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2,791 papers
This paper derives an estimator of the AUC risk using biased PU data with confidence, enabling AUC maximization under such bias, and shows that the rewritten AUC risk induces a Bayes-optimal AUC ranking even when the available confidence is any strictly increasing transformation of the true posterior probability.
We continue the study of relatively smart learning, introduced by Dughmi and Pour (2026), which asks a supervised learner to compete, marginal by marginal, with every distribution-fixed error guarantee soundly certifiable from unlabeled data. They showed that the One-Inclusion Graph (OIG) learner is relatively smart wi...
This work proves that incremental learning still occurs, and introduces a symmetry-based finite-width approximation via symmetrized networks, rather than comparing directly with an infinite-width limit, which yields better control of approximation errors and may be of independent interest.
Continuous and discrete flow matching are usually treated as separate constructions. This paper identifies a duality between them: projecting continuous convex-interpolant paths with one-hot targets through a position-wise argmax yields discrete convex-interpolant paths. The result requires source laws with appropriate...
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Across CIFAR-100-C, Tiny-ImageNet-C, and SEN12MS, this work shows reliable target-domain coverage transfer without labeled target calibration data, with label-free diagnostics that predict when correction is needed.
Food structure and its effect on deformation behaviour and sensory perception is still a challenge in food science. Small-Amplitude Oscillatory Shear (SAOS) is a common method for the determination of the linear viscoelastic properties and stability of food materials, but it is not applicable to large deformations, suc...
It is argued that a third, largely untapped signal is both richer and more principled: critical adjacency, the pairwise relation established when an expert critic explicitly links two artists in long-form prose, which offers both a scalable cold-start discovery mechanism and a sociological grounded approach to MIR and...
We develop a reinforcement learning pipeline for simplifying knot diagrams. A trained agent learns move proposals and a value heuristic for navigating Reidemeister moves. The pipeline applies to arbitrary knots and links; we test it on ``very hard'' unknot diagrams and, using diagram inflation, on $4_1\#9_{10}$ where w...
Many high-dimensional datasets concentrate near a low-dimensional structure embedded in the ambient space. Generative models for such data must control off-support mass while remaining computationally practical. Diffusion models use iterative denoising at inference, whereas standard normalizing flows require invertible...
A universal approximation theorem is established for DFNNs, advancing the state-of-the-art of neural network approximation theory to general metric-space-valued responses, without making model assumptions or relying on local smoothing.
This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilbert spaces (RKHS). They are widely studied and used in machine learning, statistics, and numerical analysis. We study co...
An overview of the theory of overparameterized ML is provided by focusing on explaining the most foundational findings through a statistical signal processing perspective and emphasizing the unique aspects that define the TOPML research area as a subfield of modern ML theory and outline interesting open frontiers that...
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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