Category
2,570 papers
A UCB-based algorithm that constructs assortments item-wise by maximizing optimistic marginal gains, avoids black-box optimization oracles, and offers a statistically and computationally efficient solution for diversity-aware assortment optimization under uncertainty is proposed.
The main technical component is to show that the gradient of the logistic loss satisfies a certain approximate invertibility condition (AIC) and uniformly control the deviation of the gradient from its population counterpart by covering and peeling arguments, and then show that the population GD is a contraction by a d...
This work proposes a stratified greedy algorithm that maximizes a heteroscedastic information-gain objective and proves that the objective is monotone and submodular under query-dependent noise, so stratified greedy selection carries a 1/2-approximation guarantee under the partition constraint.
We study nonstationary generalized linear bandits (GLBs), where the expected reward is modeled through a nonlinear link function with an unknown time-varying parameter. This framework encompasses a broad class of reward models, including linear, Bernoulli, and binomial rewards. Existing approaches are predominantly bas...
Reach audiences
Advertise in front of researchers, engineers, and readers.
The functional ANOVA provides a fundamental representation of square-integrable multivariate functions into main effects and higher-order interactions. For independent inputs, the components belong to mutually orthogonal Hilbert subspaces and admit an explicit representation. For dependent inputs, however, the componen...
BroxGD is proposed, a projection-free sister method to projected gradient descent for constrained optimization for constrained optimization that expects to find many applications and inspire numerous extensions, much like projected gradient descent.
This work introduces Multiscale Euclidean Network Trajectories (MENT), whose central estimands are pairwise second-moment operators of the dynamic latent positions of the dynamic latent positions, whose central estimands are pairwise second-moment operators of the dynamic latent positions.
We propose a new regularized optimal transport (OT) formulation, termed sliced-regularized optimal transport (SROT). Unlike entropic OT (EOT), which regularizes the transport plan toward an independent coupling, SROT regularizes it toward a smoothened sliced OT (SOT) plan. To the best of our knowledge, SROT is the firs...
We introduce a robust conformal prediction method based on the half-mass radius $\dP(x):=\inf\{r>0:P(B(x,r))>1/2\}$. Its empirical version is exactly the distance from a point to its $\mathbf{k}$-th nearest neighbour, with $\mathbf{k}=\lfloor n/2\rfloor+1$. The resulting conformal sets retain finite-sample marginal val...
We introduce the exponentially weighted signature (EWS), a continuous-time model that computes iterated integrals of a path, where each increment is weighted by the matrix exponential of a learnable generator over elapsed clock time. We prove that it solves a linear controlled differential equation, keeps the group-lik...
AI systems increasingly assist decision making by producing cheap factor-level assessments of complex inputs, but these assessments can be biased or incomplete. We study selective human evaluation: given AI signals, which factors should be dispatched to human experts? We formulate this human--AI collaboration problem a...
We study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point clouds under a manifold assumption. Graph convolution is defined spectrally via the graph Laplacian, whose low-frequency spectrum approximates that of the Laplace-Be...
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.
Mobile Systems
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
