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Found 14 papers, 3 papers with code
On Three-Layer Data Markets
We study a three-layer data market comprising users (data owners), platforms, and a data buyer.
The Limits of Price Discrimination Under Privacy Constraints
We consider a privacy mechanism that provides a degree of protection by probabilistically masking each market segment, and we establish that the resultant set of all consumer-producer utilities forms a convex polygon, characterized explicitly as a linear mapping of a certain high-dimensional convex polytope into $\mathbb{R}^2$.
Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms
We consider a platform's problem of collecting data from privacy sensitive users to estimate an underlying parameter of interest.
Private Adaptive Gradient Methods for Convex Optimization
We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm with adaptive stepsizes, as well as the AdaGrad algorithm.
A Wasserstein Minimax Framework for Mixed Linear Regression
We propose an optimal transport-based framework for MLR problems, Wasserstein Mixed Linear Regression (WMLR), which minimizes the Wasserstein distance between the learned and target mixture regression models.
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks
In this paper, we study the generalization properties of Model-Agnostic Meta-Learning (MAML) algorithms for supervised learning problems.
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
In this paper, we study a personalized variant of the federated learning in which our goal is to find an initial shared model that current or new users can easily adapt to their local dataset by performing one or a few steps of gradient descent with respect to their own data.
Personalized Federated Learning: A Meta-Learning Approach
In this paper, we study a personalized variant of the federated learning in which our goal is to find an initial shared model that current or new users can easily adapt to their local dataset by performing one or a few steps of gradient descent with respect to their own data.
An Optimal Multistage Stochastic Gradient Method for Minimax Problems
Next, we propose a multistage variant of stochastic GDA (M-GDA) that runs in multiple stages with a particular learning rate decay schedule and converges to the exact solution of the minimax problem.
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement Learning (RL) problems, where the goal is to find a policy using data from several tasks represented by Markov Decision Processes (MDPs) that can be updated by one step of stochastic policy gradient for the realized MDP.
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
When gradients do not contain noise, we also prove that distributed accelerated methods can \emph{achieve acceleration}, requiring $\mathcal{O}(\kappa \log(1/\varepsilon))$ gradient evaluations and $\mathcal{O}(\kappa \log(1/\varepsilon))$ communications to converge to the same fixed point with the non-accelerated variant where $\kappa$ is the condition number and $\varepsilon$ is the target accuracy.
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
We study the convergence of a class of gradient-based Model-Agnostic Meta-Learning (MAML) methods and characterize their overall complexity as well as their best achievable accuracy in terms of gradient norm for nonconvex loss functions.
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
We study the problem of minimizing a strongly convex, smooth function when we have noisy estimates of its gradient.
Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions
We study the trade-offs between convergence rate and robustness to gradient errors in designing a first-order algorithm.
