Search Results for author: Basil Saeed

Found 7 papers, 0 papers with code

A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator

no code implementations13 Mar 2024 Theodor Misiakiewicz, Basil Saeed

Specifically, we establish in this setting a non-asymptotic deterministic approximation for the test error of KRR -- with explicit non-asymptotic bounds -- that only depends on the eigenvalues and the target function alignment to the eigenvectors of the kernel.

Universality of max-margin classifiers

no code implementations29 Sep 2023 Andrea Montanari, Feng Ruan, Basil Saeed, Youngtak Sohn

Working in the high-dimensional regime in which the number of features $p$, the number of samples $n$ and the input dimension $d$ (in the nonlinear featurization setting) diverge, with ratios of order one, we prove a universality result establishing that the asymptotic behavior is completely determined by the expected covariance of feature vectors and by the covariance between features and labels.

Binary Classification

Universality of empirical risk minimization

no code implementations17 Feb 2022 Andrea Montanari, Basil Saeed

In particular, the asymptotics of these quantities can be computed $-$to leading order$-$ under a simpler model in which the feature vectors ${\boldsymbol x}_i$ are replaced by Gaussian vectors ${\boldsymbol g}_i$ with the same covariance.

Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

no code implementations ICML 2020 Basil Saeed, Snigdha Panigrahi, Caroline Uhler

We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG).


Ordering-Based Causal Structure Learning in the Presence of Latent Variables

no code implementations20 Oct 2019 Daniel Irving Bernstein, Basil Saeed, Chandler Squires, Caroline Uhler

We consider the task of learning a causal graph in the presence of latent confounders given i. i. d.~samples from the model.

Explaining intuitive difficulty judgments by modeling physical effort and risk

no code implementations11 May 2019 Ilker Yildirim, Basil Saeed, Grace Bennett-Pierre, Tobias Gerstenberg, Joshua Tenenbaum, Hyowon Gweon

The ability to estimate task difficulty is critical for many real-world decisions such as setting appropriate goals for ourselves or appreciating others' accomplishments.

Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints

no code implementations25 Jul 2017 Ilker Yildirim, Tobias Gerstenberg, Basil Saeed, Marc Toussaint, Josh Tenenbaum

In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands.

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