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Found 14 papers, 3 papers with code
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.
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.
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.
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.
Large-Scale Markov Decision Problems via the Linear Programming Dual
Moreover, we propose an efficient algorithm, scaling with the size of the subspace but not the state space, that is able to find a policy with low excess loss relative to the best policy in this class.
Horizon-Independent Minimax Linear Regression
We consider online linear regression: at each round, an adversary reveals a covariate vector, the learner predicts a real value, the adversary reveals a label, and the learner suffers the squared prediction error.
Best Arm Identification for Contaminated Bandits
Specifically, we propose a variant of the Best Arm Identification problem for \emph{contaminated bandits}, where each arm pull has probability $\varepsilon$ of generating a sample from an arbitrary contamination distribution instead of the true underlying distribution.
Random Permutation Online Isotonic Regression
We revisit isotonic regression on linear orders, the problem of fitting monotonic functions to best explain the data, in an online setting.
Hit-and-Run for Sampling and Planning in Non-Convex Spaces
We propose the Hit-and-Run algorithm for planning and sampling problems in non-convex spaces.
Online Isotonic Regression
We then prove that the Exponential Weights algorithm played over a covering net of isotonic functions has a regret bounded by $O\big(T^{1/3} \log^{2/3}(T)\big)$ and present a matching $\Omega(T^{1/3})$ lower bound on regret.
Minimax Time Series Prediction
We consider an adversarial formulation of the problem ofpredicting a time series with square loss.
Efficient Minimax Strategies for Square Loss Games
We consider online prediction problems where the loss between the prediction and the outcome is measured by the squared Euclidean distance and its generalization, the squared Mahalanobis distance.
Linear Programming for Large-Scale Markov Decision Problems
We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost.
