We offer a new, rigorous approach to conditional mean embeddings without operator constraints.
problem Lack of rigorous, operator-free approach to conditional mean embeddings.
method Measure-theoretic approach to conditional mean embeddings.
result Natural regression interpretation and universal consistency of empirical estimates.
Develops a rigorous theory for conditional mean embeddings.
problem Efficient conditioning of probability distributions in RKHSs.
method Mathematical theory for both centred and uncentred covariance operators.
result Significantly weakens conditions for applicability of CMEs.
Bayesian deconditional embeddings solve complex function recovery.
problem Recovering original functions from conditional mean observations.
method Formalizes deconditional kernel mean embeddings as Bayesian inference, connects to task-transformed Gaussian processes.
result Establishes deconditional kernel means as posterior predictive mean, providing Bayesian interpretations and uncertainty.
New recursive algorithm estimates conditional kernel mean embeddings in Hilbert space.
problem Estimating conditional distributions in RKHS for supervised learning.
method Recursive algorithm in L2 space for conditional kernel mean map. result Strong L2 consistency of recursive estimator proved. New learning rates for embeddings in RKHSs, even when the target is not Hilbert-Schmidt.
problem Applying conditional mean embeddings to complex ML/RL settings with infinite-dimensional RKHSs.
method Developed novel learning rates using interpolation theory for RKHSs, derived explicit adaptive rates for sample estimator.
result Achieved uniform convergence rates in the output RKHS for certain parameter regimes.
A new method learns hyperparameters for conditional kernel mean embeddings using Rademacher complexity bounds.
problem Hyperparameter tuning for conditional kernel mean embeddings is challenging and computationally expensive.
method Proposes a hyperparameter learning framework based on Rademacher complexity bounds for scalable kernel hyperparameter tuning.
result Demonstrates improved performance over competing methods and can incorporate deep neural network weights.
Paper develops a unified framework for measuring differences between conditional distributions.
problem Comparing conditional distributions in a unified and theoretically sound manner.
method Kernel embeddings and conditional maximum mean discrepancy (CMMD) framework.
result Established a coherent framework for measuring divergence between conditional distributions.
The study finds conditions for embedding sphere subbundles with specific mean curvatures.
problem Embedding sphere subbundles with prescribed mean curvatures in Riemannian vector bundles.
method Analyzes embeddings of sphere subbundles into Riemannian vector bundles with prescribed mean curvatures.
result Conditions for embedding sphere subbundles with specific mean curvatures are identified.
We consider mean-convex Alexandrov embedded surfaces in the round unit 3-sphere, and show under which conditions it is possible to continuously deform these preserving mean-convex Alexandrov embeddedness.
Meta-learning for estimating complex conditional distributions.
problem Estimating conditional densities in multimodal distributions.
method Noise contrastive estimation with kernel mean embeddings.
result Meta-learning can share representations across tasks for conditional density estimation.
Study optimizes learning rates for conditional mean embedding estimates.
problem Consistency of kernel ridge regression for conditional mean embedding.
method Adaptive statistical learning rate derived for misspecified setting.
result Upper bound matches optimal O(logn/n) rates without assuming finite dimensionality. Two spheres found with specific curvature constraints.
problem Existence of spheres with prescribed mean curvature.
method Proved existence of at least two embedded spheres with curvature h satisfying pinching condition. result Existence of at least two embedded spheres with prescribed mean curvature h. Efficiently approximates kernel mean embeddings using Nyström method.
problem Computational cost of kernel mean embeddings in large-scale settings.
method Nyström method for approximating a small random subset of the dataset.
result Upper bound on approximation error with sufficient subsample size conditions.
KELFI improves inference accuracy in likelihood-free settings with limited simulations.
problem Intractable likelihood evaluations in likelihood-free inference.
method Kernel embedding likelihood-free inference (KELFI) learns model hyperparameters to balance accuracy and efficiency.
result Improved accuracy and efficiency on challenging inference problems in ecology.
New tests for binary classification regression functions without distribution assumptions.
problem Testing regression functions in binary classification without distributional assumptions.
method Conditional kernel mean embeddings and resampling-based framework.
result Distribution-free hypothesis tests with exact type I error control.
Proposes a new method to analyze the distributional effects of treatments.
problem Analyzing the full distributional impact of treatments beyond just the mean.
method Uses kernel conditional mean embeddings and U-statistic regression to investigate the CoDiTE.
result Demonstrates the effectiveness of the proposed method through experiments.
New KQEs improve probability metrics without mean function constraints.
problem Improving probability metrics without relying on mean function representations.
method Kernel quantile embeddings (KQEs) to construct new distances.
result KQEs offer a competitive alternative to MMD with near-linear cost.
Verifies regularity for conditional expectation operators and embeddings, simplifying validation.
problem Characterizing when conditional expectation operators map between function spaces.
method Establishes a verifiable sufficient condition for bounded and Hilbert-Schmidt mappings based on conditional density regularity.
result Averifiable condition for mapping properties of conditional expectation operators simplifies validation.
Neural-Kernel CME tackles scalability and expressiveness challenges in conditional distribution representation.
problem Scalability and expressiveness challenges in kernel conditional mean embeddings.
method Combines deep learning with CMEs using a neural network optimization framework.
result Achieves competitive and often superior performance in conditional density estimation and RL.
Proposes CCME framework for estimating heterogeneous treatment effects.
problem Estimating heterogeneous treatment effects in complex distributions.
method Embeds conditional distributions into RKHS, develops meta-estimators for CCME.
result Establishes finite-sample convergence rates and double robustness for CCME estimators.
Mean curvature flow extended with free boundary condition.
problem Extending mean curvature flow with a free boundary condition.
method Neumann free boundary condition on a mean convex, smooth support surface in 3D Euclidean space.
result Mean curvature and perimeter stay uniformly bounded for extension.
The main point of this paper is that, under suitable conditions on the mean curvature and the Ricci curvature of the ambient space, we can extend Choi-Schoen's Compactness Theorem to compact embedded minimal surfaces to simple immersed compact H-surfaces in a Riemannian manifold with positive Ricci curvature (the mean …
Paper improves MMD estimation for analytical mean embeddings.
problem Improving MMD estimation for distributions with analytical mean embeddings.
method Proposes a tighter concentration result for MMD estimation under semi-explicit settings and extends to unbounded kernels.
result Demonstrates efficiency in real-world applications like index replication and calibration.
Novel approach to OT using kernel mean embeddings controls overfitting and achieves dimension-free sample complexity.
problem Consistently estimate optimal transport plan from samples.
method Pose OT as learning kernel mean embedding, employ MMD regularization.
result ε-optimal recovery of transport plan and map with dimension-free sample complexity.
Genus one singularity appears in mean curvature flow for certain initial conditions.
problem Understanding singularities in mean curvature flow.
method Analyzing one-parameter families of initial conditions in R3. result A robust genus one singularity appears in mean curvature flow.
Paper proves short-term existence of fractional mean curvature flow.
problem Existence of solutions for fractional mean curvature flow with capillary boundary conditions.
method Fixed point argument.
result Short time existence of solutions for fractional mean curvature flow.
We study the Dirichlet problem associated to the equation for self-similar surfaces for graphs over the Euclidean plane with a disk removed. We show the existence of a solution provided the boundary conditions on the boundary circle are small enough and satisfy some symmetries. This is the second step towards the const…
We demonstrate an equivalence between reproducing kernel Hilbert space (RKHS) embeddings of conditional distributions and vector-valued regressors. This connection introduces a natural regularized loss function which the RKHS embeddings minimise, providing an intuitive understanding of the embeddings and a justificatio…
The paper tackles automatic interpretation of manifold coordinates.
problem Finding physical meaning of abstract manifold coordinates.
method Proposes a method to explain embedding coordinates as compositions of functions from a dictionary.
result Demonstrates the effectiveness of the method on data.
Kernel mean embedding maps distributions into RKHS for machine learning.
problem Efficiently representing and comparing probability distributions.
method Mapping distributions into RKHS using kernel methods.
result Kernel mean embedding enables non-parametric operations on distributions.
We carry out the first main step towards the construction of new examples of complete embedded self-similar surfaces under mean curvature flow. An approximate solution is obtained by taking two known examples of self-similar surfaces and desingularizing the intersection circle using an appropriately modified singly per…
Ancient curve shortening flows have entropy and curvature bounds equivalent.
problem Bounding entropy and total curvature for ancient curve shortening flows.
method Equivalence of entropy and total curvature conditions for ancient curve shortening flows.
result Entropy and total curvature bounds are equivalent for ancient curve shortening flows.
The paper introduces new KMEs to capture stochastic process filtrations.
problem Missing filtration information in stochastic processes.
method Higher order kernel mean embeddings (KMEs) conditioned on filtrations.
result Consistent estimators and tests for filtration-sensitive information.
A new model for estimating multivariate densities efficiently.
problem Estimating complex multivariate densities efficiently.
method CDO model based on kernel mean embeddings and RKHS.
result Competitive performance with neural models and Gaussian processes.
Given a positive function F on Sn which satisfies a convexity condition, for 1≤r≤n, we define the r-th anisotropic mean curvature function HrF for hypersurfaces in Rn+1 which is a generalization of the usual r-th mean curvature function. We prove that a compact embedded hypersurface…
CNEs improve network embeddings by adding structural information.
problem Hard embedding of certain networks due to structural properties.
method Bayesian approach to create embeddings that maximize information with given structural properties.
result CNEs outperform state-of-the-art methods in link prediction and multi-label classification.
A new metric compares true and learned causal graphs considering data and graph structure.
problem Comparing true and learned causal graphs accurately.
method Continuous Structural Intervention Distance (CSID) using conditional mean embeddings and maximum mean discrepancy.
result Validated the CSID with synthetic data, showing its effectiveness in comparing causal graphs.
The paper explores conditions for lifting maps between graphs to embeddings.
problem Conditions for embedding maps between graphs.
method Combinatorial techniques and satisfiability of 3-CNF formulas.
result Established necessary and sufficient conditions for lifting.
In this paper, we study the global geometry of complete, constant mean curvature hypersurfaces embedded in n-manifolds. More precisely, we give conditions that imply properness of such surfaces and prove the existence of fixed size one-sided regular neighborhoods for certain constant mean curvature hypersurfaces in cer…
Paper explores RKHS properties for derivative and integral operators.
problem Establishing sufficient conditions for reproducing property in RKHS.
method Establishing reproducing property for combinations of composition operators.
result Provides framework for regularized learning algorithms involving function values, gradients, or operators.
The paper shows continuity of singular times and limit sets for certain mean-convex flows.
problem Understanding the behavior of singularities in mean curvature flow.
method Employing an Angenent-like neck-pinching argument and using Andrews' α-non-collapsed condition and Colding-Minicozzi uniqueness of tangent flows.
result Shows continuity of first singular time and limit set with respect to initial data.
Let M be a quasi-Fuchsian three-manifold that contains a closed incompressible surface with principal curvatures within the range of the unit interval, for a prescribed function H (with mild conditions) on M, we construct a closed incompressible surface with mean curvature H . A direct application is the existe…
Proves multiplicity one conjecture for surface flows, with applications in flow regularity.
problem Proving multiplicity one for mean curvature flows of surfaces.
method Analyzing blow-up limits and using level set flow properties.
result Blow-up limits of mean curvature flows have multiplicity one.
We give necessary conditions on complete embedded \cmc surfaces with three or four ends subject to reflection symmetries. The respective submoduli spaces are two-dimensional varieties in the moduli spaces of general \cmc surfaces. We characterize fundamental domains of our \cmc surfaces by associated great circle polyg…
DTE uses tree leaf means to embed data, balancing accuracy and speed.
problem High variance in decision tree splits and computational inefficiency of ensembles.
method DTE constructs an interpretable feature representation using leaf means of a trained tree.
result DTE strikes a balance between accuracy and computational efficiency, outperforming ensembles.
A new method compresses conditional distributions of labelled data.
problem No existing method directly compresses the conditional distribution of labelled data.
method Introduce Average Maximum Conditional Mean Discrepancy (AMCMD), derive a closed form estimator, and extend Kernel Herding (KH) to Average Conditional Kernel Herding (ACKH).
result Directly compressing conditional distributions outperforms joint distribution compression and greedy selection.
This paper provides a dictionary of closed-form kernel mean embeddings.
problem Challenges in deriving closed-form kernel mean embeddings.
method Comprehensive dictionary and practical tools for deriving new embeddings.
result Provides a Python library with minimal implementations of embeddings.
New algorithm quantifies uncertainty in regression models for complex data types.
problem Uncertainty quantification in regression models for complex data types.
method Model-free uncertainty quantification algorithm based on conditional depth measures and kernel mean embeddings.
result Provides faster convergence rates and non-asymptotic guarantees for prediction regions.