Search Results for author:
Found 17 papers, 9 papers with code
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
However, a majority of generative modeling approaches are focused solely on the joint distribution $p(x)$ and utilize models where it is intractable to obtain the conditional distribution of some arbitrary subset of features $x_u$ given the rest of the observed covariates $x_o$: $p(x_u \mid x_o)$.
A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness
Medical records often consist of different modalities, such as images, text, and tabular information.
Anomaly Detection via Gumbel Noise Score Matching
We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data.
Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition
We develop novel methodology for active feature acquisition (AFA), the study of how to sequentially acquire a dynamic (on a per instance basis) subset of features that minimizes acquisition costs whilst still yielding accurate predictions.
Learning to Retrieve Videos by Asking Questions
To overcome this limitation, we propose a novel framework for Video Retrieval using Dialog (ViReD), which enables the user to interact with an AI agent via multiple rounds of dialog, where the user refines retrieved results by answering questions generated by an AI agent.
Transparent Single-Cell Set Classification with Kernel Mean Embeddings
Each profiled biological sample is thus represented by a set of hundreds of thousands of multidimensional cell feature vectors, which incurs a high computational cost to predict each biological sample's associated phenotype with machine learning models.
Practical Integration via Separable Bijective Networks
Neural networks have enabled learning over examples that contain thousands of dimensions.
Hypothesis Driven Coordinate Ascent for Reinforcement Learning
This work develops a novel black box optimization technique for learning robust policies for stochastic environments.
Arbitrary Conditional Distributions with Energy
A more general and useful problem is arbitrary conditional density estimation, which aims to model any possible conditional distribution over a set of covariates, reflecting the more realistic setting of inference based on prior knowledge.
Multiscale Score Matching for Out-of-Distribution Detection
We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales.
Deep Message Passing on Sets
DMPS not only connects learning on graphs with learning on sets via deep kernel learning, but it also bridges message passing on sets and traditional diffusion dynamics commonly used in denoising models.
Meta-Neighborhoods
Making an adaptive prediction based on one's input is an important ability for general artificial intelligence.
MolecularRNN: Generating realistic molecular graphs with optimized properties
Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development.
Ranked #1 on
Molecular Graph Generation
on MOSES
Exchangeable Generative Models with Flow Scans
In this work, we develop a new approach to generative density estimation for exchangeable, non-i. i. d.
Estimating Cosmological Parameters from the Dark Matter Distribution
A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.
Bayesian Nonparametric Kernel-Learning
In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels.
Fast Function to Function Regression
Function to function regression (FFR) covers a large range of interesting applications including time-series prediction problems, and also more general tasks like studying a mapping between two separate types of distributions.
