57 citations · 478 across the 75 of their papers we have counts for
9 papers · 1 filter
Dynamic Sampling from Graphical Models
Weiming Feng, Nisheeth K. Vishnoi, Yitong Yin
In this paper, we study the problem of sampling from a graphical model when the model itself is changing dynamically with time. This problem derives its interest from a variety of…
On the Number of Circuits in Regular Matroids (with Connections to Lattices and Codes)
Rohit Gurjar, Nisheeth K. Vishnoi
We show that for any regular matroid on elements and any , the number of -minimum circuits, or circuits whose size is at most an -multiple of the minimum size of…
Balanced News Using Constrained Bandit-based Personalization
Sayash Kapoor, Vijay Keswani, Nisheeth K. Vishnoi +1
We present a prototype for a news search engine that presents balanced viewpoints across liberal and conservative articles with the goal of de-polarizing content and allowing users…
Geodesic Convex Optimization: Differentiation on Manifolds, Geodesics, and Convexity
Nisheeth K. Vishnoi
Convex optimization is a vibrant and successful area due to the existence of a variety of efficient algorithms that leverage the rich structure provided by convexity. Convexity of…
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani +1
Developing classification algorithms that are fair with respect to sensitive attributes of the data has become an important problem due to the growing deployment of classification…
On Geodesically Convex Formulations for the Brascamp-Lieb Constant
Nisheeth K. Vishnoi, Ozan Yildiz
We consider two non-convex formulations for computing the optimal constant in the Brascamp-Lieb inequality corresponding to a given datum, and show that they are geodesically log-c…
An Algorithmic Framework to Control Bias in Bandit-based Personalization
L. Elisa Celis, Sayash Kapoor, Farnood Salehi +1
Personalization is pervasive in the online space as it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user. However, recent studi…
Fair and Diverse DPP-based Data Summarization
L. Elisa Celis, Vijay Keswani, Damian Straszak +3
Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization. However, recent studies have noted the occu…
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
Oren Mangoubi, Nisheeth K. Vishnoi
Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from high-dimensional distributions in Statistics and Machine learning. HMC is known to run very efficiently in…