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Found 9 papers, 0 papers with code
Misalignment, Learning, and Ranking: Harnessing Users Limited Attention
Each user selects an item by first considering a prefix window of these ranked items and then picking the highest preferred item in that window (and the platform observes its payoff for this item).
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization
We leverage this notion to transform greedy robust offline algorithms into a $O(T^{2/3})$ (approximate) regret in the bandit setting.
Fair Dynamic Rationing
For an arbitrarily correlated sequence of demands, we establish upper bounds on the expected minimum fill rate (ex-post fairness) and the minimum expected fill rate (ex-ante fairness) achievable by any policy.
Fairness
Computer Science and Game Theory
Data Structures and Algorithms
Hierarchical Clustering better than Average-Linkage
Hierarchical Clustering (HC) is a widely studied problem in exploratory data analysis, usually tackled by simple agglomerative procedures like average-linkage, single-linkage or complete-linkage.
Hierarchical Clustering with Structural Constraints
For many real-world applications, we would like to exploit prior information about the data that imposes constraints on the clustering hierarchy, and is not captured by the set of features available to the algorithm.
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Our main result is the first $\frac{1}{2}$-approximation algorithm for continuous submodular function maximization; this approximation factor of $\frac{1}{2}$ is the best possible for algorithms that only query the objective function at polynomially many points.
Multi-scale Online Learning and its Applications to Online Auctions
For the online posted pricing problem, we show regret bounds that scale with the best fixed price, rather than the range of the values.
Truth and Regret in Online Scheduling
The service provider's goal is to implement a truthful online mechanism for scheduling jobs so as to maximize the social welfare of the schedule.
On the Achievability of Cramér-Rao Bound In Noisy Compressed Sensing
Recently, it has been proved in Babadi et al. that in noisy compressed sensing, a joint typical estimator can asymptotically achieve the Cramer-Rao lower bound of the problem. To prove this result, this paper used a lemma, which is provided in Akcakaya et al, that comprises the main building block of the proof.
