Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Saeed, Basil"'
Autor:
Misiakiewicz, Theodor, Saeed, Basil
We consider learning an unknown target function $f_*$ using kernel ridge regression (KRR) given i.i.d. data $(u_i,y_i)$, $i\leq n$, where $u_i \in U$ is a covariate vector and $y_i = f_* (u_i) +\varepsilon_i \in \mathbb{R}$. A recent string of work h
Externí odkaz:
http://arxiv.org/abs/2403.08938
Maximum margin binary classification is one of the most fundamental algorithms in machine learning, yet the role of featurization maps and the high-dimensional asymptotics of the misclassification error for non-Gaussian features are still poorly unde
Externí odkaz:
http://arxiv.org/abs/2310.00176
Autor:
Montanari, Andrea, Saeed, Basil
Consider supervised learning from i.i.d. samples $\{{\boldsymbol x}_i,y_i\}_{i\le n}$ where ${\boldsymbol x}_i \in\mathbb{R}^p$ are feature vectors and ${y} \in \mathbb{R}$ are labels. We study empirical risk minimization over a class of functions th
Externí odkaz:
http://arxiv.org/abs/2202.08832
We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG). We provide a graphical representation of such mixture distributions and prove that this representation encodes the c
Externí odkaz:
http://arxiv.org/abs/2001.11940
We consider the task of learning a causal graph in the presence of latent confounders given i.i.d.~samples from the model. While current algorithms for causal structure discovery in the presence of latent confounders are constraint-based, we here pro
Externí odkaz:
http://arxiv.org/abs/1910.09014
We consider the problem of learning a causal graph in the presence of measurement error. This setting is for example common in genomics, where gene expression is corrupted through the measurement process. We develop a provably consistent procedure fo
Externí odkaz:
http://arxiv.org/abs/1906.00928
Autor:
Yildirim, Ilker, Saeed, Basil, Bennett-Pierre, Grace, Gerstenberg, Tobias, Tenenbaum, Joshua, Gweon, Hyowon
The ability to estimate task difficulty is critical for many real-world decisions such as setting appropriate goals for ourselves or appreciating others' accomplishments. Here we give a computational account of how humans judge the difficulty of a ra
Externí odkaz:
http://arxiv.org/abs/1905.04445
In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an ini
Externí odkaz:
http://arxiv.org/abs/1707.08212
Autor:
Majeed, Sinan Ahmed1,2 (AUTHOR) sinanalali86@gmail.com, Saeed, Basil M. N.3 (AUTHOR)
Publikováno v:
Indian Journal of Otolaryngology & Head & Neck Surgery. 2022 Suppl, Vol. 74, p1713-1717. 5p.
Publikováno v:
In Egyptian Journal of Ear, Nose, Throat and Allied Sciences July 2016 17(2):63-69