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Found 18 papers, 7 papers with code
Sanitized Clustering against Confounding Bias
Therein, the interested clustering factor and the confounding factor are coarsely considered in the raw feature space, where the correlation between the data and the confounding factor is ideally assumed to be linear for convenient solutions.
Earning Extra Performance from Restrictive Feedbacks
Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate.
Coarse-to-Fine Contrastive Learning on Graphs
Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner.
Taming Overconfident Prediction on Unlabeled Data from Hindsight
The prediction uncertainty is typically expressed as the \emph{entropy} computed by the transformed probabilities in output space.
TRIP: Refining Image-to-Image Translation via Rival Preferences
In particular, we simultaneously train two modules: a generator that translates an input image to the desired image with smooth subtle changes with respect to the interested attributes; and a ranker that ranks rival preferences consisting of the input image and the desired image.
Fine-Tuning from Limited Feedbacks
Instead of learning from scratch, fine-tuning a pre-trained model to fit a related target dataset of interest or downstream tasks has been a handy trick to achieve the desired performance.
Differential-Critic GAN: Generating What You Want by a Cue of Preferences
This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property.
Streamlining EM into Auto-Encoder Networks
We present a new deep neural network architecture, named EDGaM, for deep clustering.
Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation
In this paper, we attempt to learn both features of user preferences in a more principled way.
Learning Node Representations against Perturbations
In this paper, we study how to learn node representations against perturbations in GNN.
Multi-view Alignment and Generation in CCA via Consistent Latent Encoding
Multi-view alignment, achieving one-to-one correspondence of multi-view inputs, is critical in many real-world multi-view applications, especially for cross-view data analysis problems.
Secure Metric Learning via Differential Pairwise Privacy
This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning.
Domain-adversarial Network Alignment
Network alignment is a critical task to a wide variety of fields.
Probabilistic CCA with Implicit Distributions
Although some works have studied probabilistic interpretation for CCA, these models still require the explicit form of the distributions to achieve a tractable solution for the inference.
Fast and Robust Rank Aggregation against Model Misspecification
Specifically, the properties of our CoarsenRank are summarized as follows: (1) CoarsenRank is designed for mild model misspecification, which assumes there exist the ideal preferences (consistent with model assumption) that locates in a neighborhood of the actual preferences.
Mental Fatigue Monitoring using Brain Dynamics Preferences
Furthermore, a transition matrix is introduced to characterize the reliability of each channel used in EEG data, which helps in learning brain dynamics preferences only from informative EEG channels.
Canonical Correlation Analysis with Implicit Distributions
Based on this objective, we present an implicit probabilistic formulation for CCA, named Implicit CCA (ICCA), which provides a flexible framework to design CCA extensions with implicit distributions.
Label Embedding with Partial Heterogeneous Contexts
In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges.
