Search Results for author: Alan Malek

Found 13 papers, 1 papers with code

Constrained Causal Bayesian Optimization

no code implementations31 May 2023 Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa

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.

Pragmatic Fairness: Developing Policies with Outcome Disparity Control

1 code implementation28 Jan 2023 Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva

We introduce a causal framework for designing optimal policies that satisfy fairness constraints.


Asymptotically Best Causal Effect Identification with Multi-Armed Bandits

no code implementations NeurIPS 2021 Alan Malek, Silvia Chiappa

This paper considers the problem of selecting a formula for identifying a causal quantity of interest among a set of available formulas.

Multi-Armed Bandits

Prequential MDL for Causal Structure Learning with Neural Networks

no code implementations2 Jul 2021 Jorg Bornschein, Silvia Chiappa, Alan Malek, Rosemary Nan Ke

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

no code implementations6 Jan 2019 Yasin Abbasi-Yadkori, Peter L. Bartlett, Xi Chen, Alan Malek

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

no code implementations NeurIPS 2018 Alan Malek, Peter L. Bartlett

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

no code implementations26 Feb 2018 Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek

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.

Active Learning

Random Permutation Online Isotonic Regression

no code implementations NeurIPS 2017 Wojciech Kotlowski, Wouter M. Koolen, Alan Malek

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

no code implementations19 Oct 2016 Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek

We propose the Hit-and-Run algorithm for planning and sampling problems in non-convex spaces.

Online Isotonic Regression

no code implementations14 Mar 2016 Wojciech Kotłowski, Wouter M. Koolen, Alan Malek

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

no code implementations NeurIPS 2015 Wouter M. Koolen, Alan Malek, Peter L. Bartlett, Yasin Abbasi

We consider an adversarial formulation of the problem ofpredicting a time series with square loss.

Time Series Prediction

Efficient Minimax Strategies for Square Loss Games

no code implementations NeurIPS 2014 Wouter M. Koolen, Alan Malek, Peter L. Bartlett

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.

Density Estimation

Linear Programming for Large-Scale Markov Decision Problems

no code implementations27 Feb 2014 Yasin Abbasi-Yadkori, Peter L. Bartlett, Alan Malek

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost.

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