Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

Abstract Large Language Models (LLMs), despite their recent impressive accomplishments,
are notably cost-prohibitive to deploy, particularly for applications involving long-content …

We give an algorithm that computes exact maximum flows and minimum-cost flows on
directed graphs with m edges and polynomially bounded integral demands, costs, and …

Gradient inversion attack (or input recovery from gradient) is an emerging threat to the
security and privacy preservation of Federated learning, whereby malicious eavesdroppers …

How can multiple distributed entities train a shared deep net on their private data while
protecting data privacy? This paper introduces InstaHide, a simple encryption of training …

In this paper we provide new randomized algorithms with improved runtimes for solving
linear programs with two-sided constraints. In the special case of the minimum cost flow …

Abstract Multi-Task Learning (MTL) is a well established paradigm for jointly learning
models for multiple correlated tasks. Often the tasks conflict, requiring trade-offs between …

Online matrix vector multiplication is a fundamental step and bottleneck in many machine
learning algorithms. It is defined as follows: given a matrix at the pre-processing phase, at …

Large language models (LLMs) have made transformed changes for human society. One of
the key computation in LLMs is the softmax unit. This operation is important in LLMs …

Semidefinite programs (SDPs) are a fundamental class of optimization problems with
important recent applications in approximation algorithms, quantum complexity, robust …