10 citations · 23 across the 6 of their papers we have counts for
5 papers · 1 filter
Random Separating Hyperplane Theorem and Learning Polytopes
Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar
The Separating Hyperplane theorem is a fundamental result in Convex Geometry with myriad applications. Our first result, Random Separating Hyperplane Theorem (RSH), is a strengthen…
Rawlsian Fair Adaptation of Deep Learning Classifiers
Kulin Shah, Pooja Gupta, Amit Deshpande +1
Group-fairness in classification aims for equality of a predictive utility across different sensitive sub-populations, e.g., race or gender. Equality or near-equality constraints i…
Learning a Latent Simplex in Input-Sparsity Time
Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan +2
We consider the problem of learning a latent -vertex simplex , given access to , which can be viewed as a data matrix with …
Finding a latent k-simplex in O(k . nnz(data)) time via Subset Smoothing
Chiranjib Bhattacharyya, Ravindran Kannan
In this paper we show that a large class of Latent variable models, such as Mixed Membership Stochastic Block(MMSB) Models, Topic Models, and Adversarial Clustering, can be unified…
How Many Pairwise Preferences Do We Need to Rank A Graph Consistently?
Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya
We consider the problem of optimal recovery of true ranking of items from a randomly chosen subset of their pairwise preferences. It is well known that without any further assu…