2 citations · 4 across the 12 of their papers we have counts for
13 papers
Benign Landscape of Quadratic Programs with Orthogonality Constraints and Its Application to Heteroscedastic Probabilistic PCA
Peng Wang, Po Chen, Rujun Jiang +1
In this work, we study the optimization landscape of homogeneous quadratic programs with orthogonality constraints (QPOC) and apply the resulting theory to heteroscedastic probabil…
A Concentration Inequality for the Covariance Matrix of an Arbitrary Subset of Random Vectors
Huikang Liu, Peng Wang, Laura Balzano
Concentration inequalities for sample covariance matrices are fundamental tools in high-dimensional probability. Classical results typically assume that the selected random vectors…
The Effect of Training Task Diversity on In-Context Learning through the Lens of Low-Dimensional Subspaces
Soo Min Kwon, Alec S. Xu, Can Yaras +3
The transformer's emergent ability to perform in-context learning (ICL) has sparked a wide range of studies designed to understand its underlying mechanisms. Existing works often s…
Emergent Low-Rank Training Dynamics in MLPs with Smooth Activations
Alec S. Xu, Can Yaras, Matthew Asato +2
Recent empirical evidence has demonstrated that the training dynamics of large-scale deep neural networks occur within low-dimensional subspaces. While this has inspired new resear…
MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention
Can Yaras, Alec S. Xu, Pierre Abillama +2
Transformers have achieved state-of-the-art performance across various tasks, but suffer from a notable quadratic complexity in sequence length due to the attention mechanism. In t…
Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective
Soo Min Kwon, Alec S. Xu, Can Yaras +2
The transformer's remarkable ability to perform in-context learning (ICL) has sparked a wide range of studies designed to understand its strengths and limitations. However, a theor…
An Overview of Low-Rank Structures in the Training and Adaptation of Large Models
Laura Balzano, Tianjiao Ding, Benjamin D. Haeffele +5
The substantial computational demands of modern large-scale deep learning present significant challenges for efficient training and deployment. Recent research has revealed a wides…
A Spectral Framework for Tracking Communities in Evolving Networks
Jacob Hume, Laura Balzano
Discovering and tracking communities in time-varying networks is an important task in network science, motivated by applications in fields ranging from neuroscience to sociology. I…
Symmetric Matrix Completion with ReLU Sampling
Huikang Liu, Peng Wang, Longxiu Huang +2
We study the problem of symmetric positive semi-definite low-rank matrix completion (MC) with deterministic entry-dependent sampling. In particular, we consider rectified linear un…
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Can Yaras, Peng Wang, Laura Balzano +1
While overparameterization in machine learning models offers great benefits in terms of optimization and generalization, it also leads to increased computational requirements as mo…
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics
Soo Min Kwon, Zekai Zhang, Dogyoon Song +2
Overparameterized models have proven to be powerful tools for solving various machine learning tasks. However, overparameterization often leads to a substantial increase in computa…
Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination
Peng Wang, Xiao Li, Can Yaras +4
Over the past decade, deep learning has proven to be a highly effective tool for learning meaningful features from raw data. However, it remains an open question how deep networks…
The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks
Can Yaras, Peng Wang, Wei Hu +3
Over the past few years, an extensively studied phenomenon in training deep networks is the implicit bias of gradient descent towards parsimonious solutions. In this work, we inves…