TL;DR
It is demonstrated that performance can be significantly improved by retaining all data and using a learning model that reflects the temporal structure of the environment.
A one-step-per-layer smoother that approximates as Gaussian only each layer's affine output is derived that applies both to deterministic systems with noisy observations and to stochastic systems described by output statistics.
Temporal Domain Generalization (TDG) has emerged to address real-world streaming data with distribution shifts over time. However, existing methods are either prone to overfitting to domain-specific noise in the data space or become overly complex and less interpretable in the parameter space. To bridge these gaps, we...
Modern deep classifiers trained by gradient descent need many passes over the data to become accurate, and even then are not always well calibrated; post-hoc temperature scaling corrects calibration after training but cannot influence training-time accuracy or convergence. We propose a variational filtering algorithm f...
Prior Fitted Networks (PFNs) such as TabPFN now rival established statistical procedures across prediction and estimation tasks. A natural explanation is that PFNs have the property of statistical adaptivity, that is, they perform nearly as well as a method tailored to the true data-generating model for a heterogeneous...
Score-based generative models generate new samples by integrating a time-dependent drift that carries Gaussian noise onto the target distribution. In practice this drift is modeled by a neural network, trained on a loss integrated over time $t$ with a weighting schedule $w(t)$. Along the backward dynamics, and for mult...
Theoretical scaling laws from linear regression to multiclass classification to multiclass classification are extended, while connecting to empirical scaling laws observed in large-scale neural networks.
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