Machine Learning
Batched Robust iHMM for Outlier-Prone Streaming Data
This paper introduces Batched Robust iHMM (BR-iHMM), a novel approach for online infinite hidden Markov models that addresses data outliers and model misspecification. BR-iHMM utilizes generalized Bayesian inference and posterior influence functions to ensure robustness while offering tunable parame…
Synthetic Augmentation: Bias-Variance Trade-offs in Financial ML
Synthetic augmentation in financial machine learning, while addressing data scarcity, introduces a structural bias-variance trade-off. This trade-off is formalized as a modification of the effective training distribution. The core insight demonstrates that while synthetic data can reduce estimation …
Masked Autoencoders Show Untapped Potential for Downhole Metric Prediction
Predicting downhole metrics from surface drilling data is challenging due to limited labeled downhole measurements. While current approaches utilize neural networks like ANNs and LSTMs, Masked Autoencoder Foundation Models (MAEFMs) are underexplored despite their efficacy in time-series modeling. MA…
One-Shot Learning for Complex Nonlinear Oscillators
MEv-SINDy (Multi-frequency Evolutionary Sparse Identification of Nonlinear Dynamics) identifies global frequency-response curves from a single excitation time history by learning governing equations. It utilizes the Generalized Harmonic Balance (GHB) method to decompose complex forced responses into…
Adaptive Canonicalization for Continuous Equivariant Learning
The authors introduce adaptive canonicalization, a framework where the input's standard form is dynamically determined to maximize network predictive confidence. This approach eliminates the discontinuities associated with fixed canonicalization, ensuring continuity and universal approximation while…
Improved Regret Bounds for Contextual Bandits with Latent State Dynamics
This paper introduces a novel approach to contextual bandits where contexts and rewards are governed by a hidden Markov chain. Unlike previous methods, this work directly models dependencies on hidden states and achieves stronger, high-probability regret bounds. The key innovation lies in a fully ad…
Modeling Nonreciprocal Pairwise Comparisons for Improved Decision Analysis
This paper introduces an additive model for analyzing pairwise comparisons where nonreciprocity is not seen as a defect but as a combination of genuine scale variation and random perturbations. It allows for the estimation of noise levels, assessment of scale variation, and assignment of probabiliti…
Generative Path-Law Jump-Diffusion for Discontinuous Stochastic Trajectories
This paper introduces the Anticipatory Neural Jump-Diffusion (ANJD) flow, a novel generative framework for synthesizing forward-looking, càdlàg stochastic trajectories. The methodology addresses challenges in modeling structural breaks and non-autonomous dynamics by framing path synthesis as a seque…
Optimal Active Learning for Linear System Identification
This paper introduces an active learning algorithm for linear system identification that utilizes optimal centered noise excitation. The method, based on ordinary least squares and semidefinite programming, achieves minimal sample complexity. The authors establish tight lower and upper bounds for sa…