Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromised servers. In this approach, each worker node processes a combination of the data, rather than the raw data itself. The final result then is decoded from the collective outputs of the worker nodes. However, there is a significant gap between current coded computing approaches and the broader landscape of general distributed computing, particularly when it comes to machine learning workloads. To bridge this gap, we propose a novel foundation for coded computing, integrating the principles of learning theory, and developing a framework that seamlessly adapts with machine learning applications. In this framework, the objective is to find the encoder and decoder functions that minimize the loss function, defined as the mean squared error between the estimated and true values. Facilitating the search for the optimum decoding and functions, we show that the loss function can be upper-bounded by the summation of two terms: the generalization error of the decoding function and the training error of the encoding function. Focusing on the second-order Sobolev space, we then derive the optimal encoder and decoder.

Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions.

A wide array of sequence models are built on a framework modeled after Transformers, comprising alternating sequence mixer and channel mixer layers.

Environment Animals Wildlife Fish Self-piloting submarine set to begin historic mission to circle Earth's oceans Breakthroughs, discoveries, and DIY tips sent every weekday. An autonomous submersible named Redwing is heading out on a truly historic voyage. If successful, it will achieve the first around-the-world ocean trip made by an unpiloted underwater vehicle . Marine engineering company Teledyne Marine and researchers at Rutgers University in New Jersey are planning to launch the nearly nine-foot-long, specially outfitted Slocum Sentinel Glider on October 11 from Woods Hole Oceanographic Institution off the coast of Martha's Vineyard in Massachusetts. A livestream of the launch will be broadcast here, beginning at about 8:15 a.m. EDT on Saturday October 11.

Diffusion-based models have achieved notable empirical successes in reinforcement learning (RL) due to their expressiveness in modeling complex distributions. Despite existing methods being promising, the key challenge of extending existing methods for broader real-world applications lies in the computational cost at inference time, i.e., sampling from a diffusion model is considerably slow as it often requires tens to hundreds of iterations to generate even one sample. To circumvent this issue, we propose to leverage the flexibility of diffusion models for RL from a representation learning perspective.