Vedant Nanda

Vedant Nanda
Indraprastha Institute of Information Technology | IIITD · Department of Computer Science

Bachelor of Technology

About

12
Publications
1,801
Reads
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40
Citations
Citations since 2016
12 Research Items
40 Citations
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Publications

Publications (12)
Preprint
Full-text available
A major challenge in studying robustness in deep learning is defining the set of ``meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work on robustness implicitly uses a human as the reference model to define such perturbations. Our work offers a new view on robustness by using another reference NN to define...
Preprint
Online bipartite-matching platforms are ubiquitous and find applications in important areas such as crowdsourcing and ridesharing. In the most general form, the platform consists of three entities: two sides to be matched and a platform operator that decides the matching. The design of algorithms for such platforms has traditionally focused on the...
Preprint
Full-text available
We argue that a valuable perspective on when a model learns \textit{good} representations is that inputs that are mapped to similar representations by the model should be perceived similarly by humans. We use \textit{representation inversion} to generate multiple inputs that map to the same model representation, then quantify the perceptual similar...
Preprint
As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes settings (such as facial recognition and medical imaging). To respond to these concerns, the community has proposed and formalized various notions of fairness as well as metho...
Preprint
As deep neural networks (DNNs) get adopted in an ever-increasing number of applications, explainability has emerged as a crucial desideratum for these models. In many real-world tasks, one of the principal reasons for requiring explainability is to in turn assess prediction robustness, where predictions (i.e., class labels) that do not conform to t...
Preprint
Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population. We take a broader perspective on algorithmic fairness. We...
Conference Paper
Full-text available
Selfies have become a prominent medium for self-portrayal on social media. Unfortunately, certain social media users go to extreme lengths to click selfies, which puts their lives at risk. Two hundred and sixteen individuals have died since March 2014 until January 2018 while trying to click selfies. It is imperative to be able to identify dangerou...
Article
Full-text available
In this paper we analyze Facebook’s Free Basics program, which provides free Internet access to a restricted set of web services. As the program grows to 60+ developing countries, an independent and data-driven audit of its scope and outreach is highly relevant to the ICTD community. We provide the first large scale empirical observations on how co...
Conference Paper
Full-text available
In this paper we analyze Facebook's Free Basics program, which provides free Internet access to a restricted set of web services. As the program grows to 60+ developing countries, an independent and data-driven audit of its scope and outreach is highly relevant to the ICTD community. We provide the first large scale empirical observations on how co...
Article
Full-text available
In this paper we analyze Facebook’s Free Basics program, which provides free Internet access to a restricted set of web services. As the program grows to 60+ developing countries, an independent and data-driven audit of its scope and outreach is highly relevant to the ICTD community. We provide the first large scale empirical observations on how co...

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