no code implementations • 2 May 2024 • Bingshan Hu, Zhiming Huang, Tianyue H. Zhang, Mathias Lécuyer, Nidhi Hegde
We study Thompson Sampling-based algorithms for stochastic bandits with bounded rewards.
no code implementations • 25 Sep 2023 • Nidhi Hegde, Sujoy Paul, Gagan Madan, Gaurav Aggarwal
Recent document question answering models consist of two key components: the vision encoder, which captures layout and visual elements in images, and a Large Language Model (LLM) that helps contextualize questions to the image and supplements them with external world knowledge to generate accurate answers.
1 code implementation • 5 Feb 2023 • Akash Saravanan, Dhruv Mullick, Habibur Rahman, Nidhi Hegde
Our results show that FineDeb offers stronger debiasing in comparison to other methods which often result in models as biased as the original language model.
no code implementations • 12 Jul 2022 • Liam Peet-Pare, Nidhi Hegde, Alona Fyshe
Fairness researchers in machine learning (ML) have coalesced around several fairness criteria which provide formal definitions of what it means for an ML model to be fair.
no code implementations • ICLR 2022 • Kirby Banman, Liam Peet-Pare, Nidhi Hegde, Alona Fyshe, Martha White
In this work, we show that SGDm under covariate shift with a fixed step-size can be unstable and diverge.
no code implementations • SEMEVAL 2021 • Harshit Kumar, Jinang Shah, Nidhi Hegde, Priyanshu Gupta, Vaibhav Jindal, Ashutosh Modi
To tackle this issue of availability of annotated data, a lot of research has been done on unsupervised domain adaptation that tries to generate systems for an unlabelled target domain data, given labeled source domain data.
1 code implementation • 6 Feb 2020 • Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde
We consider two different kinds of mean field environments: a) Games where agents belong to predefined types that are known a priori and b) Games where the type of each agent is unknown and therefore must be learned based on observations.
1 code implementation • NeurIPS 2019 • Baoxiang Wang, Nidhi Hegde
Our aim is to protect the value function approximator, without regard to the number of states queried to the function.
1 code implementation • 30 Jan 2019 • Baoxiang Wang, Nidhi Hegde
Our aim is to protect the value function approximator, without regard to the number of states queried to the function.
no code implementations • NeurIPS 2017 • Fabio Cecchi, Nidhi Hegde
We consider the problem of active sequential hypothesis testing where a Bayesian decision maker must infer the true hypothesis from a set of hypotheses.
no code implementations • 13 Jul 2012 • Siddhartha Banerjee, Nidhi Hegde, Laurent Massoulié
In the information-rich regime, where each user rates at least a constant fraction of items, a spectral clustering approach is shown to achieve a sample-complexity lower bound derived from a simple information-theoretic argument based on Fano's inequality.