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.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,694 papers · 148 categories
Trend · papers per month
48 results for “adaptive normalization”
New algorithm tackles nonconvex machine learning problems with adaptive normalization and independent sampling.
problem Nonconvex machine learning problems with generalized-smoothness.
method Adaptive gradient normalization, independent sampling, and gradient clipping.
result Achieves an O(ε^(-4)) sample complexity for fast convergence.
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.
We propose a novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks. We aim to adapt to both domains by specializing batch normalization layers in convolutional neural networks while allowing them to share all other model parameters, which is realized by a tw…
Paper proposes a debiased estimator for adaptive linear regression.
problem Non-normal asymptotic behavior of OLS estimator in adaptive linear regression.
method Adaptive linear estimating equations to construct debiased estimator.
result Established asymptotic normality of the debiased estimator.
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networkscs.LG
EDAIN layer normalizes time series data for neural networks, improving model performance.
problem Irregularities in time series data degrade model performance in neural networks.
method EDAIN layer learns adaptive normalization parameters during end-to-end training.
result EDAIN layer outperforms conventional normalization methods and adaptive layers.
Adaptive networks improve model robustness through conditional normalization.
problem Limited robustness of adversarial-trained networks due to network capacity and training samples.
method Proposes a conditional normalization module to adapt networks during adversarial training.
result Adaptive networks outperform both clean validation accuracy and robustness compared to non-adaptive counterparts.
We address challenges in estimating parameters from adaptively collected data.
problem Estimating parameters from data collected adaptively leads to non-normal asymptotic distributions.
method We develop semi-parametric estimators that account for adaptivity in data collection.
result Our estimators are asymptotically normal under certain conditions.
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.
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.
We tackle unsupervised anomaly detection (UAD), a problem of detecting data that significantly differ from normal data. UAD is typically solved by using density estimation. Recently, deep neural network (DNN)-based density estimators, such as Normalizing Flows, have been attracting attention. However, one of their draw…
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.
Efficient inference for adaptive data with directional stability condition.
problem Efficient inference on scalar targets after adaptive data collection.
method Introduces directional stability, a weaker condition than i.i.d. data, and shows asymptotic normality and efficiency of estimators.
result Estimators remain asymptotically normal and semiparametrically efficient under directional stability.
The paper develops a method for self-normalized inference in adaptive experiments.
problem Adaptive experiments require a fixed horizon for ATE estimation, but propensities can change.
method The method uses self-normalized martingale limit theory to estimate ATE.
result The Studentized statistic is asymptotically N(0,1) at the prespecified horizon.
The paper constructs hypersurfaces in symmetric space products.
problem Creating curvature-adapted hypersurfaces in symmetric space products.
method Constructing hypersurfaces using the product of symmetric spaces.
result Obtained many examples of curvature-adapted hypersurfaces.
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.
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.
We formulate the problem of neural network optimization as Bayesian filtering, where the observations are the backpropagated gradients. While neural network optimization has previously been studied using natural gradient methods which are closely related to Bayesian inference, they were unable to recover standard optim…
The multivariate normal density is a monotonic function of the distance to the mean, and its ellipsoidal shape is due to the underlying Euclidean metric. We suggest to replace this metric with a locally adaptive, smoothly changing (Riemannian) metric that favors regions of high local density. The resulting locally adap…
LAWN normalizes logits to improve deep network adaptability and generalization.
problem Large logits and weights lead to overfitting in deep networks.
method Logit Attenuating Weight Normalization (LAWN) constrains weight norms in the final sub-network.
result LAWN improves generalization and adaptability of deep networks.
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.
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.
The estimation of normalizing constants is a fundamental step in probabilistic model comparison. Sequential Monte Carlo methods may be used for this task and have the advantage of being inherently parallelizable. However, the standard choice of using a fixed number of particles at each iteration is suboptimal because s…
GAS-Norm improves deep learning time series forecasting in non-stationary settings.
problem Deep learning models struggle with non-stationary time series data.
method Combines GAS model for adaptive normalization with deep neural networks.
result Improves deep learning performance in 21 out of 25 settings.
Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data. In this work, we show that the presence of such variables can degrade the performance of deep-learning models. We study three datasets where there is a strong influence of known extraneo…
In this article, we first describe a normal form of real-analytic, Levi-nondegenerate submanifolds of of codimension d 1 under the action of formal biholomorphisms, that is, of perturbations of Levi-nondegenerate hyperquadrics. We give a sufficient condition on the formal normal form that ensures that the n…
Photometric stereo is a method for estimating the normal vectors of an object from images of the object under varying lighting conditions. Motivated by several recent works that extend photometric stereo to more general objects and lighting conditions, we study a new robust approach to photometric stereo that utilizes …
Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc. In this work, we focus on mixture distributions that arise naturally in several application domains where the data contains different sub-populations. For …
This paper presents a normalization mechanism called Instance-Level Meta Normalization (ILM~Norm) to address a learning-to-normalize problem. ILM~Norm learns to predict the normalization parameters via both the feature feed-forward and the gradient back-propagation paths. ILM~Norm provides a meta normalization mechanis…
This research investigates if deep neural networks can be trained without batch normalization.
problem Training deep neural networks efficiently without batch normalization.
method Detailed study of batch normalization, comparison with other methods, and adaptation of training process.
result It is possible to train deep neural networks effectively without batch normalization.
Deep Learning (DL) models can be used to tackle time series analysis tasks with great success. However, the performance of DL models can degenerate rapidly if the data are not appropriately normalized. This issue is even more apparent when DL is used for financial time series forecasting tasks, where the non-stationary…
New Hermite approximations accelerate convergence with adaptive coordinate transformations.
problem Accelerating convergence of spectral approximations for Hermite expansions.
method Using normalizing flows for adaptive coordinate transformations and deriving error estimates.
result Error estimates for Hermite expansions under adaptive coordinate transformations.
NoFAS combines variational inference and adaptive surrogate models for efficient inference of computationally expensive models.
problem Efficient inference of parameters from data with computationally expensive models.
method Variational inference with normalizing flow and adaptive surrogate model training.
result NoFAS reduces computational cost without sacrificing inferential accuracy.
Adaptive step-size improves optimization in complex geometries.
problem Optimizing functions with non-Euclidean geometries.
method Adaptive step-size strategy for optimization algorithms.
result Guaranteed convergence for Adaptive Conditional Gradient Descent.
GraphNorm accelerates GNN training by adapting InstanceNorm, improving convergence and generalization.
problem Improving convergence and generalization of Graph Neural Networks (GNNs).
method Adapting InstanceNorm to GNNs, proposing GraphNorm with a learnable shift.
result GNNs with GraphNorm converge faster and achieve better performance on benchmarks.
Unified framework for understanding and optimizing training acceleration.
problem Challenges in optimizing training with regularization and acceleration techniques.
method Explains how AdaGrad, RMSProp, and Adam accelerate training, and derives a generalization for -regularization.
result Derives a unified mathematical framework for understanding and optimizing training acceleration.
Optimizing full likelihoods adapts loss scales and shapes for robust modeling.
problem Rigid loss functions limit model adaptability and robustness.
method Optimize full likelihoods with adjustable parameters.
result Adaptive tuning of loss scales and shapes improves model robustness.
A TTA framework improves forecasting accuracy in non-stationary time series.
problem Improving forecasting accuracy in non-stationary time series.
method Normalization-based test-time adaptation for causal timeseries forecasting and direction classification.
result Normalization-based TTA improves forecasting error in synthetic gradual drift and can even hurt in aggressive norm-only adaptation in financial markets.
FredNormer improves time series forecasting by adapting to frequency domain patterns.
problem Current normalization methods struggle with non-stationary time series due to their time-domain approach.
method FredNormer analyzes frequency components, adapts weights, and improves robustness.
result FredNormer boosts forecasting accuracy by 33.3% on ETTm2 dataset.
As all physical adaptive quantum-enhanced metrology schemes operate under noisy conditions with only partially understood noise characteristics, so a practical control policy must be robust even for unknown noise. We aim to devise a test to evaluate the robustness of AQEM policies and assess the resource used by the po…
LinUCB algorithm handles adaptive sampling biases for linear bandits.
problem Adaptive sampling introduces biases in statistical inference.
method LinUCB algorithm with stability property for linear bandits.
result LinUCB achieves asymptotic normality with Wald-type confidence sets.
Study curvature-adapted submanifolds in semi-Riemannian Lie groups.
problem Understanding curvature-adapted submanifolds in semi-Riemannian Lie groups.
method Analyzing normal Jacobi operators and shape operators in terms of Lie bracket and bi-invariant metrics.
result Established a geometric interpretation of curvature adaptation in terms of left translations.
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains. Our approach employs multiple normalization methods while learning separate affine parameters per domain. For each domain,…
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.
AdaAnn optimizes annealing for efficient probability density approximation.
problem Efficiently approximating complex probability distributions with multiple modes.
method AdaAnn is an adaptive annealing scheduler that adjusts temperature increments based on KL divergence.
result AdaAnn improves computational efficiency in variational inference and parameter estimation.
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.
Improved statistical inference for adaptive Thompson Sampling.
problem Statistical inference challenges in Thompson Sampling.
method Inflating posterior variance in Thompson Sampling.
result Asymptotically normal estimates of arm means with logarithmic regret increase.
Estimates RL data for dynamic treatment effects using GMM.
problem Estimating dynamic treatment effects from RL data with nonstationary behavior policies.
method Weighted GMM approach to stabilize variance in adaptive RL settings.
result Valid hypothesis testing and confidence regions for dynamic treatment effects.