3 citations · 6 across the 8 of their papers we have counts for
9 papers
A Minimal ReLU Classifier on Non-Linearly Separable Data: Convergence and Implicit Bias at and beyond the Edge of Stability
Quan Nguyen, Robin Yadav, Param Mody +1
Gradient descent (GD) with large learning rates can successfully optimize neural networks even though classical smoothness-based learning rate conditions are violated. Yet theory f…
Amortized nonmyopic active search via deep imitation learning
Quan Nguyen, Anindya Sarkar, Roman Garnett
Active search formalizes a specialized active learning setting where the goal is to collect members of a rare, valuable class. The state-of-the-art algorithm approximates the optim…
Probabilistic Prediction of Material Stability: Integrating Convex Hulls into Active Learning
Andrew Novick, Diana Cai, Quan Nguyen +3
Active learning is a valuable tool for efficiently exploring complex spaces, finding a variety of uses in materials science. However, the determination of convex hulls for phase di…
Adversarial Online Multi-Task Reinforcement Learning
Quan Nguyen, Nishant A. Mehta
We consider the adversarial online multi-task reinforcement learning setting, where in each of episodes the learner is given an unknown task taken from a finite set of unkn…
The Mixing Thermodynamics and Local Structure of High-entropy Alloys from Randomly Sampled Ordered Configurations
Andrew Novick, Quan Nguyen, Roman Garnett +2
A general method is presented for modeling high entropy alloys as ensembles of randomly sampled, ordered configurations on a given lattice. Statistical mechanics is applied post ho…
Local Bayesian optimization via maximizing probability of descent
Quan Nguyen, Kaiwen Wu, Jacob R. Gardner +1
Local optimization presents a promising approach to expensive, high-dimensional black-box optimization by sidestepping the need to globally explore the search space. For objective…
Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery
Quan Nguyen, Roman Garnett
Active search is a setting in adaptive experimental design where we aim to uncover members of rare, valuable class(es) subject to a budget constraint. An important consideration in…
Nonmyopic Multifidelity Active Search
Quan Nguyen, Arghavan Modiri, Roman Garnett
Active search is a learning paradigm where we seek to identify as many members of a rare, valuable class as possible given a labeling budget. Previous work on active search has ass…
Guided Data Discovery in Interactive Visualizations via Active Search
Shayan Monadjemi, Sunwoo Ha, Quan Nguyen +3
Recent advances in visual analytics have enabled us to learn from user interactions and uncover analytic goals. These innovations set the foundation for actively guiding users duri…