2 citations · 5 across the 8 of their papers we have counts for
8 papers
Lap2: Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory
Meisam Mohammady, Qin Yang, Nicholas Stout +4
Differentially Private Stochastic Gradient Descent (DP-SGD) is a cornerstone technique for ensuring privacy in deep learning, widely used in both training from scratch and fine-tun…
A Resilience Framework for Bi-Criteria Combinatorial Optimization with Bandit Feedback
Vaneet Aggarwal, Shweta Jain, Subham Pokhriyal +1
We study bi-criteria combinatorial optimization under noisy function evaluations. While resilience and black-box offline-to-online reductions have been studied in single-objective…
Stochastic -Submodular Bandits with Full Bandit Feedback
Guanyu Nie, Vaneet Aggarwal, Christopher John Quinn
In this paper, we present the first sublinear -regret bounds for online -submodular optimization problems with full-bandit feedback, where is a corresponding offline appr…
Combinatorial Stochastic-Greedy Bandit
Fares Fourati, Christopher John Quinn, Mohamed-Slim Alouini +1
We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for combinatorial multi-armed bandit problems when no extra information other than the joint reward of the…
A Unified Approach for Maximizing Continuous DR-submodular Functions
Mohammad Pedramfar, Christopher John Quinn, Vaneet Aggarwal
This paper presents a unified approach for maximizing continuous DR-submodular functions that encompasses a range of settings and oracle access types. Our approach includes a Frank…
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback
Fares Fourati, Vaneet Aggarwal, Christopher John Quinn +1
We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization. Previous works investiga…
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback
Guanyu Nie, Yididiya Y Nadew, Yanhui Zhu +2
We investigate the problem of stochastic, combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non-linear. We provi…
A Community-Aware Framework for Social Influence Maximization
Abhishek K. Umrawal, Christopher J. Quinn, Vaneet Aggarwal
We consider the problem of Influence Maximization (IM), the task of selecting seed nodes in a social network such that the expected number of nodes influenced is maximized. We…