4 citations · 6 across the 9 of their papers we have counts for
9 papers · 1 filter
Adapting Prediction Sets to Distribution Shifts Without Labels
Kevin Kasa, Zhiyu Zhang, Heng Yang +1
Recently there has been a surge of interest to deploy confidence set predictions rather than point predictions in machine learning. Unfortunately, the effectiveness of such predict…
Discounted Adaptive Online Learning: Towards Better Regularization
Zhiyu Zhang, David Bombara, Heng Yang
We study online learning in adversarial nonstationary environments. Since the future can be very different from the past, a critical challenge is to gracefully forget the history w…
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook +1
Despite the success of the Adam optimizer in practice, the theoretical understanding of its algorithmic components still remains limited. In particular, most existing analyses of A…
Improving Adaptive Online Learning Using Refined Discretization
Zhiyu Zhang, Heng Yang, Ashok Cutkosky +1
We study unconstrained Online Linear Optimization with Lipschitz losses. Motivated by the pursuit of instance optimality, we propose a new algorithm that simultaneously achieves ($…
Unconstrained Dynamic Regret via Sparse Coding
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
Motivated by the challenge of nonstationarity in sequential decision making, we study Online Convex Optimization (OCO) under the coupling of two problem structures: the domain is u…
Optimal Comparator Adaptive Online Learning with Switching Cost
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
Practical online learning tasks are often naturally defined on unconstrained domains, where optimal algorithms for general convex losses are characterized by the notion of comparat…
PDE-Based Optimal Strategy for Unconstrained Online Learning
Zhiyu Zhang, Ashok Cutkosky, Ioannis Paschalidis
Unconstrained Online Linear Optimization (OLO) is a practical problem setting to study the training of machine learning models. Existing works proposed a number of potential-based…
Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
We consider the problem of tracking an adversarial state sequence in a linear dynamical system subject to adversarial disturbances and loss functions, generalizing earlier settings…
Provable Hierarchical Imitation Learning via EM
Zhiyu Zhang, Ioannis Paschalidis
Due to recent empirical successes, the options framework for hierarchical reinforcement learning is gaining increasing popularity. Rather than learning from rewards which suffers f…