research
∙
08/09/2023
Bayesian Inverse Transition Learning for Offline Settings
Offline Reinforcement learning is commonly used for sequential decision-...
share
research
∙
07/13/2023
Leveraging Factored Action Spaces for Off-Policy Evaluation
Off-policy evaluation (OPE) aims to estimate the benefit of following a ...
research
∙
06/20/2023
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
Discount regularization, using a shorter planning horizon when calculati...
research
∙
04/06/2023
Robust Decision-Focused Learning for Reward Transfer
Decision-focused (DF) model-based reinforcement learning has recently be...
research
∙
01/13/2023
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning
In safety-critical decision-making scenarios being able to identify wors...
research
∙
07/13/2022
Policy Optimization with Sparse Global Contrastive Explanations
We develop a Reinforcement Learning (RL) framework for improving an exis...
research
∙
01/20/2022
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making
Assessing the effects of a policy based on observational data from a dif...
research
∙
11/25/2021
Learning Conditional Invariance through Cycle Consistency
Identifying meaningful and independent factors of variation in a dataset...
research
∙
10/25/2021
On Learning Prediction-Focused Mixtures
Probabilistic models help us encode latent structures that both model th...
research
∙
09/22/2021
Learning Predictive and Interpretable Timeseries Summaries from ICU Data
Machine learning models that utilize patient data across time (rather th...
research
∙
09/13/2021
Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty
We propose SLTD (`Sequential Learning-to-Defer') a framework for learnin...
research
∙
03/20/2021
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
Estimating an individual's potential response to interventions from obse...
research
∙
01/13/2021
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible
We propose Preferential MoE, a novel human-ML mixture-of-experts model t...
research
∙
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...
research
∙
02/07/2020
Inverse Learning of Symmetry Transformations
Symmetry transformations induce invariances and are a crucial building b...
research
∙
08/14/2019
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Deep models have advanced prediction in many domains, but their lack of ...
research
∙
08/13/2019
Regional Tree Regularization for Interpretability in Black Box Models
The lack of interpretability remains a barrier to the adoption of deep n...
research
∙
11/26/2018
Estimating Causal Effects With Partial Covariates For Clinical Interpretability
Estimating the causal effects of an intervention in the presence of conf...
research
∙
11/19/2018
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Computer vision tasks are difficult because of the large variability in ...
research
∙
07/06/2018
Causal Deep Information Bottleneck
Estimating causal effects in the presence of latent confounding is a fre...
research
∙
11/16/2017
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
The lack of interpretability remains a key barrier to the adoption of de...
research
∙
10/06/2015
