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Found 14 papers, 1 papers with code
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.
Moments, Random Walks, and Limits for Spectrum Approximation
We study lower bounds for the problem of approximating a one dimensional distribution given (noisy) measurements of its moments.
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.
VO$Q$L: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation
We study time-inhomogeneous episodic reinforcement learning (RL) under general function approximation and sparse rewards.
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).
Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods
We generalize our algorithms for minimax and finite sum optimization to solve a natural family of minimax finite sum optimization problems at an accelerated rate, encapsulating both above results up to a logarithmic factor.
A Unified Framework for Multi-distribution Density Ratio Estimation
Binary density ratio estimation (DRE), the problem of estimating the ratio $p_1/p_2$ given their empirical samples, provides the foundation for many state-of-the-art machine learning algorithms such as contrastive representation learning and covariate shift adaptation.
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.
Towards Tight Bounds on the Sample Complexity of Average-reward MDPs
We prove new upper and lower bounds for sample complexity of finding an $\epsilon$-optimal policy of an infinite-horizon average-reward Markov decision process (MDP) given access to a generative model.
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 $f_1,\ldots, f_N$.
Coordinate Methods for Matrix Games
For linear regression with an elementwise nonnegative matrix, our guarantees improve on exact gradient methods by a factor of $\sqrt{\mathrm{nnz}(A)/(m+n)}$.
Efficiently Solving MDPs with Stochastic Mirror Descent
We present a unified framework based on primal-dual stochastic mirror descent for approximately solving infinite-horizon Markov decision processes (MDPs) given a generative model.
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG
Given a data matrix $\mathbf{A} \in \mathbb{R}^{n \times d}$, principal component projection (PCP) and principal component regression (PCR), i. e. projection and regression restricted to the top-eigenspace of $\mathbf{A}$, are fundamental problems in machine learning, optimization, and numerical analysis.
Variance Reduction for Matrix Games
We present a randomized primal-dual algorithm that solves the problem $\min_{x} \max_{y} y^\top A x$ to additive error $\epsilon$ in time $\mathrm{nnz}(A) + \sqrt{\mathrm{nnz}(A)n}/\epsilon$, for matrix $A$ with larger dimension $n$ and $\mathrm{nnz}(A)$ nonzero entries.
