80 citations · 343 across the 26 of their papers we have counts for
26 papers
Efficient Maximal Coding Rate Reduction by Variational Forms
Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan +3
The principle of Maximal Coding Rate Reduction (MCR) has recently been proposed as a training objective for learning discriminative low-dimensional structures intrinsic to high…
Predicting Out-of-Distribution Error with the Projection Norm
Yaodong Yu, Zitong Yang, Alexander Wei +2
We propose a metric -- Projection Norm -- to predict a model's performance on out-of-distribution (OOD) data without access to ground truth labels. Projection Norm first uses model…
Incremental Learning of Structured Memory via Closed-Loop Transcription
Shengbang Tong, Xili Dai, Ziyang Wu +3
This work proposes a minimal computational model for learning structured memories of multiple object classes in an incremental setting. Our approach is based on establishing a clos…
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
Xili Dai, Shengbang Tong, Mingyang Li +8
This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a closed-loop transcription…
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning
Yuexiang Zhai, Christina Baek, Zhengyuan Zhou +2
Many goal-reaching reinforcement learning (RL) tasks have empirically verified that rewarding the agent on subgoals improves convergence speed and practical performance. We attempt…
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging
Chris Junchi Li, Yaodong Yu, Nicolas Loizou +4
We study the stochastic bilinear minimax optimization problem, presenting an analysis of the same-sample Stochastic ExtraGradient (SEG) method with constant step size, and presenti…