252 citations · 3.3k across the 92 of their papers we have counts for
6 papers · 1 filter
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei, Jason D. Lee, Qiang Liu +1
Recent works have shown that on sufficiently over-parametrized neural nets, gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the…
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li +3
Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical understanding…
Optimal Design of Process Flexibility for General Production Systems
Xi Chen, Tengyu Ma, Jiawei Zhang +1
Process flexibility is widely adopted as an effective strategy for responding to uncertain demand. Many algorithms for constructing sparse flexibility designs with good theoretical…
Approximability of Discriminators Implies Diversity in GANs
Yu Bai, Tengyu Ma, Andrej Risteski
While Generative Adversarial Networks (GANs) have empirically produced impressive results on learning complex real-world distributions, recent works have shown that they suffer fro…
The Toybox Dataset of Egocentric Visual Object Transformations
Xiaohan Wang, Tengyu Ma, James Ainooson +4
In object recognition research, many commonly used datasets (e.g., ImageNet and similar) contain relatively sparse distributions of object instances and views, e.g., one might see…
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Mikhail Khodak, Nikunj Saunshi, Yingyu Liang +3
Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textua…