A new distribution adapts to local data density.
problem Fitting complex probability distributions over manifolds.
method Developed a locally adaptive normal distribution (LAND) using a non-parametric metric.
result LAND generalizes the normal distribution to manifold settings.
Study improves BN TTA under distribution shift using higher-order asymptotics.
problem Improving BN TTA for changing data distributions.
method Integrates Edgeworth expansion and saddlepoint approximation with one-step M-estimation.
result Derives optimal weighting parameter for minimized mean-squared error.
Adapts Hölder smoothness with normalized gradients.
problem Improving smoothness adaptation methods.
method Black-box adaptation of Levy's method using normalized gradients.
result Bound depends on local Hölder smoothness.
Proposes a new regression method using Lp-norms for non-Gaussian noise.
problem Non-Gaussian noise in residuals affects the performance of local least squares regression.
method Introduces local polynomial Lp-norm regression, replacing weighted least squares with weighted Lp-norm estimation. result Demonstrates superior performance over local least squares in one-dimensional data and higher dimensions.
Adapts flow matching for MCMC to improve sampling efficiency.
problem Improving sampling efficiency in MCMC for complex distributions.
method Combines Markov chain and CNFs to learn a path between distributions.
result Achieves similar performance to state-of-the-art methods but with lower computational cost.
Combines local and global samplers for efficient sampling.
problem Limited learning accuracy in regions with little data.
method Explore-Exploit Markov chain Monte Carlo strategy (Ex2MCMC) result Proves V-uniform geometric ergodicity of Ex2MCMC. EvoMSN tackles time series forecasting under distribution shifts by evolving multi-scale normalization.
problem Accurate long-term time series forecasting under complex distribution shifts.
method EvoMSN framework with multi-scale statistics prediction and adaptive ensembling for collaborative updating.
result Improves forecasting performance of five mainstream methods on benchmark datasets.
A new method improves posterior approximation for complex distributions.
problem Difficulty in capturing multimodal and heavy-tailed posteriors with standard normalizing flows.
method StiCTAF: stick-breaking mixture base with component-wise tail adaptation.
result Improved tail recovery and better mode coverage compared to benchmarks.
AdaFlow adapts DNN density estimators to new distributions for anomaly detection and cross-domain translation.
problem Unsupervised anomaly detection and cross-domain translation with limited paired data.
method AdaFlow combines Normalizing Flows and Adaptive Batch-Normalizations for domain adaptation.
result AdaFlow efficiently adapts to new distributions with minimal computational resources.
A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the purpose of sampling from a target distribution with strongly nonlinear support. The algorithm embeds the trajectory of the Markov chain into a reproducing kernel Hilbert space (RKHS), such that the feature space covariance of the samples informs the…
Local normal forms for symmetrical contact structures on 3-manifolds.
problem Understanding symmetrical contact structures on 3-manifolds.
method Determining local normal forms for pairs of transverse contact distributions with symmetries.
result Orientable Anosov flows can be globally represented by intersecting contact distributions with maximal symmetries.
Normalizing Flows improve prediction interval efficiency in CP.
problem Inefficient prediction intervals in CP due to non-uniform error distribution.
method Train a Normalizing Flow to optimize the distance metric between errors and inputs.
result Optimized prediction intervals are more efficient and valid.
New method for robust distribution alignment using log-likelihood ratio and normalizing flows.
problem Distribution alignment challenges in deep learning.
method Log-likelihood ratio statistic and normalizing flows.
result Minimizing the proposed objective yields robust domain alignment.
Paper introduces Normalized Wasserstein measure for better handling of imbalanced mixture distributions.
problem Wasserstein distance fails for mixture distributions with imbalanced proportions.
method Introduce mixture proportions as optimization variables to normalize Wasserstein formulation.
result Normalized Wasserstein measure leads to significant performance gains for mixture distributions.
Local asymptotic minimax risk bounds in a locally asymptotically mixture of normal family of distributions have been investigated under asymmetric loss functions and the asymptotic distribution of the optimal estimator that attains the bound has been obtained.
Adapts to high dimensions for estimating conditional moments.
problem Estimation and inference in high-dimensional settings with unknown intrinsic dimension.
method Sub-sampled k-NN Z-estimator, adaptive data-driven sub-sampling. result Estimation error of n−1/(d+2) and asymptotic normality with n1/(d+2) rate. A new probabilistic polygonal curve representation using Gaussian Mixture Models.
problem Capturing curves with uncertainty in both tangent and normal directions.
method Probabilistic polygonal approximation with Gaussian Mixture Model (GMM).
result The GMM accurately captures the local geometry and uncertainty of curves.
Proves a theorem for normal distributions on manifolds with boundary.
problem Normal distributions on manifolds with boundary require a new approach to integration.
method Introduces neat integral manifolds with boundary and conditions for integrability.
result Conditions for integrability expressed in terms of adapted collars and integrability on interior and boundary.
A new framework enhances generative modeling by learning local flows over complex manifolds.
problem Limited expressivity of current normalizing flows for low-dimensional manifolds.
method Vector quantized local normalizing flows (VQ-Flows) using a VQ-AE atlas and conditional flows.
result Enhanced modeling of complex data distributions over manifolds.
A new method uses normalizing flows for gradual domain adaptation.
problem Difficulty in domain adaptation when source and target domains have a large gap.
method Proposes using normalizing flows to learn a transformation from target to Gaussian mixture distribution.
result Improves classification performance and mitigates the problem of gradual self-training failure.
cKAM improves adaptive sampling by incorporating a cyclical stepsize scheme.
problem Adaptive Metropolis algorithms can get stuck in local modes.
method cKAM uses a cyclical stepsize scheme to encourage exploration and escape from local modes.
result cKAM successfully escapes local modes and converges to the true posterior distribution.
In this paper we describe the local Ricci and Bianchi identities for an h-normal N-linear connection DΓ(N) on the dual 1-jet space J^{1*}(T,M). To reach this aim, we firstly give the expressions of the local distinguished (d-) adapted components of torsion and curvature tensors produced by DΓ(N), and then we analyze th…
The paper addresses statistical inference issues in adaptive experiments.
problem Statistical inference problems in adaptive experiments.
method Explains and fixes statistical inference issues in adaptive experiments using various methods.
result Various methods to stabilize inferences and recover asymptotic normality.
Extends CMA-ES for discrete variables using multivariate binomial distributions.
problem Optimization of discrete variables with non-convex, non-continuous functions.
method Adapts CMA-ES to multivariate binomial distributions, modeling interactions efficiently.
result Created a version of CMA-ES for discrete variables, capable of higher-order interactions.
Adaptive batch sizes improve local gradient methods in distributed training.
problem Communication bottlenecks in distributed deep learning.
method Adaptive batch size strategies for local gradient methods.
result Adaptive batch sizes reduce minibatch gradient variance and improve training efficiency.
Study examines preservation of curvature-adaptedness during mean curvature flow.
problem Preservation of curvature-adaptedness during mean curvature flow.
method Investigates curvature-adaptedness in locally symmetric spaces.
result Curvature-adaptedness is preserved along mean curvature flow.
Zero-shot anomaly detection method using batch normalization.
problem Adapting anomaly detectors to new normal data distributions without training data.
method Adaptive Centered Representations (ACR) with batch normalization.
result First zero-shot AD results for tabular data and image data.
New method learns domain-invariant local feature patterns for unsupervised domain adaptation.
problem Performance degradation due to domain-shift in unsupervised domain adaptation.
method Jointly learns domain-invariant local feature patterns and holistic feature distributions.
result Superior performance on benchmark datasets compared to state-of-the-art methods.
Test the robustness of quantum-enhanced phase estimation under various noise conditions.
problem Evaluate the robustness of quantum-enhanced adaptive phase estimation (QEAPE) in noisy conditions.
method Simulated QEAPE under four phase-noise models and compared resource usage of evolutionary and Bayesian control policies.
result Demonstrated the effectiveness of both evolutionary and Bayesian control policies in noisy conditions.
Proposes a new random forest weighted local Fréchet regression method.
problem Complex metric space valued responses and curse of dimensionality in Fréchet regression.
method Locally adaptive kernel generated by random forests for local average and local linear Fréchet regression.
result Significantly improves existing Fréchet regression methods with theoretical guarantees.
This research explains how extreme events can emerge from adaptive systems.
problem Extreme events in complex systems.
method Examines self-similar bursts of activity in adaptive systems.
result Locally minimising fluctuations can increase system sensitivity to unpredictable perturbations.
Study shows annealing with adaptive schedule reduces mode collapse in NFs for parameter estimation.
problem Mode collapse in normalizing flows for multimodal distributions.
method Annealing with an adaptive schedule based on effective sample size (ESS).
result Our approach reduces mode collapse and converges marginal likelihood faster than MCMC methods.
Adaptive feature normalization improves model robustness to extraneous variables.
problem Degrading model performance due to extraneous variables in deep learning.
method Adaptive feature normalization using instance normalization instead of batch normalization.
result Adaptive normalization leads to significant performance gains across different datasets and architectures.
Develops a new framework for estimating joint probability distributions.
problem Estimating joint probability distributions from large sample sizes.
method Tensor product reproducing kernel Hilbert spaces (RKHS) with normalized and positive model.
result Fast computation and applicability to prediction and classification problems.
ARM algorithm improves normalizing constant estimation efficiency.
problem Estimating normalizing constants in probabilistic models.
method Adaptive Resample-Move algorithm with optimal particle expansion.
result ARM reduces variance and computational resources compared to fixed particle methods.
Normalizing flows fail to detect OOD data due to learning local pixel correlations.
problem Detecting out-of-distribution data in machine learning systems.
method Investigated why normalizing flows fail to distinguish between in- and out-of-distribution data, and modified flow architecture to improve OOD detection.
result Modifying flow architecture can improve OOD detection by biasing the flow towards learning semantic structure of the target data.
This paper explores optimization methods for deep learning.
problem How to effectively train neural networks.
method Discusses gradient explosion/vanishing, initialization, normalization, SGD, adaptive gradient methods, distributed methods, and global issues.
result Provides theoretical insights and practical solutions for neural network training.
Improves multi-objective learning by adapting to local subintervals.
problem Learning a predictor satisfying multiple objectives in an online, changing data setting.
method Adapting an existing multi-objective learning method with an adaptive online algorithm.
result Improves predictions over subgroups and remains robust under distribution shift.
Copula-based normalizing flows improve flexibility and stability for heavy-tailed data.
problem Limited expressive power of vanilla normalizing flows.
method Generalize base distribution to copula for more accurate representation of target distribution.
result Copula-based normalizing flows improve flexibility, stability, and effectiveness for heavy-tailed data.
The aim of this paper is to describe the local Bianchi identities for an h-normal Γ-linear connection of Cartan type ∇Γ on the first-order jet space J1(R,M). In this direction, we present the local expressions of the adapted components of the torsion and curvature d-tensors produced by ∇Γ and we gi…
Model for equity trading with asynchronous price updates converging to a stationary return distribution.
problem Equity trading dynamics with asynchronous price updates and varying number of participants.
method Modeling agents' adaptive strategies and using numerical simulations to analyze returns.
result The model converges to a stationary return distribution, with mean returns influenced by adaptive mechanisms and agent interactions.
LSCI provides locally adaptive prediction sets for operator models with tighter coverage.
problem Generating robust, calibrated uncertainty quantification for operator models.
method Local Sliced Conformal Inference (LSCI) for operator models.
result LSCI yields tighter prediction sets with stronger adaptivity compared to conformal baselines.
Proposes online debiasing estimators for adaptive linear regression.
problem Adaptive data collection leads to non-normal asymptotic behavior in simple methods.
method Online debiasing estimators that correct distributional anomalies.
result Asymptotic normality and minimax lower bound for proposed estimators.
Paper improves normalizing flows to better capture distribution tails.
problem Difficult to learn tail behavior of distributions.
method Develops a new type of flows using flexible base distributions and data-driven linear layers.
result Improves accuracy, especially on distribution tails, and generates heavy-tailed data.
Locally adaptive federated learning improves convergence in distributed machine learning.
problem Balancing local updates in federated learning leads to slow convergence.
method Locally adaptive federated learning algorithms that use uncoordinated stepsizes based on local geometric information.
result Locally adaptive methods can be particularly efficient in overparameterized settings and outperform standard federated algorithms.
Yau's Affine Normal Descent optimizes smooth unconstrained problems with geometrically adapted directions.
problem Optimizing smooth unconstrained problems with geometrically adapted directions.
method Yau's Affine Normal Descent (YAND) uses the equi-affine normal of level-set hypersurfaces as search directions.
result YAND converges globally under standard smoothness assumptions and locally quadratically near nondegenerate minimizers.
LDAO addresses imbalanced regression by learning local distribution structures.
problem Imbalanced regression with sparse target regions difficult for models.
method LDAO learns local distribution structures, models and samples from each, then merges.
result LDAO outperforms state-of-the-art methods on 45 imbalanced datasets.
Adapted metrics found on complex manifolds.
problem Finding metrics suitable for complex manifolds.
method Characterizing adapted metrics as critical points of a functional.
result Gauduchon metric is adapted on locally conformally product manifolds.