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06/15/2023
Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
High-content cellular imaging, transcriptomics, and proteomics data prov...
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11/07/2022
FED-CD: Federated Causal Discovery from Interventional and Observational Data
Causal discovery, the inference of causal relations from data, is a core...
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10/31/2022
CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data
Mapping biological mechanisms in cellular systems is a fundamental step ...
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10/25/2022
From Points to Functions: Infinite-dimensional Representations in Diffusion Models
Diffusion-based generative models learn to iteratively transfer unstruct...
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06/15/2022
Diffusion Models for Video Prediction and Infilling
To predict and anticipate future outcomes or reason about missing inform...
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04/20/2022
Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database
Federated learning (FL) has been proposed as a method to train a model o...
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01/15/2022
Physical Derivatives: Computing policy gradients by physical forward-propagation
Model-free and model-based reinforcement learning are two ends of a spec...
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10/29/2021
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
Out-of-distribution (OOD) detection is a well-studied topic in supervise...
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10/22/2021
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
In vitro cellular experimentation with genetic interventions, using for ...
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07/08/2021
Federated Learning as a Mean-Field Game
We establish a connection between federated learning, a concept from mac...
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05/29/2021
Representation Learning in Continuous-Time Score-Based Generative Models
Score-based methods represented as stochastic differential equations on ...
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03/24/2021
Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
Simulating the spread of infectious diseases in human communities is cri...
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10/07/2020
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
Humans show an innate ability to learn the regularities of the world thr...
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08/31/2020
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease c...
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08/23/2020
Learning Dynamical Systems using Local Stability Priors
A coupled computational approach to simultaneously learn a vector field ...
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06/17/2020
Artificial Buildings: Safety, Complexity and a Quantifiable Measure of Beauty
A place to live is one of the most crucial necessities for all living or...
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06/06/2020
Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems
Learning controllers merely based on a performance metric has been prove...
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10/29/2019
Kernel-Guided Training of Implicit Generative Models with Stability Guarantees
Modern implicit generative models such as generative adversarial network...
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10/27/2019
Dual IV: A Single Stage Instrumental Variable Regression
We present a novel single-stage procedure for instrumental variable (IV)...
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05/16/2019
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
We consider the problem of recovering a common latent source with indepe...
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01/26/2019
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces
Modern implicit generative models such as generative adversarial network...
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11/14/2018
Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems
Filtering is a general name for inferring the states of a dynamical syst...
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05/27/2018
A Local Information Criterion for Dynamical Systems
Encoding a sequence of observations is an essential task with many appli...
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05/23/2018
Distribution Aware Active Learning
Discriminative learning machines often need a large set of labeled sampl...
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05/21/2018
Deep Energy Estimator Networks
Density estimation is a fundamental problem in statistical learning. Thi...
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03/13/2018
Analysis of Nonautonomous Adversarial Systems
Generative adversarial networks are used to generate images but still th...
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11/08/2017
Fidelity-Weighted Learning
Training deep neural networks requires many training samples, but in pra...
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08/17/2017
Towards life cycle identification of malaria parasites using machine learning and Riemannian geometry
Malaria is a serious infectious disease that is responsible for over hal...
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05/21/2017