Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Mason Blake"'
Autor:
David A. Shearer, Shona Leeworthy, Sarah Jones, Emma Rickards, Mason Blake, Robert M. Heirene, Mike J. Gross, Adam M. Bruton
Publikováno v:
Frontiers in Sports and Active Living, Vol 2 (2020)
Little is understood about the attentional mechanisms that lead to perceptions of collective efficacy. This paper presents two studies that address this lack of understanding. Study one examined participant's (N = 59) attentional processes relating t
Externí odkaz:
https://doaj.org/article/23bc8f9148274071b032a08aa52ec31f
Autor:
Weltz, Justin, Fiez, Tanner, Volfovsky, Alexander, Laber, Eric, Mason, Blake, Nassif, Houssam, Jain, Lalit
Publikováno v:
Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, pp. 65967-66005, 2023
Most linear experimental design problems assume homogeneous variance although heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set of measurement vectors $\mathcal{X}\subset \mathbb{R}^d$ that can be
Externí odkaz:
http://arxiv.org/abs/2310.04390
Autor:
McDonald, Tavish, Tsan, Brian, Saini, Amar, Ordonez, Juanita, Gutierrez, Luis, Nguyen, Phan, Mason, Blake, Ng, Brenda
Researchers produce thousands of scholarly documents containing valuable technical knowledge. The community faces the laborious task of reading these documents to identify, extract, and synthesize information. To automate information gathering, docum
Externí odkaz:
http://arxiv.org/abs/2210.01959
This paper investigates simultaneous preference and metric learning from a crowd of respondents. A set of items represented by $d$-dimensional feature vectors and paired comparisons of the form ``item $i$ is preferable to item $j$'' made by each user
Externí odkaz:
http://arxiv.org/abs/2207.03609
Is overparameterization a privacy liability? In this work, we study the effect that the number of parameters has on a classifier's vulnerability to membership inference attacks. We first demonstrate how the number of parameters of a model can induce
Externí odkaz:
http://arxiv.org/abs/2205.14055
In this work we consider the problem of regret minimization for logistic bandits. The main challenge of logistic bandits is reducing the dependence on a potentially large problem dependent constant $\kappa$ that can at worst scale exponentially with
Externí odkaz:
http://arxiv.org/abs/2202.02407
A surprising phenomenon in modern machine learning is the ability of a highly overparameterized model to generalize well (small error on the test data) even when it is trained to memorize the training data (zero error on the training data). This has
Externí odkaz:
http://arxiv.org/abs/2202.01243
We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We begin our investigation with the observation that agnostic algorithms \emp
Externí odkaz:
http://arxiv.org/abs/2111.04915
Autor:
Mason, Blake, Camilleri, Romain, Mukherjee, Subhojyoti, Jamieson, Kevin, Nowak, Robert, Jain, Lalit
The level set estimation problem seeks to find all points in a domain ${\cal X}$ where the value of an unknown function $f:{\cal X}\rightarrow \mathbb{R}$ exceeds a threshold $\alpha$. The estimation is based on noisy function evaluations that may be
Externí odkaz:
http://arxiv.org/abs/2111.01768
Autor:
Alemohammad, Sina, Babaei, Hossein, Barberan, CJ, Liu, Naiming, Luzi, Lorenzo, Mason, Blake, Baraniuk, Richard G.
Deep neural networks have become essential for numerous applications due to their strong empirical performance such as vision, RL, and classification. Unfortunately, these networks are quite difficult to interpret, and this limits their applicability
Externí odkaz:
http://arxiv.org/abs/2110.04945