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Found 24 papers, 6 papers with code
Using Bayesian Dynamical Systems for Motion Template Libraries
Motor primitives or motion templates have become an important concept for both modeling human motor control as well as generating robot behaviors using imitation learning.
Movement extraction by detecting dynamics switches and repetitions
Many time-series such as human movement data consist of a sequence of basic actions, e. g., forehands and backhands in tennis.
Recurrent Environment Simulators
Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently.
Path-Specific Counterfactual Fairness
We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways.
Causal Reasoning from Meta-reinforcement Learning
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents.
Degenerate Feedback Loops in Recommender Systems
Machine learning is used extensively in recommender systems deployed in products.
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class.
A Causal Bayesian Networks Viewpoint on Fairness
We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios.
Unsupervised Separation of Dynamics from Pixels
We present an approach to learn the dynamics of multiple objects from image sequences in an unsupervised way.
Wasserstein Fair Classification
We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances.
Explicit-Duration Markov Switching Models
Markov switching models (MSMs) are probabilistic models that employ multiple sets of parameters to describe different dynamic regimes that a time series may exhibit at different periods of time.
Fairness in Machine Learning
Machine learning based systems are reaching society at large and in many aspects of everyday life.
Fairness with Continuous Optimal Transport
Whilst optimal transport (OT) is increasingly being recognized as a powerful and flexible approach for dealing with fairness issues, current OT fairness methods are confined to the use of discrete OT.
Prequential MDL for Causal Structure Learning with Neural Networks
Learning the structure of Bayesian networks and causal relationships from observations is a common goal in several areas of science and technology.
Statistical discrimination in learning agents
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics.
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits
This paper considers the problem of selecting a formula for identifying a causal quantity of interest among a set of available formulas.
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings.
Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness
In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects.
Learning to Induce Causal Structure
The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data.
DiscoGen: Learning to Discover Gene Regulatory Networks
To accurately identify GRNs, perturbational data is required.
Constrained Causal Bayesian Optimization
We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints.
Functional Causal Bayesian Optimization
We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph.
Additive Causal Bandits with Unknown Graph
We explore algorithms to select actions in the causal bandit setting where the learner can choose to intervene on a set of random variables related by a causal graph, and the learner sequentially chooses interventions and observes a sample from the interventional distribution.
