4 papers
Temporal Functional Circuits: From Spline Plots to Faithful Explanations in KAN Forecasting
Naveen Mysore
Unlike MLPs, Kolmogorov-Arnold Networks (KANs) expose explicit learnable edge functions on every connection, enabling mechanistic explanation in time-series forecasting. This paper…
Prediction-Based Markov Violation Scores for Detecting Non-Markovian Observations in Reinforcement Learning
Naveen Mysore
Reinforcement learning algorithms assume that observations satisfy the Markov property, yet real-world sensors frequently violate this assumption through correlated noise, latency,…
DecompKAN: Decomposed Patch-KAN for Long-Term Time Series Forecasting
Naveen Mysore
Accurate time series forecasting in scientific domains such as climate modeling, physiological monitoring, and energy systems benefits from both competitive predictions and model t…
Quantifying First-Order Markov Violations in Noisy Reinforcement Learning: A Causal Discovery Approach
Naveen Mysore
Reinforcement learning (RL) methods frequently assume that each new observation completely reflects the environment's state, thereby guaranteeing Markovian (one-step) transitions.…