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Found 13 papers, 1 papers with code
Distributed Extra-gradient with Optimal Complexity and Communication Guarantees
We consider monotone variational inequality (VI) problems in multi-GPU settings where multiple processors/workers/clients have access to local stochastic dual vectors.
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization
We propose an adaptive variance-reduction method, called AdaSpider, for minimization of $L$-smooth, non-convex functions with a finite-sum structure.
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods
This work proposes a universal and adaptive second-order method for minimizing second-order smooth, convex functions.
No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation
We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is possible to achieve significantly lower regret relative to fully adversarial environments.
Sifting through the noise: Universal first-order methods for stochastic variational inequalities
Our first result is that the algorithm achieves the optimal rates of convergence for cocoercive problems when the profile of the randomness is known to the optimizer: $\mathcal{O}(1/\sqrt{T})$ for absolute noise profiles, and $\mathcal{O}(1/T)$ for relative ones.
Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights
We examine an adaptive learning framework for nonatomic congestion games where the players' cost functions may be subject to exogenous fluctuations (e. g., due to disturbances in the network, variations in the traffic going through a link).
Adaptive first-order methods revisited: Convex optimization without Lipschitz requirements
We propose a new family of adaptive first-order methods for a class of convex minimization problems that may fail to be Lipschitz continuous or smooth in the standard sense.
Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements
We propose a new family of adaptive first-order methods for a class of convex minimization problems that may fail to be Lipschitz continuous or smooth in the standard sense.
Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium
In game-theoretic learning, several agents are simultaneously following their individual interests, so the environment is non-stationary from each player's perspective.
Adaptive extra-gradient methods for min-max optimization and games
We present a new family of min-max optimization algorithms that automatically exploit the geometry of the gradient data observed at earlier iterations to perform more informative extra-gradient steps in later ones.
Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach
Motivated by applications to machine learning and imaging science, we study a class of online and stochastic optimization problems with loss functions that are not Lipschitz continuous; in particular, the loss functions encountered by the optimizer could exhibit gradient singularities or be singular themselves.
An adaptive Mirror-Prox method for variational inequalities with singular operators
Lipschitz continuity is a central requirement for achieving the optimal O(1/T) rate of convergence in monotone, deterministic variational inequalities (a setting that includes convex minimization, convex-concave optimization, nonatomic games, and many other problems).
On the Generalization of Stochastic Gradient Descent with Momentum
While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods.
