23 citations · 40 across the 8 of their papers we have counts for
8 papers
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method
Bugra Can, Saeed Soori, Maryam Mehri Dehnavi +1
This work proposes a distributed algorithm for solving empirical risk minimization problems, called L-DQN, under the master/worker communication model. L-DQN is a distributed limit…
TENGraD: Time-Efficient Natural Gradient Descent with Exact Fisher-Block Inversion
Saeed Soori, Bugra Can, Baourun Mu +2
This work proposes a time-efficient Natural Gradient Descent method, called TENGraD, with linear convergence guarantees. Computing the inverse of the neural network's Fisher inform…
Randomized Gossiping with Effective Resistance Weights: Performance Guarantees and Applications
Bugra Can, Saeed Soori, Necdet Serhat Aybat +2
The effective resistance between a pair of nodes in a weighted undirected graph is defined as the potential difference induced when a unit current is injected at one node and extra…
ASYNC: A Cloud Engine with Asynchrony and History for Distributed Machine Learning
Saeed Soori, Bugra Can, Mert Gurbuzbalaba +1
ASYNC is a framework that supports the implementation of asynchrony and history for optimization methods on distributed computing platforms. The popularity of asynchronous optimiza…
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate
Saeed Soori, Konstantin Mischenko, Aryan Mokhtari +2
In this paper, we consider distributed algorithms for solving the empirical risk minimization problem under the master/worker communication model. We develop a distributed asynchro…
MatRox: Modular approach for improving data locality in Hierarchical (Mat)rix App(Rox)imation
Bangtian Liu, Kazem Cheshmi, Saeed Soori +2
Hierarchical matrix approximations have gained significant traction in the machine learning and scientific community as they exploit available low-rank structures in kernel methods…
Avoiding Communication in Proximal Methods for Convex Optimization Problems
Saeed Soori, Aditya Devarakonda, James Demmel +2
The fast iterative soft thresholding algorithm (FISTA) is used to solve convex regularized optimization problems in machine learning. Distributed implementations of the algorithm h…