no code implementations • 11 Apr 2024 • Haoyuan Sun, Ali Jadbabaie
The focus of this paper is on linear system identification in the setting where it is known that the underlying partially-observed linear dynamical system lies within a finite collection of known candidate models.
no code implementations • 15 Oct 2023 • Haoyuan Sun, Navid Azizan, Akash Srivastava, Hao Wang
When machine learning models are trained on synthetic data and then deployed on real data, there is often a performance drop due to the distribution shift between synthetic and real data.
no code implementations • 24 Jun 2023 • Haoyuan Sun, Khashayar Gatmiry, Kwangjun Ahn, Navid Azizan
However, the implicit regularization of different algorithms are confined to either a specific geometry or a particular class of learning problems, indicating a gap in a general approach for controlling the implicit regularization.
no code implementations • 21 Mar 2023 • Devansh Jalota, Haoyuan Sun, Navid Azizan
In this incomplete information setting, we consider the online learning problem of learning equilibrium prices over time while jointly optimizing three performance metrics -- unmet demand, cost regret, and payment regret -- pertinent in the context of equilibrium pricing over a horizon of $T$ periods.
no code implementations • 25 May 2022 • Haoyuan Sun, Kwangjun Ahn, Christos Thrampoulidis, Navid Azizan
Driven by the empirical success and wide use of deep neural networks, understanding the generalization performance of overparameterized models has become an increasingly popular question.
no code implementations • 18 Dec 2019 • Pablo Moscato, Haoyuan Sun, Mohammad Nazmul Haque
We present an approach for regression problems that employs analytic continued fractions as a novel representation.
no code implementations • NeurIPS 2019 • Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman
We prove a new lower bound on the competitive ratio of any online algorithm in the setting where the costs are $m$-strongly convex and the movement costs are the squared $\ell_2$ norm.