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Found 12 papers, 1 papers with code
Black-Box $k$-to-$1$-PCA Reductions: Theory and Applications
The $k$-principal component analysis ($k$-PCA) problem is a fundamental algorithmic primitive that is widely-used in data analysis and dimensionality reduction applications.
A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions
For $n>d$ and $\epsilon=1/\sqrt{n}$ this improves over all existing first-order methods.
Testing Causality for High Dimensional Data
Determining causal relationship between high dimensional observations are among the most important tasks in scientific discoveries.
ReSQueing Parallel and Private Stochastic Convex Optimization
We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator.
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization
The accelerated proximal point algorithm (APPA), also known as "Catalyst", is a well-established reduction from convex optimization to approximate proximal point computation (i. e., regularized minimization).
Robust Regression Revisited: Acceleration and Improved Estimation Rates
For the general case of smooth GLMs (e. g. logistic regression), we show that the robust gradient descent framework of Prasad et.
Stochastic Bias-Reduced Gradient Methods
We develop a new primitive for stochastic optimization: a low-bias, low-cost estimator of the minimizer $x_\star$ of any Lipschitz strongly-convex function.
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss
We characterize the complexity of minimizing $\max_{i\in[N]} f_i(x)$ for convex, Lipschitz functions $f_1,\ldots, f_N$.
Fast and Near-Optimal Diagonal Preconditioning
In this paper, we revisit the decades-old problem of how to best improve $\mathbf{A}$'s condition number by left or right diagonal rescaling.
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
We develop two methods for the following fundamental statistical task: given an $\epsilon$-corrupted set of $n$ samples from a $d$-dimensional sub-Gaussian distribution, return an approximate top eigenvector of the covariance matrix.
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport
Optimal transportation, or computing the Wasserstein or ``earth mover's'' distance between two $n$-dimensional distributions, is a fundamental primitive which arises in many learning and statistical settings.
A Direct $\tilde{O}(1/ε)$ Iteration Parallel Algorithm for Optimal Transport
Optimal transportation, or computing the Wasserstein or ``earth mover's'' distance between two distributions, is a fundamental primitive which arises in many learning and statistical settings.
