Held in Naha, Okinawa, Japan, the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) draws researchers from all over the world to present their latest findings in artificial intelligence, machine learning, statistics, and related areas. The Machine Learning Center at Georgia Tech (ML@GT) researchers will present 12 papers at the 2019 conference, held April 16-18.
“AISTATS is an exciting conference that allows for engaging conversations and interactions at the intersection of machine learning and statistics. ML@GT is thrilled to be a part of this growing conference and we are looking forward to connecting with other researchers from around the world,” said Sebastian Pokutta, associate director of ML@GT and a paper author.
ML@GT faculty members Le Song, Byron Boots, and Negar Kiyavash are 2019 area chairs.
Georgia Tech’s twelve papers:
- Nearly Optimal Adaptive Procedure for Piecewise-Stationary Bandit: a Change-Point Detection Approach
- Robust Submodular Maximization: Offline and Online Algorithms
- Restarting Frank-Wolfe
- Truncated Back-propagation for Bilevel Optimization
- Accelerating Imitation Learning with Predictive Models
- Risk-Averse Stochastic Convex Bandit
- Differentially Private Online Submodular Minimization
- Structured Robust Submodular Maximization: Offline and Online Algorithms
- Kernel Exponential Family Estimation via Doubly Dual Embedding
- Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization
- Database Alignment with Gaussian Features
- On Landscape of Lagrangian Function for Stochastic Search for Constrained Nonconvex Optimization