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2,653 papers
We revisit Breiman's observation that reducing inter-tree correlation without weakening individual trees can improve random forests. Building on this principle, we introduce two variants: Dirichlet-Multinomial Bagging Random Forest (DM) and Dirichlet-Weighted Random Forest (DW). Both modulate sample reweighting via a c...
This work studies the conditional empirical Rademacher complexity of deep ReLU representations followed by bounded-norm predictors in additive or L\'evy-Brownian RKHSs, termed Brownian heads, and derives an exact dual identity and sharp bounds in terms of activation mass, the average norm of the observed hidden vectors...
The Logic Screening method (LoSc) is proposed, an ultra fast, accurate, and theoretically grounded framework for large-scale screening that explicitly incorporates dilution and sample usage constraints into pooling designs, and establishes theoretical guarantees to guide optimal pooling configurations.
This paper formalizes an individualized sparse regression framework for matrix-valued covariates in which each observation has its own rows of interest, while the associated regression effects are shared across the population.
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A matrix-free equivariant full-GP implementation that combines an exact Kronecker reduction with preconditioned conjugate-gradient solves is developed, enabling fast and scalable evaluation of the full joint predictive density, and triply scalable equivariant Gaussian processes are introduced.
We study data-driven early stopping for spectral regularisation methods in the classical non-parametric regression setting. Building on the discrepancy principle, we propose a multi-scale stopping rule that applies to general kernel estimators and show that, unlike previous approaches, it achieves full adaptivity over...
Experiments with variational autoencoders show that aggregated posterior sampling improves generation for heavy-tailed and clustered data relative to Gaussian prior sampling while performing comparably to models with more flexible latent priors.
This framework organizes existing methods around five components: explicit state representation, transition structuring, validation and constraint enforcement, search and rollback, and uncertainty management, and yields a diagnostic hypothesis: interventions should be most effective when they target the error or uncert...
It is proved that a deterministic order rule on $\mathbb R$ beats chance for every pair of distinct Borel probability measures and that no rule works for every distinct pair of distributions and every unknown unbalanced class prior.
Visual signals require compact yet sufficient representations for robust downstream prediction. Convolutional sparse coding (CSC) provides an explicit mechanism for suppressing redundant components while preserving signal content, but its sparsity coefficient is often manually selected during training. We propose a tra...
Geometric Mean Pooling is introduced, a signed pooling operator that combines the product of feature signs with the geometric mean of feature magnitudes and retains both joint sign information and a characteristic multiplicative scale without introducing learnable pooling parameters.
This work presents a general framework for multi-domain clustering via measure quantization: given samples from multiple domains, given samples from multiple domains, they learn a shared set of cluster prototypes by minimizing a probability metric between each domain's probability measure and the measure of prototypes.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
As director, Bourg has focused on digital access, open and equitable scholarly publishing, and expanded support for data-intensive research.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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