2 citations · 3 across the 6 of their papers we have counts for
8 papers
The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
Tavor Z. Baharav, Spyros Dragazis, Aldo Pacchiano
We study sequential testing for a binary disease outcome when risk follows an unknown logistic model. At each round, the decision maker may either pay for a test revealing the true…
Adaptive Data Depth via Multi-Armed Bandits
Tavor Z. Baharav, Tze Leung Lai
Data depth, introduced by Tukey (1975), is an important tool in data science, robust statistics, and computational geometry. One chief barrier to its broader practical utility is t…
Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits
Yifei Wang, Tavor Baharav, Yanjun Han +2
In the infinite-armed bandit problem, each arm's average reward is sampled from an unknown distribution, and each arm can be sampled further to obtain noisy estimates of the averag…
Approximate Function Evaluation via Multi-Armed Bandits
Tavor Z. Baharav, Gary Cheng, Mert Pilanci +1
We study the problem of estimating the value of a known smooth function at an unknown point , where each component can be sampled via a noi…
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…
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment
Govinda M. Kamath, Tavor Z. Baharav, Ilan Shomorony
Pairwise alignment of DNA sequencing data is a ubiquitous task in bioinformatics and typically represents a heavy computational burden. State-of-the-art approaches to speed up this…