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Found 7 papers, 1 papers with code
Causal clustering: design of cluster experiments under network interference
This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers.
Multi-Platform Budget Management in Ad Markets with Non-IC Auctions
Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation.
Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets
We present four main results: 1) for the episodic setting we give sample complexity bounds for the spend rate prediction problem: given $n$ samples from each episode, with high probability we have $|\widehat{\rho}_e - \rho_e| \leq \tilde{O}(\frac{1}{n^{1/3}})$ where $\rho_e$ is the optimal spend rate for the episode, $\widehat{\rho}_e$ is the estimate from our algorithm, 2) we extend the algorithm of Balseiro and Gur (2017) to operate on varying, approximate spend rates and show that the resulting combined system of optimal spend rate estimation and online pacing algorithm for episodic settings has regret that vanishes in number of historic samples $n$ and the number of rounds $T$, 3) for non-episodic but slowly-changing distributions we show that the same approach approximates the optimal bidding strategy up to a factor dependent on the rate-of-change of the distributions and 4) we provide experiments showing that our algorithm outperforms both static spend plans and non-pacing across a wide variety of settings.
Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising
We modify the algorithm proposed in Badanidiyuru \emph{et al.,} to extend it to the case of multiple platforms to obtain an algorithm for both the discrete and continuous bid-spaces.
Equilibria in Auctions With Ad Types
This paper studies equilibrium quality of semi-separable position auctions (known as the Ad Types setting) with greedy or optimal allocation combined with generalized second-price (GSP) or Vickrey-Clarke-Groves (VCG) pricing.
Computer Science and Game Theory
Online Learning for Measuring Incentive Compatibility in Ad Auctions
In this paper we investigate the problem of measuring end-to-end Incentive Compatibility (IC) regret given black-box access to an auction mechanism.
Robust random cut forest based anomaly detection on streams
In this paper we focus on the anomaly detection problem for dynamic data streams through the lens of random cut forests.
