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Found 9 papers, 1 papers with code
Gradient Descent for Low-Rank Functions
When $r \ll p$, these complexities are smaller than the known complexities of $\mathcal{O}(p \log(1/\epsilon))$ and $\mathcal{O}(p/\epsilon^2)$ of {\gd} in the strongly convex and non-convex settings, respectively.
Functional Linear Regression of Cumulative Distribution Functions
In particular, given $n$ samples with $d$ basis functions, we show estimation error upper bounds of $\widetilde O(\sqrt{d/n})$ for fixed design, random design, and adversarial context cases.
Federated Optimization of Smooth Loss Functions
Under some assumptions on the loss function, e. g., strong convexity in parameter, $\eta$-H\"older smoothness in data, etc., we prove that the federated oracle complexity of FedLRGD scales like $\phi m(p/\epsilon)^{\Theta(d/\eta)}$ and that of FedAve scales like $\phi m(p/\epsilon)^{3/4}$ (neglecting sub-dominant factors), where $\phi\gg 1$ is a "communication-to-computation ratio," $p$ is the parameter dimension, and $d$ is the data dimension.
Inference in Opinion Dynamics under Social Pressure
At each time step, agents broadcast their declared opinions on a social network, which are governed by the agents' inherent opinions and social pressure.
Estimation of Skill Distribution from a Tournament
In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament.
Gradient-Based Empirical Risk Minimization using Local Polynomial Regression
In contrast, we demonstrate that when the loss function is smooth in the data, we can learn the oracle at every iteration and beat the oracle complexities of both GD and SGD in important regimes.
Estimation of Skill Distributions
In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament.
On Universal Features for High-Dimensional Learning and Inference
We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning.
Probabilistic Clustering Using Maximal Matrix Norm Couplings
In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable.
