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2,791 papers
COVariate-ovERlap regularized multi-task learning (COVER) is proposed to address covariate and posterior heterogeneity and achieves the lowest response-averaged prediction error among the compared methods and reveals tissue-pair integration patterns.
This work proposes pruning (removing unnecessary variables) and clustering (combining variables) as preprocessing operations for causal data fusion and gives sufficient conditions for inferring the identifiability or non-identifiability of a causal effect in a larger graph based on a smaller graph.
Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). A key result in this area shows that with accurate score estimates, SGMs can efficiently generate samples from any realistic data distribution (Chen et al., ICLR'23; Lee et al., ALT'23)...
This work proposes a novel formalization of label flipping attacks as a per-round constrained optimization problem, derives a greedy label-selection rule for logistic regression, and empirically evaluates it beyond its derivation setting, including on MLPs and robust aggregators.
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This paper establishes a collection of covering number bounds for linear function classes under various norm constraints on the inputs and matrices and explicitly incorporates rank constraints on the underlying matrix classes, allowing us to characterize how low-rank structures affect the metric entropy.
It is shown that EM recovers the mixture atoms at the parametric rate, up to logarithmic factors, after $\mathcal{O}(\log N)$ iterations, provided atom separation is at least of order $\log K$.
Offline reinforcement learning enables evaluation and optimization of sequential decisions from historical data, when it is not possible to deploy new policies online due to safety, cost, and other concerns. Big data advances enable rich state information, but may naively include reward- and action- irrelevant dynamics...
Mixed membership modeling for undirected networks has been extensively explored in network science over the past few years. Despite the substantial progress made for undirected cases, handling mixed membership structures in directed networks continues to pose substantial difficulties. To address this gap, we introduce...
This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods in statistical and probabilistic learning. We start with the optimization of decision boundary on which the posteriors are equal. Then, LDA and QDA are derived for binary and mul...
It has been discovered that negative curvature subverts the loss landscape, specifically sectional curvature, so it has been discovered that negative curvature subverts the loss landscape, so this work expands on this and draw interconnections to negative-definite Ricci curvature.
This work introduces the first algorithm that exactly enumerates decision-tree Rashomon sets while exploiting the ordered structure of continuous features, and develops a relaxation for approximate enumeration and an anytime algorithm that progressively refines the set of candidate thresholds, producing increasingly de...
The results indicate that diffusion noise is not merely a passive stochastic perturbation, but a learnable---and therefore potentially designable---input dimension.
General Science
General Science
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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.
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