We focus on the design and analysis of machine learning and optimization algorithms. The two topics are very related: Machine learning is just a form of stochastic optimization. In particular, we focus on online learning, empirical processes, and stochastic algorithms for convex and non-convex domains.
PhD: We currently do not plan to hire more PhD students in the lab. Also, for lack of time, it is difficult to answer to the inquiries for PhD positions.
09/25/20 One NeurIPS'20 paper accepted!
06/27/20 One ICML'20 Workshop on Beyond First Order Methods in ML Systems paper accepted!
05/19/20 Francesco Orabona and Ashok Cutkosky will give a tutorial at ICML'20 on Parameter-free Online Optimization
01/24/20 One ICASSP'20 paper accepted!
10/02/19 One NeurIPS'19 Workshop on Meta-Learning paper accepted!
10/01/19 One NeurIPS'19 Workshop on Privacy in Machine Learning paper accepted!
09/03/19 Two NeurIPS'19 papers accepted!
07/27/19 NSF founded our project in collaboration with Omkant Pandey
04/21/19 One ICML'19 paper accepted!
04/18/19 One COLT'19 paper accepted!
12/22/18 One AISTATS'19 paper accepted!
09/01/18 We moved to Boston University
12/19/17 The website is up!
N. Campolongo and F. Orabona. Temporal Variability in Implicit Online Learning. NeurIPS 2020 [PDF]
X. Li and F. Orabona. A High Probability Analysis of Adaptive SGD with Momentum. ICML 2020 Workshop on Beyond First Order Methods in ML Systems. [PDF]
Z. Zhuang, Y. Wang, K. Yu, and S. Lu. No-regret Non-convex Online Meta-Learning. ICASSP 2020 [PDF]
K.-S. Jun and F. Orabona. Parameter-Free Locally Differentially Private Stochastic Subgradient Descent. NeurIPS 2019 Workshop on Privacy in Machine Learning [PDF]
Z. Zhuang, K. Yu, S. Lu, L. Glass, and Y. Wang. Online Meta-Learning on Non-convex Setting. NeurIPS 2019 Workshop on Meta-Learning [PDF]
A. Cutkosky and F. Orabona. Momentum-Based Variance Reduction in Non-Convex SGD. NeurIPS 2019 [PDF]
K.-S. Jun, A. Cutkosky, F. Orabona. Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. NeurIPS 2019 [PDF]
K.-S. Jun and F. Orabona. Parameter-free Online Convex Optimization with Sub-Exponential Noise. COLT 2019 [PDF]
X. Li and F. Orabona. On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes. AISTATS 2019 [PDF]
A. Cutkosky and F. Orabona. Black-Box Reductions for Parameter-free Online Learning in Banach Spaces. COLT 2018 [PDF]
F. Orabona and D. Pal. Scale-free Online Learning, Theoretical Computer Science, 716. 2018 [PDF]
Our research is founded by