3 citations · 9 across the 12 of their papers we have counts for
12 papers
Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses
Ziye Ma, Ying Chen, Javad Lavaei +1
Matrix sensing problems exhibit pervasive non-convexity, plaguing optimization with a proliferation of suboptimal spurious solutions. Avoiding convergence to these critical points…
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding
Yatong Bai, Utsav Garg, Apaar Shanker +10
Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collec…
Tempo Adaptation in Non-stationary Reinforcement Learning
Hyunin Lee, Yuhao Ding, Jongmin Lee +3
We first raise and tackle a ``time synchronization'' issue between the agent and the environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real-…
Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing
Ziye Ma, Javad Lavaei, Somayeh Sojoudi
Gradient descent (GD) is crucial for generalization in machine learning models, as it induces implicit regularization, promoting compact representations. In this work, we examine t…
Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks
Brendon G. Anderson, Samuel Pfrommer, Somayeh Sojoudi
The reliable deployment of neural networks in control systems requires rigorous robustness guarantees. In this paper, we obtain tight robustness certificates over convex attack set…
Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization
Tanmay Gautam, Reid Pryzant, Ziyi Yang +2
Vector Quantization (VQ) is a well-known technique in deep learning for extracting informative discrete latent representations. VQ-embedded models have shown impressive results in…