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Found 12 papers, 1 papers with code
A Moreau Envelope Approach for LQR Meta-Policy Estimation
We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear time-invariant uncertain dynamical systems.
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations
In this work, we address the challenge of multi-task image generation with limited data for denoising diffusion probabilistic models (DDPM), a class of generative models that produce high-quality images by reversing a noisy diffusion process.
Adaptive Federated Learning with Auto-Tuned Clients
Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients without sharing their data.
On First-Order Meta-Reinforcement Learning with Moreau Envelopes
We provide a detailed analysis of the MEMRL algorithm, where we show a sublinear convergence rate to a first-order stationary point for non-convex policy gradient optimization.
PersA-FL: Personalized Asynchronous Federated Learning
Our main technical contribution is a unified proof for asynchronous federated learning with bounded staleness that we apply to MAML and ME personalization frameworks.
Unbounded Gradients in Federated Leaning with Buffered Asynchronous Aggregation
Synchronous updates may compromise the efficiency of cross-device federated learning once the number of active clients increases.
On Arbitrary Compression for Decentralized Consensus and Stochastic Optimization over Directed Networks
We study the decentralized consensus and stochastic optimization problems with compressed communications over static directed graphs.
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix over a set of local machines.
Scalable Average Consensus with Compressed Communications
We propose a new decentralized average consensus algorithm with compressed communication that scales linearly with the network size n. We prove that the proposed method converges to the average of the initial values held locally by the agents of a network when agents are allowed to communicate with compressed messages.
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming.
Communication-efficient Distributed Cooperative Learning with Compressed Beliefs
We study the problem of distributed cooperative learning, where a group of agents seeks to agree on a set of hypotheses that best describes a sequence of private observations.
MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
We achieve this by developing MP-Boost, an algorithm loosely based on AdaBoost that learns by adaptively selecting small subsets of instances and features, or what we term minipatches (MP), at each iteration.
