PNDMs accelerate DDPMs by treating them as differential equations on manifolds.
problem Accelerate DDPMs while maintaining sample quality.
method Propose pseudo numerical methods (PNDMs) to solve differential equations on manifolds.
result PNDMs generate higher quality images with only 50 steps compared to 1000-step DDIMs (20x speedup).
New invariants explain topological properties of pseudo-Anosov maps.
problem Understanding invariants of pseudo-Anosov maps at roots of unity.
method Numerical methods and quantum modularity conjecture.
result Descendant invariants related by Fourier transform.
The pseudo-likelihood method is one of the most popular algorithms for learning sparse binary pairwise Markov networks. In this paper, we formulate the L1 regularized pseudo-likelihood problem as a sparse multiple logistic regression problem. In this way, many insights and optimization procedures for sparse logistic…
Measurements of cosmic microwave background (CMB) anisotropy are ideal experiments for discovering the non-trivial global topology of the universe. To evaluate the CMB anisotropy in multiply-connected compact cosmological models, one needs to compute the eigenmodes of the Laplace-Beltrami operator. Using the direct bou…
A new pseudo-metric uses data depth to compare probability distributions.
problem Designing a metric between probability distributions for machine learning applications.
method Extension of univariate quantiles to multivariate spaces, using data depth and Hausdorff distance.
result The pseudo-metric is robust, factorizes translations, and has good behavior under transformations.
Learning the undirected graph structure of a Markov network from data is a problem that has received a lot of attention during the last few decades. As a result of the general applicability of the model class, a myriad of methods have been developed in parallel in several research fields. Recently, as the size of the c…
Paper establishes convergence rates for learning elliptic pseudo-differential operators.
problem Learning elliptic pseudo-differential operators in partial differential equations.
method Wavelet-Galerkin framework, structured infinite-dimensional regression problem, sparse estimator, matrix compression, nested-support strategy.
result Obtained convergence rates for the estimator and efficient Galerkin solver.
We introduce new variants of classical regression-based algorithms for optimal stopping problems based on computation of regression coefficients by Monte Carlo approximation of the corresponding L2 inner products instead of the least-squares error functional. Coupled with new proposals for simulation of the underlyi…
Gaussian processes (GPs) are flexible distributions over functions that enable high-level assumptions about unknown functions to be encoded in a parsimonious, flexible and general way. Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise when data are sufficien…
In this paper, a complete Lie symmetry analysis of the damped wave equation with time-dependent coefficients is investigated. Then the invariant solutions and the exact solutions generated from the symmetries are presented. Moreover, a Lie algebraic classifications and the optimal system are discussed. Finally, using C…
Several machine learning problems arising in natural language processing can be modeled as a sequence labeling problem. We provide Gaussian process models based on pseudo-likelihood approximation to perform sequence labeling. Gaussian processes (GPs) provide a Bayesian approach to learning in a kernel based framework. …
A neural network method estimates densities from characteristic functions.
problem Estimating fixed-horizon probability densities from empirical characteristic functions.
method Data-driven Fourier-mixture neural-network method trained in Fourier space.
result Competitive performance and clear gains on heavy-tailed targets.
New periodic solutions found in 2n-body problem, braids of pseudo-Anosov type with stretch factors as metallic ratios.
problem Periodic solutions of the 2n-body problem and their braid types.
method Analyzing braid types and stretch factors associated with pseudo-Anosov braids.
result Braids from new periodic solutions are of pseudo-Anosov type with stretch factors as metallic ratios.
A new method for safer statistical inference after predictions.
problem Statistical inference with pseudo-outcomes from machine learning predictions.
method Prediction De-Correlated Inference (PDC) framework.
result PDC consistently outperforms supervised methods and can adapt to any model.
The conjugacy problem for the pseudo-Anosov automorphisms of a compact surface is studied. To each pseudo-Anosov automorphism f, we assign an AF-algebra A(f) (an operator algebra). It is proved that the assignment is functorial, i.e. every f', conjugate to f, maps to an AF-algebra A(f'), which is stably isomorphic to A…
Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l1-penalties to either (1) parametric likelihoods, or, (2) regularized regression/pseudo-likelihoods, with the latter having the distinct advantage that they do not explicitly assume Ga…
Develops first and second-order pseudo-mirror descent methods for nonnegative function estimation.
problem Nonnegative function estimation in settings like MLE and trajectory optimization.
method First and second-order pseudo-mirror descent with pseudo-gradients and projections.
result Establishes tradeoffs and non-asymptotic bounds on model complexity.
Optimizes Thompson sampling policies using policy gradient methods.
problem Improving Thompson sampling in bandit problems.
method Applies policy gradient algorithms to optimize Thompson sampling policies.
result Direct policy search on Thompson sampling improves performance.
New method improves matrix completion accuracy, especially in noisy data.
problem Noisy matrix completion in recommendation systems and signal processing.
method Residual Spectral Matching criterion and pseudo-gradient algorithms.
result Improved numerical performance in noisy data environments.
Model analyzes cooccurrence data for recommender systems and item relevance.
problem High-dimensional cooccurrence data from online platforms.
method Shared parameter Alternating Tweedie (SA-Tweedie) model with Fisher scoring and learning rate adjustment.
result SA-Tweedie model outperforms other methods in optimizing parameters.
New method constructs Birkhoff sections for pseudo-Anosov flows with controlled complexity.
problem Constructing Birkhoff sections for pseudo-Anosov flows with specific properties.
method Uses connection between pseudo-Anosov flows and veering triangulations to explicitly construct sections with controlled complexity.
result Shows that any transitive pseudo-Anosov flow has a Birkhoff section with two boundary components.
Paper explores embedding methods for detecting pseudo-cliques in random graphs, showing limitations and potential.
problem Detecting planted pseudo-cliques in random dot product graphs.
method Adjacency Spectral Embedding (ASE) and Graph Encoder Embedding (GEE).
result These methods can localize pseudo-cliques with additional clean network data, but not without it.
We consider non-self-adjoint Schrödinger operators Δ+V where Δ is the Laplace-Beltrami operator on a Zoll manifold X and V∈C∞(X,C). We obtain asymptotic results on the pseudo-spectrum and numerical range of such operators.
Study on pseudo-Hermitian quadratic nilpotent Lie algebras with methods and classifications.
problem Characterizing and classifying pseudo-Hermitian quadratic nilpotent Lie algebras.
method Construction methods and double extension by planes.
result Complete classification of nilpotent quadratic Lie algebras and pseudo-Hermitian metrics up to dimension 8.
Method predicts RMST from censored data using pseudo-observations and super learner.
problem Estimating RMST from right-censored data.
method Ensemble algorithm combining pseudo-observations and super learner.
result Method performs well in simulations and real data applications.
SVH-PSL uses Stein Variational Gradient Descent and Hypernetworks to improve Pareto set learning for expensive MOO.
problem Fragmented surrogate models and pseudo-local optima in expensive multi-objective optimization problems.
method SVH-PSL integrates Stein Variational Gradient Descent (SVGD) with Hypernetworks to address fragmentation and pseudo-local optima.
result SVH-PSL significantly improves the quality of the learned Pareto set, offering a promising solution for expensive MOO.
New Einstein metrics found on specific Lie algebras.
problem Finding special Einstein metrics on solvable Lie algebras.
method Concrete procedure to construct Einstein pseudo-Kähler and para-Kähler metrics.
result Existence of Einstein pseudo-Riemannian metrics on specific Lie algebras.
In this paper, we derived biharmonic equations for pseudo-Riemannian submanifolds of pseudo-Riemannian manifolds which includes the biharmonic equations for submanifolds of Riemannian manifolds as a special case. As applications, we proved that a pseudo-umbilical biharmonic pseudo-Riemannian submanifold of a pseudo-Rie…
Meta Pseudo Labels boosts image classification accuracy to 90.2%.
problem Improving semi-supervised learning for image classification.
method Adapts a teacher network to generate better pseudo labels through student feedback.
result Achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet.
This paper shows how to perform likelihood inference for complex graphical models efficiently.
problem Intractable normalizing constants in fully and partially observed exponential family graphical models.
method Using a technique from Geyer (1991), the paper estimates the normalizing constant and its gradient.
result Full likelihood-based analysis is feasible and computationally efficient for these models.
Study curvatures on compact pseudo-Hermitian manifolds using special methods.
problem Prescribing Webster scalar curvatures on compact pseudo-Hermitian manifolds.
method Upper and lower solutions, perturbation theory of self-adjoint operators, CR conformal deformations.
result Described sets of Webster scalar curvature functions that can be realized.
In mathematical finance a popular approach for pricing options under some Levy model is to consider underlying that follows a Poisson jump diffusion process. As it is well known this results in a partial integro-differential equation (PIDE) that usually does not allow an analytical solution while numerical solution bri…
Extends tangle theory to include undetermined crossings in periodic structures.
problem Classical tangle theory's limitations in handling undetermined crossings.
method Introduces pseudo DP tangles, defined as liftings of pseudo motifs in the thickened torus, and analyzes them through diagrammatic methods.
result Defines equivalence for pseudo DP tangles and proves an analogue of Reidemeister theorem.
Corrects pseudo log-likelihood method issues in various applications.
problem Log-likelihood function unbounded issues in pseudo log-likelihood methods.
method Provided a counterexample and corrected algorithms in previous literature.
result Ensured well-definedness of maximum pseudo log-likelihood estimation.
New method for simplifying complex 4D shapes with boundaries.
problem Complexity reduction in 4D shape analysis.
method Introducing pseudo-trisections and pseudo-bridge trisections.
result Existence and uniqueness of pseudo-trisections established.
Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack theoretical support and cannot explain why predictions are good candidates for pseudo-labels. In this p…
Probabilistic pseudo knots model uncertain knot diagrams.
problem Modeling knots with unresolved crossings.
method Assign probabilities to undetermined crossings; define probabilistic equivalence and extend classical knot invariants.
result Capture uncertainty in physical, biological, and computational contexts.
AV-CPL uses continuous pseudo-labels for AVSR combining labeled and unlabeled data.
problem Improving AVSR performance with labeled and unlabeled data.
method Semi-supervised method using continuous pseudo-labels generated by the same AVSR model.
result Significant improvements in VSR performance on LRS3 dataset.
n this paper we define an invariant of a pair of 6 dimensional symplectic %optional manifold with vanishing 1st Chern class and its Lagrangian submanifold with vanishing Maslov index. This invariant is a function on the set of the path connected components of the bounding cochains (solution of A infinity version of Mau…
Doubly robust self-training improves semi-supervised learning by balancing labeled and pseudo-labeled data.
problem Improving semi-supervised learning performance with limited labeled data.
method Introduces doubly robust self-training, a method that combines labeled and pseudo-labeled data to balance between labeled-only and pseudo-labeled-only training.
result Demonstrates superior performance of doubly robust self-training on ImageNet and nuScenes datasets.
Sampling from posterior distributions using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations, particularly when the posterior is multi-modal as the MCMC sampler can become trapped in a local mode for a large number of iterations. In this paper, we introduce the pseudo-extended MCMC…
Method leverages data transfer for estimating CATE with KRR.
problem Leveraging findings from one study to estimate CATE in a different population.
method Overlap-adaptive transfer learning of CATE using kernel ridge regression.
result The method achieves superior efficiency and adaptability in estimating CATE.
Study of symplectic Stiefel and Grassmann manifolds with geodesics and applications.
problem Understanding symplectic bases and subspaces for data processing.
method Lie group approach to derive geodesics and retractions for pseudo-Riemannian and Riemannian metrics.
result Efficient formulas for geodesics and retractions on symplectic manifolds.
New method removes pseudo-label bias for unsupervised domain adaptation.
problem Class imbalance and distribution shift between domains.
method Implicit class-conditioned domain alignment without explicit pseudo-label optimization.
result Effective in handling within-domain class imbalance and between-domain class distribution shift.
We give a new proof that the sphere S^6 does not admit an integrable orthogonal complex structure, as in \cite{LeBrun}, following the methods from twistor theory. We present the twistor space of a pseudo-sphere S^{2n}_{2q}=SO_{2p+1,2q}/SO_{2p,2q} as a pseudo-Kähler symmetric space. We then consider orthogonal complex s…
Method calculates Z2-Thurston norm for Seifert 3-manifolds using pseudo-horizontal surfaces.
problem Computing Z2-Thurston norm for Z2-homology classes in orientable Seifert 3-manifolds. method Using pseudo-horizontal surfaces, we provide a necessary and sufficient criterion for their existence, calculate non-orientable genera, and develop an algorithm to compute the Z2-Thurston norm. result We successfully compute the Z2-Thurston norm for every Z2-homology class in orientable Seifert 3-manifolds. Locally isotropic pseudo-Riemannian manifolds are known to be locally symmetric; this result is due to Wolf. In the Riemannian setting one proof, due to Szabó, uses spectral properties of the so-called Szabó operator. In this paper we extend Szabó's method to the pseudo-Riemannian setting, obtaining results comparable …
Method estimates CATE using RCT data to handle hidden confounders.
problem Estimating CATE in the presence of hidden confounders.
method Pseudo-confounder generator and CATE model alignment.
result Method reduces bias in CATE estimation.