arXiv research

A new framework reduces inconsistencies in chaotic surrogate modeling.

New approach separates VAE and GP for better molecular optimisation.

A new method for multi-expert learning-to-defer avoids optimization issues.

Compositional diffusion models simulate coupled PDEs efficiently.

Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations. Inspired by the observation that CNN-learned features are naturally decoupled with the norm of features corresponding to the intra-class variation and the angle correspond…

We investigate finite-time decoupled convergence in nonlinear two-time-scale stochastic approximation.

Spectral decoupling improves neural network generalization in medical imaging.

Decoupled GCN is shown to be equivalent to label propagation.

A hybrid model combines diffusion and neural operator methods for stress prediction in hyperelastic materials.

Improved Kalman filtering with hierarchical variational approach.

We investigate probabilistic decoupling of labels supplied for training, from the underlying classes for prediction. Decoupling enables an inference scheme general enough to implement many classification problems, including supervised, semi-supervised, positive-unlabelled, noisy-label and suggests a general solution to…

The paper proposes a decoupled approach to efficiently estimate CoVaR, a measure of systemic financial risk.

This paper investigates the effectiveness of decoupled weight decay at the start of training.

Clarifies method of phase synchronization for decoupling linear differential equations.

Kahler geometry explains decoupling of Kerr perturbations.

AdaDEM decouples EM into two parts to improve class overlap and uncertainty.

Recent variants improve knowledge distillation performance.

New algorithms for interpreting complex multivariate functions.

New method combines spectral and sparse methods for Gaussian processes.

We establish decoupled functional CLTs for two-time-scale stochastic approximation.

New bounds show polyhedral surrogates are optimal for generalization.

POLAR learns efficient data acquisition policies using pretrained belief representations.

Current reinforcement learning (RL) methods can successfully learn single tasks but often generalize poorly to modest perturbations in task domain or training procedure. In this work, we present a decoupled learning strategy for RL that creates a shared representation space where knowledge can be robustly transferred. …

Stylized facts can be regarded as constraints for any modeling attempt of price dynamics on a financial market, in that an empirically reasonable model has to reproduce these stylized facts at least qualitatively. The dynamics of market prices is modeled on a macro-level as the result of the dynamic coupling of two dyn…

Backpropagation algorithm is indispensable for the training of feedforward neural networks. It requires propagating error gradients sequentially from the output layer all the way back to the input layer. The backward locking in backpropagation algorithm constrains us from updating network layers in parallel and fully l…

A new model decouples global and local image representations without supervision.

New equations simplify gauge-theoretic Khovanov homology solutions.

Decoupled PFNs improve sequential decision-making by separating epistemic and aleatoric uncertainties.

Novel Adam-family method with decoupled weight decay for training neural networks.

DAPS++ improves diffusion-based image restoration by decoupling prior and likelihood.

New method uses diffusion models for Bayesian inverse problems.

We investigate the convergence and stability properties of the decoupled extended Kalman filter learning algorithm (DEKF) within the long-short term memory network (LSTM) based online learning framework. For this purpose, we model DEKF as a perturbed extended Kalman filter and derive sufficient conditions for its stabi…

DAPS++ improves diffusion-based image restoration by decoupling prior and likelihood.

FTPL policy achieves best-of-both-worlds regret in decoupled bandits with reduced computational cost.

New method detects heuristics in complex game strategies.

We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators. Unlike traditional approaches to surrogate modeling, our surrogates retain the interpretable structure and control flow of the reference simulator. Our surrogates target stochastic simul…

Randomizing the Fourier-transform (FT) phases of temporal-spatial data generates surrogates that approximate examples from the data-generating distribution. We propose such FT surrogates as a novel tool to augment and analyze training of neural networks and explore the approach in the example of sleep-stage classificat…

We formalize and study the natural approach of designing convex surrogate loss functions via embeddings, for problems such as classification, ranking, or structured prediction. In this approach, one embeds each of the finitely many predictions (e.g.\ rankings) as a point in $\mathbb{R}^d$, assigns the original loss val…

The paper proposes a scalable framework for uncertainty quantification and propagation in surrogate-based Bayesian inference.

Paper proposes hybrid modeling to improve surrogate accuracy using multiple data sources.

Study compares constrained and decoupled moduli spaces of manifolds with particles and discs.

Improving predictive understanding of Earth system variability and change requires data-model integration. Efficient data-model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables i…

The paper proposes a method to assess surrogate heterogeneity in non-randomized data.

Unsupervised representation learning via generative modeling is a staple to many computer vision applications in the absence of labeled data. Variational Autoencoders (VAEs) are powerful generative models that learn representations useful for data generation. However, due to inherent challenges in the training objectiv…

The paper extends macroscopic market making to stochastic games, revealing properties and solving equations.

Unified q-learning for mean-field jump-diffusion models with unobservable population distribution.

Thanks to their versatility, ease of deployment and high-performance, surrogate models have become staple tools in the arsenal of uncertainty quantification (UQ). From local interpolants to global spectral decompositions, surrogates are characterised by their ability to efficiently emulate complex computational models …

This paper develops efficient surrogate models for optimization of complex dynamical systems.