57 citations · 301 across the 27 of their papers we have counts for
13 papers · 1 filter
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…