Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks160 from public data
- 383
- Active Learning from Crowds356
A probabilistic model for learning from multiple annotators that can also learn the annotator expertise even when their expertise may not be consistently accurate across the task domain is employed.
- 245
- 211
- 198
- Hybrid Heterogeneous Transfer Learning through Deep Learning197
A deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross- domain correspondences.
- Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification161
This work proposes a new margin-based min-max surrogate loss function for the AUC score that is more robust than the commonly used AUC square loss, while enjoying the same advantage in terms of large-scale stochastic optimization.
- A Unified Analysis of Stochastic Momentum Methods for Deep Learning143
The stability analysis shows that the momentum term can improve the stability of the learned model and hence improve the generalization performance through the uniform stability approach.
- 143
- Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: The Benefit of Target Expectation Maximization127
This paper proposes an attention alignment scheme on all the target convolutional layers to uncover the knowledge shared by the source domain, and estimates the posterior label distribution of the unlabeled data for target network training.
- 116
- 106
- Robust Semi-Supervised Learning through Label Aggregation58
This paper proposes an efficient RObust Semi-Supervised Ensemble Learning (ROSSEL) method, which generates pseudo-labels for unlabeled data using a set of weak annotators, and combines them to approximate the ground-truth labels to assist semi-supervised learning.
- Dynamic Time Warping Based Adversarial Framework for Time-Series Domain54
A novel framework for the time-series domain referred as Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure is proposed and a principled algorithm justified by theoretical analysis is developed to efficiently create diverse adversarial examples using random alignment paths.
- Graph-based clustering and ranking for diversified image search44
A novel ranking framework, namely cluster-constrained conditional Markov random walk (CCCMRW), which has two key steps: first, cluster images into topics, and then perform Markovrandom walk in an image graph conditioned on constraints of image cluster information is proposed.
- 41
- 41
- Active Learning from Multiple Knowledge Sources40
This work focuses on maximizing the information that an annotator label provides about the true (but unknown) label of the data point and proposes an AL approach for this new scenario motivated by information theoretic principles.
- 39
- On the Faithfulness of Vision Transformer Explanations35
This work introduces Salience-guided Faithfulness Coefficient (SaCo), a novel evaluation metric leveraging essential in-formation of salience distribution and demonstrates that the use of gradient and multi-layer aggregation can markedly enhance the faithfulness of attention-based explanation, shedding light on potential paths for advancing Vision Transformer explainability.
- Probabilistically robust conformal prediction35
A novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage is proposed to determine two parallel thresholds, one for data samples and another one for the perturbations on data (aka"quantile-of-quantile"design).
- Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn34
A next generation of talent search paradigm at LinkedIn: Search by Ideal Candidates, which only needs the searcher to input one or several examples of suitable candidates for the position and dynamically adjusts the ranking results balancing between the roles of query and ideal candidates.
- 32
- Multiple graph unsupervised feature selection32
The results support that the multiple graph could be better than the single graph in the unsupervised feature selection, and the overall performance of the proposed method is higher than the other comparisons.
- Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation30
This work introduces a Conditional Discrete Contrastive Diffusion (CDCD) loss and design two contrastive diffusion mechanisms to effectively incorporate it into the denoising process, and forms CDCD by connecting it with the conventional variational objectives.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.