2 citations · 3 across the 8 of their papers we have counts for
8 papers
Efficient Adaptive Data Analysis over Dense Distributions
Joon Suk Huh
Modern data workflows are inherently adaptive, repeatedly querying the same dataset to refine and validate sequential decisions, but such adaptivity can lead to overfitting and inv…
Strategy-robust Online Learning in Contextual Pricing
Joon Suk Huh, Kirthevasan Kandasamy
Learning effective pricing strategies is crucial in digital marketplaces, especially when buyers' valuations are unknown and must be inferred through interaction. We study the onli…
Agora: A System for Consumption-Based GPU Pricing
Ian McDougall, Noah Scott, Joon Huh +2
We present Agora, a lightweight system designed for cloud-based GPUs which meters a three-dimensional resource vector from commodity performance counters and prices it with a funct…
Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning
Joon Suk Huh, Kirthevasan Kandasamy
We study a multi-round mechanism design problem, where we interact with a set of agents over a sequence of rounds. We wish to design an incentive-compatible (IC) online learning sc…
Learning to Price Homogeneous Data
Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy
We study a data pricing problem, where a seller has access to homogeneous data points (e.g. drawn i.i.d. from some distribution). There are types of buyers in the market, w…
Bandit Profit-maximization for Targeted Marketing
Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy
We study a sequential profit-maximization problem, optimizing for both price and ancillary variables like marketing expenditures. Specifically, we aim to maximize profit over an ar…
Differentially Private Online Item Pricing
Joon Suk Huh
This work addresses the problem of revenue maximization in a repeated, unlimited supply item-pricing auction while preserving buyer privacy. We present a novel algorithm that provi…
On the Power of Deep but Naive Partial Label Learning
Junghoon Seo, Joon Suk Huh
Partial label learning (PLL) is a class of weakly supervised learning where each training instance consists of a data and a set of candidate labels containing a unique ground truth…