Search Results for author: Saba Ahmadi

Found 10 papers, 3 papers with code

An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics

1 code implementation24 May 2023 Saba Ahmadi, Aishwarya Agrawal

Furthermore, we found that all metrics are sensitive to variations in the size of image-relevant objects mentioned in the caption, while CLIPScore and PAC-S are also sensitive to the number of mentions of image-relevant objects in the caption.

Image Captioning Negation +2

Agnostic Multi-Robust Learning Using ERM

no code implementations15 Mar 2023 Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin Stangl

A fundamental problem in robust learning is asymmetry: a learner needs to correctly classify every one of exponentially-many perturbations that an adversary might make to a test-time natural example.

Image Classification

Fundamental Bounds on Online Strategic Classification

no code implementations23 Feb 2023 Saba Ahmadi, Avrim Blum, Kunhe Yang

For instance, whereas in the non-strategic case, a mistake bound of $\ln|H|$ is achievable via the halving algorithm when the target function belongs to a known class $H$, we show that no deterministic algorithm can achieve a mistake bound $o(\Delta)$ in the strategic setting, where $\Delta$ is the maximum degree of the manipulation graph (even when $|H|=O(\Delta)$).

Binary Classification Classification

Individual Preference Stability for Clustering

1 code implementation7 Jul 2022 Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian

In this paper, we propose a natural notion of individual preference (IP) stability for clustering, which asks that every data point, on average, is closer to the points in its own cluster than to the points in any other cluster.

Clustering Fairness

Setting Fair Incentives to Maximize Improvement

no code implementations28 Feb 2022 Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita

A key technical challenge of this problem is the non-monotonicity of social welfare in the set of target levels, i. e., adding a new target level may decrease the total amount of improvement as it may get easier for some agents to improve.

Fairness

On classification of strategic agents who can both game and improve

no code implementations28 Feb 2022 Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita

For the general discrete model, we give an efficient algorithm for the problem of maximizing the number of true positives subject to no false positives, and show how to extend this to a partial-information learning setting.

The Strategic Perceptron

no code implementations4 Aug 2020 Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita

The classical Perceptron algorithm provides a simple and elegant procedure for learning a linear classifier.

Position

Fair Correlation Clustering

no code implementations10 Feb 2020 Saba Ahmadi, Sainyam Galhotra, Barna Saha, Roy Schwartz

We consider two variations of fairness constraint for the problem of correlation clustering where each node has a color, and the goal is to form clusters that do not over-represent vertices of any color.

Clustering Fairness

An Algorithm for Multi-Attribute Diverse Matching

no code implementations7 Sep 2019 Saba Ahmadi, Faez Ahmed, John P. Dickerson, Mark Fuge, Samir Khuller

Bipartite b-matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas such as healthcare, advertising, education, and general resource allocation.

Attribute

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