Search Results for author:
Found 8 papers, 4 papers with code
AutoML for Large Capacity Modeling of Meta's Ranking Systems
The large scale of models and tight production schedule requires AutoML to outperform human baselines by only using a small number of model evaluation trials (around 100).
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree.
Cost Effective Active Search
We study a special paradigm of active learning, called cost effective active search, where the goal is to find a given number of positive points from a large unlabeled pool with minimum labeling cost.
BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design
Finite-horizon sequential experimental design (SED) arises naturally in many contexts, including hyperparameter tuning in machine learning among more traditional settings.
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Graph structured data are abundant in the real world.
Efficient nonmyopic batch active search
A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery.
Efficient nonmyopic active search with applications in drug and materials discovery
We also study the batch setting for the first time, where a batch of $b>1$ points can be queried at each iteration.
Efficient Nonmyopic Active Search
Active search is an active learning setting with the goal of identifying as many members of a given class as possible under a labeling budget.
