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Found 22 papers, 14 papers with code
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce.
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms.
BatchGFN: Generative Flow Networks for Batch Active Learning
We introduce BatchGFN -- a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points proportional to a batch reward.
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
We show that the additive term is bounded proportional to the Lipschitz constant of the value function, which offers theoretical grounding for spectral normalization of critic weights.
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
There has been recent progress on robust and efficient methods for estimating the conditional average treatment effect (CATE) function, but these methods often do not take into account the risk of hidden confounding, which could arbitrarily and unknowingly bias any causal estimate based on observational data.
Differentiable Multi-Target Causal Bayesian Experimental Design
We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky.
Using uncertainty-aware machine learning models to study aerosol-cloud interactions
Aerosol-cloud interactions (ACI) include various effects that result from aerosols entering a cloud, and affecting cloud properties.
Partial Identification of Dose Responses with Hidden Confounders
Sensitivity analyses provide principled ways to give bounds on causal estimates when confounding variables are hidden.
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics.
Interventions, Where and How? Experimental Design for Causal Models at Scale
Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target.
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
We introduce causal, Bayesian acquisition functions grounded in information theory that bias data acquisition towards regions with overlapping support to maximize sample efficiency for learning personalized treatment effects.
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters a cloud and acts as cloud condensation nuclei (CCN).
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
We examine a simple stochastic strategy for adapting well-known single-point acquisition functions to allow batch active learning.
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
We study the problem of learning conditional average treatment effects (CATE) from high-dimensional, observational data with unobserved confounders.
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
Inducing point Gaussian process approximations are often considered a gold standard in uncertainty estimation since they retain many of the properties of the exact GP and scale to large datasets.
Variational Deterministic Uncertainty Quantification
Building on recent advances in uncertainty quantification using a single deep deterministic model (DUQ), we introduce variational Deterministic Uncertainty Quantification (vDUQ).
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
We show that our methods enable us to deal gracefully with situations of "no-overlap", common in high-dimensional data, where standard applications of causal effect approaches fail.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance.
Adversarially Learned Mixture Model
The Adversarially Learned Mixture Model (AMM) is a generative model for unsupervised or semi-supervised data clustering.
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification.
