Power in Monte Carlo permutation tests is non-monotonic and can decrease with more sampled permutations, with such decreases occurring infinitely often due to distributional discreteness.
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Domain transfer becomes identifiable from marginals plus one anchor under Jacobian sparsity, enabled by a randomized masked finite-difference regularizer.
Score-based diffusion built intrinsically on the quantum pure-state manifold CP^{d-1}, trained with a local-time Gaussian teacher, matches pure-state ensembles far better than Euclidean baselines in the local-cluster regime, with gains shrinking on globally spread ensembles.
Adversarial optimal transport objectives jointly learn summary statistics and a chaotic-system emulator from a single noisy trajectory, improving long-term statistical fidelity over handcrafted-feature baselines.
LeJEPA derives an optimal isotropic Gaussian target for embeddings and enforces it via sketched regularization to deliver scalable, heuristics-free self-supervised pretraining with 79% ImageNet linear accuracy on ViT-H/14.
citing papers explorer
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More Permutations Do Not Always Increase Power: Non-monotonicity in Monte Carlo Permutation Tests
Power in Monte Carlo permutation tests is non-monotonic and can decrease with more sampled permutations, with such decreases occurring infinitely often due to distributional discreteness.
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Domain Transfer Becomes Identifiable via a Single Alignment
Domain transfer becomes identifiable from marginals plus one anchor under Jacobian sparsity, enabled by a randomized masked finite-difference regularizer.
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Local-Time Riemannian Score Matching on the Quantum Pure-State Manifold
Score-based diffusion built intrinsically on the quantum pure-state manifold CP^{d-1}, trained with a local-time Gaussian teacher, matches pure-state ensembles far better than Euclidean baselines in the local-cluster regime, with gains shrinking on globally spread ensembles.
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Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
Adversarial optimal transport objectives jointly learn summary statistics and a chaotic-system emulator from a single noisy trajectory, improving long-term statistical fidelity over handcrafted-feature baselines.
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LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
LeJEPA derives an optimal isotropic Gaussian target for embeddings and enforces it via sketched regularization to deliver scalable, heuristics-free self-supervised pretraining with 79% ImageNet linear accuracy on ViT-H/14.