof of the Frobenius theorem.
The paper classifies Ricci solitons on specific Lorentzian Lie groups.
problem Classifying algebraic Ricci solitons on three-dimensional Lorentzian Lie groups.
method Computed canonical and Kobayashi-Nomizu connections and their curvatures; defined algebraic Ricci solitons.
result Classified algebraic Ricci solitons on specific Lorentzian Lie groups.
Trajectory-level supervision allows efficient offline reinforcement learning.
problem Offline reinforcement learning
method Developing a statistical theory for offline policy optimization from trajectory-level labels
result Proving a high-probability guarantee of order O ~ ( H 2 C s a ( π ⋆ ) / n ) \widetilde O(H^2\sqrt{C_{sa}(\pi^\star)/n}) O ( H 2 C s a ( π ⋆ ) / n )