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Found 13 papers, 2 papers with code
On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes
We present the first finite time global convergence analysis of policy gradient in the context of infinite horizon average reward Markov decision processes (MDPs).
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
Byzantine-robust learning has emerged as a prominent fault-tolerant distributed machine learning framework.
$μ^2$-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism
We consider stochastic convex optimization problems where the objective is an expectation over smooth functions.
SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
We consider distributed learning scenarios where M machines interact with a parameter server along several communication rounds in order to minimize a joint objective function.
DoCoFL: Downlink Compression for Cross-Device Federated Learning
Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures.
Online Meta-Learning in Adversarial Multi-Armed Bandits
This solution comprises an inner learner that plays each episode separately, and an outer learner that updates the hyper-parameters of the inner algorithm between the episodes.
Adapting to Mixing Time in Stochastic Optimization with Markovian Data
We consider stochastic optimization problems where data is drawn from a Markov chain.
Robust Linear Regression for General Feature Distribution
In this work we go beyond these assumptions and investigate robust regression under a more general set of assumptions: $\textbf{(i)}$ we allow the covariance matrix to be either positive definite or positive semi definite, $\textbf{(ii)}$ we do not necessarily assume that the features are centered, $\textbf{(iii)}$ we make no further assumption beyond boundedness (sub-Gaussianity) of features and measurement noise.
No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization
We develop an algorithmic framework for solving convex optimization problems using no-regret game dynamics.
STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization
In this work we propose STORM+, a new method that is completely parameter-free, does not require large batch-sizes, and obtains the optimal $O(1/T^{1/3})$ rate for finding an approximate stationary point.
Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays
We consider stochastic convex optimization problems, where several machines act asynchronously in parallel while sharing a common memory.
Generative Minimization Networks: Training GANs Without Competition
Among known problems experienced by practitioners is the lack of convergence guarantees or convergence to a non-optimum cycle.
Faster Neural Network Training with Approximate Tensor Operations
We propose a novel technique for faster deep neural network training which systematically applies sample-based approximation to the constituent tensor operations, i. e., matrix multiplications and convolutions.
