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2,654 papers
By leveraging interpolating orthogonal projections in RKHSs, this work uses finitely many point evaluations in place of integration-based basis function expansions to lead to a simpler and more flexible neural-network architecture that remains applicable even when the data domain or the underlying kernel is not explici...
This study investigates the use of stochastic gradient descent to learn operators between general Hilbert spaces and establishes upper bounds for convergence rates of the SGD algorithm and derives a minimax lower bound analysis, illustrating that convergence analysis and regularity conditions quantitatively characteriz...
This work suggests that fairness can be directly analyzed as a property of the utility function, and suggests modifying utility functions such that they satisfy value of information fairness.
AI systems increasingly search among candidate answers and deploy the highest-scoring one. Increasing search changes which errors matter, so a precise evaluation at one computation budget can leave another budget unresolved. We connect this information gap to the cost of closing it. For independent best-of-n search, ag...
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A per-layer critical update strength is derived, computed from the measured spectrum of $W$ alone through the rectangular spiked-deformation transform, together with an exact secular-equation characterization of the updated spectrum, with no fitted parameters.
Damped sinusoidal oscillations are widely observed in many physical systems, and their analysis provides access to underlying physical properties. However, parameter estimation becomes difficult when the signal decays rapidly, multiple components are superposed, and observational noise is present. In this study, we dev...
Approximate Nearest Neighbor (ANN) search and Approximate Kernel Density Estimation (A-KDE) are fundamental problems at the core of modern machine learning, with broad applications in data analysis, information systems, and large-scale decision making. In massive and dynamic data streams, a central challenge is to desi...
Targeted data poisoning attacks manipulate model predictions on specific test samples by injecting malicious data into training. Yet existing evaluations report average attack success rates over randomly selected targets, obscuring true worst-case effectiveness. We argue that the right evaluation focuses on the hardest...
This paper introduces N$^2$, a unified Python package and testbed that consolidates a broad class of NN-based methods through a modular, extensible interface and introduces a new NN variant that achieves state-of-the-art results in several settings.
Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states changes abruptly and many sampling algorithms stall. Nested sampling is a particle method that traverses the density of states under a hard energy constraint and is known to...
A finite-sample theory for continuous density ratio estimation and importance-weighted learning under target shift is established and the resulting regression estimator attains the minimax-optimal rates of standard kernel regression.
This work proposes to amortize the graph matching (node alignment) problem and showcases the efficiency of this approach on toy and real world SGP problems of increasing complexity including a novel Mass-spectra to Scaffold task that is introduced.
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.
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.
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