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ARXIV:2604.13414 · ENSEMBLE METHODS · SUBMITTED 16 APR · 18:20 UTC · FRESHNESS STALE
ARXIV:2604.13414ENSEMBLE METHODSSUBMITTED 16 APR · 18:20 UTCFRESHNESS STALEIbne Farabi Shihab · Sanjeda Akter · Anuj Sharma · arXiv
A theoretical framework and adaptive algorithm for minimax optimal majority-vote ensembles in Markov-dependent data, improving time-series and RL applications.
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Pain A theoretical framework and adaptive algorithm for minimax optimal majority-vote ensembles in Markov-dependent data, improving time-series and RL applications.
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A theoretical framework and adaptive algorithm for minimax optimal majority-vote ensembles in Markov-dependent data, improving time-series and RL applications. When training data exhibits Markov dependence, as in time-series forecasting, reinforcement learning (RL) replay buffers,…
Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. When training data exhibits Markov dependence, as in time-series forecasting, reinforcement learning (RL) replay buffers, and spatial grids, this classical guarantee…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. Code availability is flagged in the production record; the public repository…
Ensemble Methods moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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A theoretical framework and adaptive algorithm for minimax optimal majority-vote ensembles in Markov-dependent data, improving time-series and RL applications.
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10.48550/arXiv.2604.13414A theoretical framework and adaptive algorithm for minimax optimal majority-vote ensembles in Markov-dependent data, improving time-series and RL applications.
Abstract
Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. When training data exhibits Markov dependence, as in time-series forecasting, reinforcement learning (RL) replay buffers, and spatial grids, this classical guarantee degrades in ways that existing theory does not fully quantify. We provide a minimax characterization of this phenomenon for discrete classification in a fixed-dimensional Markov setting, together with an adaptive algorithm that matches the rate on a graph-regular subclass. We first establish an information-theoretic lower bound for stationary, reversible, geometrically ergodic chains in fixed ambient dimension, showing that no measurable estimator can achieve excess classification risk better than $Ω(\sqrt{\Tmix/n})$. We then prove that, on the AR(1) witness subclass underlying the lower-bound construction, dependence-agnostic uniform bagging is provably suboptimal with excess risk bounded below by $Ω(\Tmix/\sqrt{n})$, exhibiting a $\sqrt{\Tmix}$ algorithmic gap. Finally, we propose \emph{adaptive spectral routing}, which partitions the training data via the empirical Fiedler eigenvector of a dependency graph and achieves the minimax rate $\mathcal{O}(\sqrt{\Tmix/n})$ up to a lower-order geometric cut term on a graph-regular subclass, without knowledge of $\Tmix$. Experiments on synthetic Markov chains, 2D spatial grids, the 128-dataset UCR archive, and Atari DQN ensembles validate the theoretical predictions. Consequences for deep RL target variance, scalability via Nyström approximation, and bounded non-stationarity are developed as supporting material in the appendix.
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A theoretical framework and adaptive algorithm for minimax optimal majority-vote ensembles in Markov-dependent data, improving time-series and RL applications. When training data exhibits Markov dependence, as in time-series forecasting, reinforcement learning (RL) replay buffer...
METHOD
Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. When training data exhibits Markov dependence, as in time-series forecasting, reinforcement learning (RL) replay buffers, and spatial grids, this classical guar...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. Code availability is flagged in the production record; the public repository link sti...
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Ensemble Methods moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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A theoretical framework and adaptive algorithm for minimax optimal majority-vote ensembles in Markov-dependent data, improving time-series and RL applications. When training data exhibits Markov dependence, as in time-series forecasting, reinforcement learning (RL) replay buffers, and spatial grids, this classical guarantee degrades in ways that existing theory does not fully quantify.
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Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. When training data exhibits Markov dependence, as in time-series forecasting, reinforcement learning (RL) replay buffers, and spatial grids, this classical guarantee degrades in ways that existing theory does not fully quantify.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. Code availability is flagged in the production record; the public repository link still needs proof alignment.
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Ensemble Methods moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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A theoretical framework and adaptive algorithm for minimax optimal majority-vote ensembles in Markov-dependent data, improving time-series and RL applications.
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