4 citations · 4 across the 4 of their papers we have counts for
4 papers
On Noisy Evaluation in Federated Hyperparameter Tuning
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak +4
Hyperparameter tuning is critical to the success of federated learning applications. Unfortunately, appropriately selecting hyperparameters is challenging in federated networks. Is…
Extreme Multi-label Learning for Semantic Matching in Product Search
Wei-Cheng Chang, Daniel Jiang, Hsiang-Fu Yu +9
We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more.…
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification
Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri +2
Extreme multi-label classification (XMC) aims to learn a model that can tag data points with a subset of relevant labels from an extremely large label set. Real world e-commerce ap…
The Power of Batching in Multiple Hypothesis Testing
Tijana Zrnic, Daniel L. Jiang, Aaditya Ramdas +1
One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms. The first generally achieve signific…