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This paper introduces a normal-fan geometry for finite-horizon adversarial MDPs with fixed transitions, developing a face-crossing price that separates consequential from harmless non-stationarity. It shows that dynamic regret decomposes into intrinsic priced face motion plus within-face selection error.
This paper proves two necessary conditions for optimal inference in a mesh of sovereign agents with irregular, non-stationary observations: an adaptive timescale and gap-dependence, which are satisfied only by liquid (continuous-time) networks.
The paper proposes RAVEN, a Mixture-of-Experts framework that adaptively determines temporal context windows for each input sample to handle non-stationary financial time series. It achieves state-of-the-art performance on financial and traffic benchmarks.
This paper studies piecewise-stationary low-rank linear contextual bandits, proposes the SPSC algorithm that achieves dynamic regret scaling with the intrinsic rank instead of the ambient dimension, and characterizes the identification boundary for subspace recovery under scalar feedback.
The paper introduces TTCD, a novel framework for temporal causal discovery from non-stationary time series data using transformer-based feature learning and reconstruction-guided signal distillation.