Search Results for author: Vivek F. Farias

Found 10 papers, 1 papers with code

Policy Optimization for Personalized Interventions in Behavioral Health

no code implementations21 Mar 2023 Jackie Baek, Justin J. Boutilier, Vivek F. Farias, Jonas Oddur Jonasson, Erez Yoeli

DecompPI is simple and easy to implement for organizations aiming to improve long-term behavior through targeted interventions, and this paper demonstrates its strong performance both theoretically and empirically.

Markovian Interference in Experiments

no code implementations6 Jun 2022 Vivek F. Farias, Andrew A. Li, Tianyi Peng, Andrew Zheng

We consider experiments in dynamical systems where interventions on some experimental units impact other units through a limiting constraint (such as a limited inventory).

Off-policy evaluation

Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise

no code implementations22 Oct 2021 Vivek F. Farias, Andrew A. Li, Tianyi Peng

The problem of low-rank matrix completion with heterogeneous and sub-exponential (as opposed to homogeneous and Gaussian) noise is particularly relevant to a number of applications in modern commerce.

Low-Rank Matrix Completion Uncertainty Quantification

Fair Exploration via Axiomatic Bargaining

no code implementations NeurIPS 2021 Jackie Baek, Vivek F. Farias

When patients are associated with natural groups on the basis of, say, race or age, it is natural to ask whether the cost of exploration borne by any single group is 'fair'.

Fairness Multi-Armed Bandits

Optimizing Offer Sets in Sub-Linear Time

no code implementations17 Nov 2020 Vivek F. Farias, Andrew A. Li, Deeksha Sinha

Personalization and recommendations are now accepted as core competencies in just about every online setting, ranging from media platforms to e-commerce to social networks.

Dimensionality Reduction

Fixing Inventory Inaccuracies At Scale

no code implementations23 Jun 2020 Vivek F. Farias, Andrew A. Li, Tianyi Peng

Using synthetic data and real data from a consumer goods retailer, we show that our approach provides up to a 10x cost reduction over incumbent approaches to anomaly detection.

Anomaly Detection Matrix Completion

TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation

no code implementations11 Jun 2020 Jackie Baek, Vivek F. Farias

The key algorithmic task for Thompson sampling is drawing a sample from the posterior of the optimal action.

Multi-Armed Bandits Thompson Sampling

The Limits to Learning a Diffusion Model

no code implementations11 Jun 2020 Jackie Baek, Vivek F. Farias, Andreea Georgescu, Retsef Levi, Tianyi Peng, Deeksha Sinha, Joshua Wilde, Andrew Zheng

In a similar vein, our results imply that in the case of an SIR model, one cannot hope to predict the eventual number of infections until one is approximately two-thirds of the way to the time at which the infection rate has peaked.

Decision Making

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