arXiv Machine Learning
Free-Probability Kernels for Zero-Rollout Hyperparameter Selection in Reservoir Computing
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arXiv Machine Learning
4d ago
RC Prediction: A Python Package for Parameter-Aware Reservoir Computing
The article introduces rc_prediction, a Python package that implements parameter‑aware reservoir computing (PARC) for modeling nonlinear dynamical systems when governing equations are unknown. PARC trains a reservoir computer on time‑series data collected at multiple parameter values, then uses the trained model in closed‑loop to generate trajectories at unseen parameter settings, enabling parameter scans and autonomous prediction. The package offers hyperparameter optimization, optional GPU acceleration via PyTorch, and demonstrates its use on three benchmark systems, including predicting critical transitions and system collapse from pre‑critical data.
arXiv Machine Learning
Aug 4
Stochastic Sequential Search in Very-High-Dimensional Feature Selection
arXiv:2608. 01502v1 Announce Type: new Abstract: Sequential subset search -- forward selection with floating backtracking and its descendants -- remains the quality reference in feature selection, but every member of the family sweeps the full pool of remaining candidate features at each step, which excludes it from very-high-dimensional problems; there, only individual-feature ranking remains practical, and it models feature interplay weakly or not at all.
arXiv AI
Oct 27
EvoBench: An Evolving, Diagnostic Benchmark Framework for Environmental Modeling
EvoBench is a diagnostic benchmark framework designed to reveal where and when machine‑learning models for environmental variables fail, rather than relying on a single average accuracy score. It tracks model performance over time and across spatial extrapolation, highlighting issues such as non‑stationarity, poor performance at unmonitored sites, and under‑prediction of rare events like floods. In a case study on daily streamflow prediction across 9,000 U.S. basins, EvoBench shows that no model dominates all scenarios and that targeted fixes can mitigate identified weaknesses.
arXiv AI
Sep 25
Revalidation Beats Stateful Routing for Scientific Surrogates Under Distribution Shift
The study introduces RegimeShift‑Surrogates, a streaming benchmark that tests surrogate models across eight tasks and multiple regimes. It compares revalidation—choosing the model with lowest current‑window validation loss—to stateful adaptive controllers and finds that revalidation consistently outperforms stateful methods, achieving lower mean log regret in most task‑scenario combinations. The results suggest that fresh validation evidence is more valuable than carrying over past evidence when dealing with distribution shifts.
arXiv Machine Learning
Oct 9
Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting
arXiv:2610. 12232v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods for time-series forecasting update a deployed model, or a small adapter around it, from incoming ground truth.
arXiv AI
Oct 27
Dynamic Multi-Step Decoding for Time-Series Foundation Models
The paper introduces a dynamic multi‑step decoding approach for time‑series foundation models, arguing that the best decoding strategy varies by instance rather than being fixed. A lightweight selector network ranks candidate rollout schedules using the backbone’s hidden states, trained on historical windows and evaluated via dynamic programming to keep complexity quadratic. At deployment, multiple selectors vote to choose a schedule, defaulting to one‑shot decoding when they disagree, and the method shows statistically significant improvements in point‑forecast accuracy on the fev‑bench benchmark with frozen Chronos‑2 and Moirai‑1.1‑R‑small backbones.