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Real-time optimization of personalized assortments
TLDR
It is proved that Inventory-Balancing algorithms with a strictly concave penalty function always obtain more than 50% of the optimal revenue, and that personalization based on each customer's location can lead to over 10% improvements in revenue, compared to a policy that treats all customers the same.
Dynamic Pricing in High-Dimensions
TLDR
A dynamic policy is proposed, called Regularized Maximum Likelihood Pricing (RMLP) that leverages the (sparsity) structure of the high-dimensional model and obtains a logarithmic regret in T, and it is shown that no policy can obtain regret better than RMLP.
Maximizing Stochastic Monotone Submodular Functions
TLDR
This model can capture the effect of stochasticity in a wide range of applications and shows that the adaptivity gap—the ratio between the values of optimal adaptive and optimal nonadaptive policies—is bounded.
Real-Time Optimization of Personalized Assortments
TLDR
This work proposes a family of index-based policies that effectively coordinate the real-time assortment decisions with the back-end supply chain constraints, allowing the demand process to be arbitrary and proving that the algorithms achieve an optimal competitive ratio.
Computing Optimal Bundles for Sponsored Search
TLDR
This work studies the algorithmic question of computing the revenue-maximizing partition of a set of items under a secondprice mechanism and additive valuations for bundles, and presents an algorithm that yields a 1/2- approximation of the revenue from the optimal partition.
Position Ranking and Auctions for Online Marketplaces
Online e-commerce platforms such as Amazon and Taobao connect thousands of sellers and consumers every day. In this work, we study how such platforms should rank products displayed to consumers, and
Boosted Second Price Auctions: Revenue Optimization for Heterogeneous Bidders
The second price auction has been the prevalent auction format used by advertising exchanges because of its simplicity and desirable incentive properties. However, even with an optimized choice of
Allocating online advertisement space with unreliable estimates
TLDR
The problem of optimally allocating online advertisement space to budget-constrained advertisers is studied and an algorithm that takes advantage of the given estimates of the frequencies of keywords to compute a near optimal solution when the estimates are accurate, while at the same time maintaining a good worst-case competitive ratio.
Optimal Dynamic Mechanism Design and the Virtual-Pivot Mechanism
TLDR
The virtual-pivot mechanism is presented, which is optimal in a large class of environments that satisfy a separability condition and has a very simple structure (a virtual index) in multiarmed bandit settings.
Approximating nash equilibria using small-support strategies
TLDR
It is shown that for any 0<ε<1, there is no 1<over>1 + ε - approximate equilibrium with strategies of support <i>O</i>(log<i>n</i><over>ε<sup>2</sup>).
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