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Found 14 papers, 3 papers with code
Inverse Reinforcement Learning with Multiple Planning Horizons
In this work, we study an inverse reinforcement learning (IRL) problem where the experts are planning under a shared reward function but with different, unknown planning horizons.
Contextual Conservative Q-Learning for Offline Reinforcement Learning
In this paper, we propose Contextual Conservative Q-Learning(C-CQL) to learn a robustly reliable policy through the contextual information captured via an inverse dynamics model.
Deep Semi-supervised Learning with Double-Contrast of Features and Semantics
Therefore, a more realistic strategy is to leverage semi-supervised learning (SSL) with a small amount of labeled data and a large amount of unlabeled data.
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Comparing Bayesian neural networks (BNNs) with different widths is challenging because, as the width increases, multiple model properties change simultaneously, and, inference in the finite-width case is intractable.
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data.
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.
Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts
Self-supervised learning (SSL), as a newly emerging unsupervised representation learning paradigm, generally follows a two-stage learning pipeline: 1) learning invariant and discriminative representations with auto-annotation pretext(s), then 2) transferring the representations to assist downstream task(s).
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
CFQI uses a compositional $Q$-value function with separate modules for each task variant, allowing it to take advantage of shared knowledge while learning distinct policies for each variant.
Power Constrained Bandits
However, when bandits are deployed in the context of a scientific study -- e. g. a clinical trial to test if a mobile health intervention is effective -- the aim is not only to personalize for an individual, but also to determine, with sufficient statistical power, whether or not the system's intervention is effective.
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Bayesian Neural Networks (BNNs) place priors over the parameters in a neural network.
Output-Constrained Bayesian Neural Networks
Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space.
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
As machine learning systems get widely adopted for high-stake decisions, quantifying uncertainty over predictions becomes crucial.
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Bayesian Neural Networks (BNNs) have recently received increasing attention for their ability to provide well-calibrated posterior uncertainties.
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare.
