Search Results for author: Rad Niazadeh

Found 8 papers, 0 papers with code

Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization

no code implementations18 Feb 2021 Rad Niazadeh, Negin Golrezaei, Joshua Wang, Fransisca Susan, Ashwinkumar Badanidiyuru

We leverage this notion to transform greedy robust offline algorithms into a $O(T^{2/3})$ (approximate) regret in the bandit setting.

Decision Making Management

Fair Dynamic Rationing

no code implementations2 Feb 2021 Vahideh Manshadi, Rad Niazadeh, Scott Rodilitz

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

no code implementations7 Aug 2018 Moses Charikar, Vaggos Chatziafratis, Rad Niazadeh

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.

Clustering Open-Ended Question Answering

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization

no code implementations NeurIPS 2018 Rad Niazadeh, Tim Roughgarden, Joshua R. Wang

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.

BIG-bench Machine Learning

Hierarchical Clustering with Structural Constraints

no code implementations ICML 2018 Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar

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.


Multi-scale Online Learning and its Applications to Online Auctions

no code implementations26 May 2017 Sébastien Bubeck, Nikhil R. Devanur, Zhiyi Huang, Rad Niazadeh

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

no code implementations1 Mar 2017 Shuchi Chawla, Nikhil Devanur, Janardhan Kulkarni, Rad Niazadeh

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

no code implementations13 Jun 2010 Rad Niazadeh, Masoud Babaie-Zadeh, Christian Jutten

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.


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