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Found 25 papers, 5 papers with code
Guarantee Regions for Local Explanations
Interpretability methods that utilise local surrogate models (e. g. LIME) are very good at describing the behaviour of the predictive model at a point of interest, but they are not guaranteed to extrapolate to the local region surrounding the point.
Bayesian Inverse Transition Learning for Offline Settings
Offline Reinforcement learning is commonly used for sequential decision-making in domains such as healthcare and education, where the rewards are known and the transition dynamics $T$ must be estimated on the basis of batch data.
Leveraging Factored Action Spaces for Off-Policy Evaluation
In this work, we propose a new family of "decomposed" importance sampling (IS) estimators based on factored action spaces.
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
We demonstrate that planning under a lower discount factor produces an identical optimal policy to planning using any prior on the transition matrix that has the same distribution for all states and actions.
Decision-Focused Model-based Reinforcement Learning for Reward Transfer
Decision-focused (DF) model-based reinforcement learning has recently been introduced as a powerful algorithm that can focus on learning the MDP dynamics that are most relevant for obtaining high returns.
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning
We find that DistDeD significantly improves over prior discovery approaches, providing indications of the risk 10 hours earlier on average as well as increasing detection by 20%.
Policy Optimization with Sparse Global Contrastive Explanations
We develop a Reinforcement Learning (RL) framework for improving an existing behavior policy via sparse, user-interpretable changes.
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making
A precise description of the causal estimand highlights which OPE estimands are identifiable from observational data under the stated generative assumptions.
Learning Conditional Invariance through Cycle Consistency
We address these shortcomings with a novel approach to cycle consistency.
On Learning Prediction-Focused Mixtures
Probabilistic models help us encode latent structures that both model the data and are ideally also useful for specific downstream tasks.
Learning Predictive and Interpretable Timeseries Summaries from ICU Data
Machine learning models that utilize patient data across time (rather than just the most recent measurements) have increased performance for many risk stratification tasks in the intensive care unit.
Learning-to-defer for sequential medical decision-making under uncertainty
Our deferral policy is adaptive to the non-stationarity in the dynamics.
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
Estimating an individual's potential response to interventions from observational data is of high practical relevance for many domains, such as healthcare, public policy or economics.
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 that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance.
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients.
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deployment in high stakes settings requires ways of assessing its validity.
Inverse Learning of Symmetries
Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser.
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Moreover, for situations in which a single, global tree is a poor estimator, we introduce a regional tree regularizer that encourages the deep model to resemble a compact, axis-aligned decision tree in predefined, human-interpretable contexts.
Regional Tree Regularization for Interpretability in Black Box Models
The lack of interpretability remains a barrier to the adoption of deep neural networks.
Estimating Causal Effects With Partial Covariates For Clinical Interpretability
Estimating the causal effects of an intervention in the presence of confounding is a frequently occurring problem in applications such as medicine.
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects.
Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates
Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding.
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
The lack of interpretability remains a key barrier to the adoption of deep models in many applications.
Greedy Structure Learning of Hierarchical Compositional Models
In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter.
Bayesian Markov Blanket Estimation
This paper considers a Bayesian view for estimating a sub-network in a Markov random field.
