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Found 19 papers, 8 papers with code
Feedback Efficient Online Fine-Tuning of Diffusion Models
It is natural to frame this as a reinforcement learning (RL) problem, in which the objective is to fine-tune a diffusion model to maximize a reward function that corresponds to some property.
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins.
Toward the Identifiability of Comparative Deep Generative Models
Finally, we introduce a novel methodology for fitting comparative DGMs that improves the treatment of multiple data sources via multi-objective optimization and that helps adjust the hyperparameter for the regularization in an interpretable manner, using constrained optimization.
Conformalized Deep Splines for Optimal and Efficient Prediction Sets
SPICE is compatible with two different efficient-to-compute conformal scores, one oracle-optimal for marginal coverage (SPICE-ND) and the other asymptotically optimal for conditional coverage (SPICE-HPD).
Towards Understanding and Improving GFlowNet Training
We investigate how to learn better flows, and propose (i) prioritized replay training of high-reward $x$, (ii) relative edge flow policy parametrization, and (iii) a novel guided trajectory balance objective, and show how it can solve a substructure credit assignment problem.
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control
How much explicit guidance is necessary for conditional diffusion?
Parameter Averaging for Feature Ranking
We conduct extensive experiments on a variety of synthetic and real-world data, demonstrating that the XTab can be used to obtain the global feature importance that is not sensitive to sub-optimal model initialisation.
MoReL: Multi-omics Relational Learning
Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems.
Bayesian Graph Contrastive Learning
By learning distributional representations, we provide uncertainty estimates in downstream graph analytics tasks and increase the expressive power of the predictive model.
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
Self-supervised learning has been shown to be very effective in learning useful representations, and yet much of the success is achieved in data types such as images, audio, and text.
Ranked #4 on
Unsupervised MNIST
on MNIST
Bayesian Relational Generative Model for Scalable Multi-modal Learning
First, they are not stochastic processes, leading to poor uncertainty estimations over their predictions.
SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data
We develop a new method -- SimCD -- that explicitly models cell heterogeneity and dynamic differential changes in one unified hierarchical gamma-negative binomial (hGNB) model, allowing simultaneous cell clustering and differential expression analysis for scRNA-seq data.
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale.
Bayesian Graph Neural Networks with Adaptive Connection Sampling
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs.
Semi-Implicit Stochastic Recurrent Neural Networks
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models.
Variational Graph Recurrent Neural Networks
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant.
Ranked #2 on
Dynamic Link Prediction
on DBLP Temporal
Semi-Implicit Graph Variational Auto-Encoders
Compared to VGAE, the derived graph latent representations by SIG-VAE are more interpretable, due to more expressive generative model and more faithful inference enabled by the flexible semi-implicit construction.
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Second, compared to the number of involved molecules and system complexity, the number of available samples for studying complex disease, such as cancer, is often limited, especially considering disease heterogeneity.
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain
Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments.
