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Found 14 papers, 1 papers with code
A Dynamic Algorithm for Weighted Submodular Cover Problem
We consider this problem in a dynamic setting where there are updates to our set $V$, in the form of insertions and deletions of elements from a ground set $\mathcal{V}$, and the goal is to maintain an approximately optimal solution with low query complexity per update.
Dueling Over Dessert, Mastering the Art of Repeated Cake Cutting
Alice cuts the cake at a point of her choice, while Bob chooses the left piece or the right piece, leaving the remainder for Alice.
Replication-proof Bandit Mechanism Design
We consider Bayesian agents who are unaware of ex-post realization of their own arms' mean rewards, which is the first to study Bayesian extension of Shin et al. (2022).
Fair Polylog-Approximate Low-Cost Hierarchical Clustering
Research in fair machine learning, and particularly clustering, has been crucial in recent years given the many ethical controversies that modern intelligent systems have posed.
Online Advertisements with LLMs: Opportunities and Challenges
This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems.
Regret Analysis of Repeated Delegated Choice
To mitigate this behavior, the principal announces an eligible set which screens out a certain set of solutions.
Dynamic Algorithms for Matroid Submodular Maximization
This is the first dynamic algorithm for the problem that has a query complexity independent of the size of ground set $V$.
Optimal Sparse Recovery with Decision Stumps
Though often used in practice for feature selection, the theoretical guarantees of these methods are not well understood.
Bandit Social Learning: Exploration under Myopic Behavior
We study social learning dynamics motivated by reviews on online platforms.
Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost
We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation.
Inverse Feature Learning: Feature learning based on Representation Learning of Error
This paper proposes inverse feature learning as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error representation approach.
Online Pandora's Boxes and Bandits
We consider online variations of the Pandora's box problem (Weitzman.
Affinity Clustering: Hierarchical Clustering at Scale
In particular, we propose affinity, a novel hierarchical clustering based on Boruvka's MST algorithm.
