3 citations · 7 across the 7 of their papers we have counts for
8 papers
Extremal Probability Bounds in Combinatorial Optimization
Divya Padmanabhan, Selin Damla Ahipasaoglu, Arjun Ramachandra +1
In this paper, we compute the tightest possible bounds on the probability that the optimal value of a combinatorial optimization problem in maximization form with a random objectiv…
Correlation Robust Influence Maximization
Louis Chen, Divya Padmanabhan, Chee Chin Lim +1
We propose a distributionally robust model for the influence maximization problem. Unlike the classic independent cascade model \citep{kempe2003maximizing}, this model's diffusion…
Tree Bounds for Sums of Bernoulli Random Variables: A Linear Optimization Approach
Divya Padmanabhan, Karthik Natarajan
We study the problem of computing the tightest upper and lower bounds on the probability that the sum of dependent Bernoulli random variables exceeds an integer . Under know…
Exploiting Partial Correlations in Distributionally Robust Optimization
Divya Padmanabhan, Karthik Natarajan, Karthyek R. A. Murthy
In this paper, we identify partial correlation information structures that allow for simpler reformulations in evaluating the maximum expected value of mixed integer linear program…
A Dominant Strategy Truthful, Deterministic Multi-Armed Bandit Mechanism with Logarithmic Regret
Divya Padmanabhan, Satyanath Bhat, Prabuchandran K. J. +2
Stochastic multi-armed bandit (MAB) mechanisms are widely used in sponsored search auctions, crowdsourcing, online procurement, etc. Existing stochastic MAB mechanisms with a deter…
Topic Model Based Multi-Label Classification from the Crowd
Divya Padmanabhan, Satyanath Bhat, Shirish Shevade +1
Multi-label classification is a common supervised machine learning problem where each instance is associated with multiple classes. The key challenge in this problem is learning th…
A Truthful Mechanism with Biparameter Learning for Online Crowdsourcing
Satyanath Bhat, Divya Padmanabhan, Shweta Jain +1
We study a problem of allocating divisible jobs, arriving online, to workers in a crowdsourcing setting which involves learning two parameters of strategically behaving workers. Ea…
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds
Divya Padmanabhan, Satyanath Bhat, Dinesh Garg +2
We study the problem of training an accurate linear regression model by procuring labels from multiple noisy crowd annotators, under a budget constraint. We propose a Bayesian mode…