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Found 12 papers, 3 papers with code
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality
We demonstrate the effectiveness of our method on a local navigation task where a quadruped robot needs to reach the target within a finite horizon.
Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning
We introduce a simple but effective method for managing risk in model-based reinforcement learning with trajectory sampling that involves probabilistic safety constraints and balancing of optimism in the face of epistemic uncertainty and pessimism in the face of aleatoric uncertainty of an ensemble of stochastic neural networks. Various experiments indicate that the separation of uncertainties is essential to performing well with data-driven MPC approaches in uncertain and safety-critical control environments.
Diffusion Generative Inverse Design
Inverse design refers to the problem of optimizing the input of an objective function in order to enact a target outcome.
Diverse Offline Imitation Learning
There has been significant recent progress in the area of unsupervised skill discovery, utilizing various information-theoretic objectives as measures of diversity.
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
To avoid failures on out-of-distribution data, recent works have sought to extract features that have an invariant or stable relationship with the label across domains, discarding "spurious" or unstable features whose relationship with the label changes across domains.
Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions
Learning diverse skills is one of the main challenges in robotics.
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations
Learning agile skills is one of the main challenges in robotics.
Backpropagation through Combinatorial Algorithms: Identity with Projection Works
Embedding discrete solvers as differentiable layers has given modern deep learning architectures combinatorial expressivity and discrete reasoning capabilities.
Ranked #1 on
Density Estimation
on MNIST
Taming Continuous Posteriors for Latent Variational Dialogue Policies
Utilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optimizing dialogue success.
Neuro-algorithmic Policies enable Fast Combinatorial Generalization
Furthermore, we show that for a certain subclass of the MDP framework, this can be alleviated by neuro-algorithmic architectures.
Optimizing Rank-based Metrics with Blackbox Differentiation
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models.
Differentiation of Blackbox Combinatorial Solvers
Achieving fusion of deep learning with combinatorial algorithms promises transformative changes to artificial intelligence.
