ViT-K uses Vision Transformers and Koopman operators to learn stable long-term spatiotemporal dynamics of coupled fluid-porous media flows from sparse data.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5verdicts
UNVERDICTED 5representative citing papers
NonZero introduces an interaction score and bandit-formalized proposal rule for local agent deviations in multi-agent MCTS, delivering a sublinear local-regret guarantee and improved sample efficiency on game benchmarks without full joint-action enumeration.
Proves √n-excess risk bounds, consistency, convergence rates, asymptotic normality, and a sufficient condition on missing probability and cluster separation for k-means under MCAR missing data.
Constructs expected-valid upper endpoints for local risk increments Pδ_v via cross-fitted ridge calibration for linear classes and componentwise application to nonsmooth loss decompositions.
PAC learning-based DR-MPC framework interpolates between robust MPC and stochastic MPC for interactive trajectory planning under agent decision uncertainty.
citing papers explorer
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ViT-K: A Few-Shot Learning Model for Coupled Fluid-Porous Media Flows with Interface Conditions
ViT-K uses Vision Transformers and Koopman operators to learn stable long-term spatiotemporal dynamics of coupled fluid-porous media flows from sparse data.
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NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
NonZero introduces an interaction score and bandit-formalized proposal rule for local agent deviations in multi-agent MCTS, delivering a sublinear local-regret guarantee and improved sample efficiency on game benchmarks without full joint-action enumeration.
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Statistical Properties of $k$-means Clustering for Data Missing Completely at Random
Proves √n-excess risk bounds, consistency, convergence rates, asymptotic normality, and a sufficient condition on missing probability and cluster separation for k-means under MCAR missing data.
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On Local Population-Risk Certificates
Constructs expected-valid upper endpoints for local risk increments Pδ_v via cross-fitted ridge calibration for linear classes and componentwise application to nonsmooth loss decompositions.
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Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems
PAC learning-based DR-MPC framework interpolates between robust MPC and stochastic MPC for interactive trajectory planning under agent decision uncertainty.