57 citations · 301 across the 27 of their papers we have counts for
27 papers
Semi-supervised Domain Adaptive Structure Learning
Can Qin, Lichen Wang, Qianqian Ma +3
Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift ac…
Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles
Bram Wallace, Devansh Arpit, Huan Wang +1
Pretraining convolutional neural networks via self-supervision, and applying them in transfer learning, is an incredibly fast-growing field that is rapidly and iteratively improvin…
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization
Devansh Arpit, Huan Wang, Yingbo Zhou +1
In Domain Generalization (DG) settings, models trained independently on a given set of training domains have notoriously chaotic performance on distribution shifted test domains, a…
Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE
Devansh Arpit, Aadyot Bhatnagar, Huan Wang +1
Wasserstein autoencoder (WAE) shows that matching two distributions is equivalent to minimizing a simple autoencoder (AE) loss under the constraint that the latent space of this AE…
Multi-Tensor Network Representation for High-Order Tensor Completion
Chang Nie, Huan Wang, Zhihui Lai
This work studies the problem of high-dimensional data (referred to as tensors) completion from partially observed samplings. We consider that a tensor is a superposition of multip…
Merlion: A Machine Learning Library for Time Series
Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu +20
We introduce Merlion, an open-source machine learning library for time series. It features a unified interface for many commonly used models and datasets for anomaly detection and…
Polyjuice: High-Performance Transactions via Learned Concurrency Control
Jiachen Wang, Ding Ding, Huan Wang +4
Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, opt…
Understanding the Under-Coverage Bias in Uncertainty Estimation
Yu Bai, Song Mei, Huan Wang +1
Estimating the data uncertainty in regression tasks is often done by learning a quantile function or a prediction interval of the true label conditioned on the input. It is frequen…
Evaluating State-of-the-Art Classification Models Against Bayes Optimality
Ryan Theisen, Huan Wang, Lav R. Varshney +2
Evaluating the inherent difficulty of a given data-driven classification problem is important for establishing absolute benchmarks and evaluating progress in the field. To this end…
Rethinking Adam: A Twofold Exponential Moving Average Approach
Yizhou Wang, Yue Kang, Can Qin +4
Adaptive gradient methods, e.g. \textsc{Adam}, have achieved tremendous success in machine learning. Scaling the learning rate element-wisely by a certain form of second moment est…
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
Tengyang Xie, Nan Jiang, Huan Wang +2
Recent theoretical work studies sample-efficient reinforcement learning (RL) extensively in two settings: learning interactively in the environment (online RL), or learning from an…
Dynamical Isometry: The Missing Ingredient for Neural Network Pruning
Huan Wang, Can Qin, Yue Bai +1
Several recent works [40, 24] observed an interesting phenomenon in neural network pruning: A larger finetuning learning rate can improve the final performance significantly. Unfor…
Local Calibration: Metrics and Recalibration
Rachel Luo, Aadyot Bhatnagar, Yu Bai +7
Probabilistic classifiers output confidence scores along with their predictions, and these confidence scores should be calibrated, i.e., they should reflect the reliability of the…
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand +6
The early phase of training a deep neural network has a dramatic effect on the local curvature of the loss function. For instance, using a small learning rate does not guarantee st…
Neural Pruning via Growing Regularization
Huan Wang, Can Qin, Yulun Zhang +1
Regularization has long been utilized to learn sparsity in deep neural network pruning. However, its role is mainly explored in the small penalty strength regime. In this work, we…
Unsupervised Paraphrasing with Pretrained Language Models
Tong Niu, Semih Yavuz, Yingbo Zhou +3
Paraphrase generation has benefited extensively from recent progress in the designing of training objectives and model architectures. However, previous explorations have largely fo…
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Minshuo Chen, Yu Bai, Jason D. Lee +4
Deep neural networks can empirically perform efficient hierarchical learning, in which the layers learn useful representations of the data. However, how they make use of the interm…
MNN: A Universal and Efficient Inference Engine
Xiaotang Jiang, Huan Wang, Yiliu Chen +9
Deploying deep learning models on mobile devices draws more and more attention recently. However, designing an efficient inference engine on devices is under the great challenges o…
Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width
Yu Bai, Ben Krause, Huan Wang +2
We propose \emph{Taylorized training} as an initiative towards better understanding neural network training at finite width. Taylorized training involves training the -th order…
Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning
Devansh Arpit, Huan Wang, Caiming Xiong +2
We introduce a parameterization method called Neural Bayes which allows computing statistical quantities that are in general difficult to compute and opens avenues for formulating…
Contradictory Structure Learning for Semi-supervised Domain Adaptation
Can Qin, Lichen Wang, Qianqian Ma +3
Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias…
Global Capacity Measures for Deep ReLU Networks via Path Sampling
Ryan Theisen, Jason M. Klusowski, Huan Wang +3
Classical results on the statistical complexity of linear models have commonly identified the norm of the weights as a fundamental capacity measure. Generalizations of this…
On the Generalization Gap in Reparameterizable Reinforcement Learning
Huan Wang, Stephan Zheng, Caiming Xiong +1
Understanding generalization in reinforcement learning (RL) is a significant challenge, as many common assumptions of traditional supervised learning theory do not apply. We focus…
Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model
Xu Liu, Xiaoqiang Di, Jinqing Li +4
Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially…