no code implementations • 1 Mar 2023 • Zahra Ashktorab, Benjamin Hoover, Mayank Agarwal, Casey Dugan, Werner Geyer, Hao Bang Yang, Mikhail Yurochkin
While several toolkits exist to aid machine learning practitioners in addressing fairness issues, little is known about the strategies practitioners employ to evaluate model fairness and what factors influence their assessment, particularly in the context of text classification.
no code implementations • 11 Jan 2023 • Naveen Venkat, Mayank Agarwal, Maneesh Singh, Shubham Tulsiani
While this representation yields (coarsely) accurate images corresponding to novel viewpoints, the lack of geometric reasoning limits the quality of these outputs.
no code implementations • 10 Feb 2022 • Jiao Sun, Q. Vera Liao, Michael Muller, Mayank Agarwal, Stephanie Houde, Kartik Talamadupula, Justin D. Weisz
Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion.
no code implementations • NeurIPS 2021 • Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
Meta-learning algorithms are widely used for few-shot learning.
no code implementations • 11 Oct 2021 • Mayank Agarwal, Kartik Talamadupula, Fernando Martinez, Stephanie Houde, Michael Muller, John Richards, Steven I Ross, Justin D. Weisz
However, due to the paucity of parallel data in this domain, supervised techniques have only been applied to a limited set of popular programming languages.
1 code implementation • 27 Sep 2021 • Mayank Agarwal, Tathagata Chakraborti, Sachin Grover, Arunima Chaudhary
While India has been one of the hotspots of COVID-19, data about the pandemic from the country has proved to be largely inaccessible at scale.
no code implementations • 3 Mar 2021 • Mayank Agarwal, Tathagata Chakraborti, Quchen Fu, David Gros, Xi Victoria Lin, Jaron Maene, Kartik Talamadupula, Zhongwei Teng, Jules White
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line.
no code implementations • ICLR Workshop LLD 2019 • Sohini Upadhyay, Mikhail Yurochkin, Mayank Agarwal, Yasaman Khazaeni, DjallelBouneffouf
We formulate a new problem at the intersectionof semi-supervised learning and contextual bandits, motivated by several applications including clini-cal trials and ad recommendations.
1 code implementation • 22 Jul 2020 • Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Whitherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
1 code implementation • 31 Jan 2020 • Mayank Agarwal, Jorge J. Barroso, Tathagata Chakraborti, Eli M. Dow, Kshitij Fadnis, Borja Godoy, Madhavan Pallan, Kartik Talamadupula
This whitepaper reports on Project CLAI (Command Line AI), which aims to bring the power of AI to the command line interface (CLI).
1 code implementation • NeurIPS 2019 • Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.
no code implementations • 22 Jun 2019 • Sohini Upadhyay, Mayank Agarwal, Djallel Bounneffouf, Yasaman Khazaeni
Building multi-domain AI agents is a challenging task and an open problem in the area of AI.
1 code implementation • 28 May 2019 • Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
no code implementations • ICLR 2019 • Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, Yasaman Khazaeni
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
no code implementations • 28 Dec 2018 • Ankush Garg, Mayank Agarwal
Machine translation (MT) plays an important role in benefiting linguists, sociologists, computer scientists, etc.
1 code implementation • 28 Nov 2018 • Navaneet K L, Priyanka Mandikal, Mayank Agarwal, R. Venkatesh Babu
We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets.
1 code implementation • 20 Jul 2018 • Priyanka Mandikal, K L Navaneet, Mayank Agarwal, R. Venkatesh Babu
3D reconstruction from single view images is an ill-posed problem.