Search Results for author: Haoyuan Sun

Found 6 papers, 0 papers with code

Private Synthetic Data Meets Ensemble Learning

no code implementations15 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.

Ensemble Learning

A Unified Approach to Controlling Implicit Regularization via Mirror Descent

no code implementations24 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.

Classification regression

Online Learning for Equilibrium Pricing in Markets under Incomplete Information

no code implementations21 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.

Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently

no code implementations25 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.

Open-Ended Question Answering

Analytic Continued Fractions for Regression: A Memetic Algorithm Approach

no code implementations18 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.

BIG-bench Machine Learning regression +1

Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization

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.

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