5 citations · 8 across the 4 of their papers we have counts for
7 papers
Bounds for Hardness Condensation in the Query Model
Chandrima Kayal, Rajat Mittal, Sai Soumya Nalli +4
For any Boolean function with a complexity measure having value , is it possible to restrict the function to variables while keeping t…
Approximate Degree Composition for Recursive Functions
Sourav Chakraborty, Chandrima Kayal, Rajat Mittal +2
Determining the approximate degree composition for Boolean functions remains a significant unsolved problem in Boolean function complexity. In recent decades, researchers have conc…
Relations between monotone complexity measures based on decision tree complexity
Farzan Byramji, Vatsal Jha, Chandrima Kayal +1
In a recent result, Knop, Lovett, McGuire and Yuan (STOC 2021) proved the log-rank conjecture for communication complexity, up to log n factor, for any Boolean function composed wi…
On the Composition of Randomized Query Complexity and Approximate Degree
Sourav Chakraborty, Chandrima Kayal, Rajat Mittal +3
For any Boolean functions and , the question whether , is known as the composition question for the randomized query complexity. Similarly,…
Improving Zero-Shot Detection of Low Prevalence Chest Pathologies using Domain Pre-trained Language Models
Aakash Mishra, Rajat Mittal, Christy Jestin +2
Recent advances in zero-shot learning have enabled the use of paired image-text data to replace structured labels, replacing the need for expert annotated datasets. Models such as…
Certificate Games and Consequences for the Classical Adversary Bound
Sourav Chakraborty, Anna Gál, Mika Göös +3
We introduce and study Certificate Game complexity, a measure of complexity based on the probability of winning a game where two players are given inputs with different function va…
A Joint Learning Approach for Semi-supervised Neural Topic Modeling
Jeffrey Chiu, Rajat Mittal, Neehal Tumma +2
Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding varia…