Search Results for author: Bryan Wilder

Found 25 papers, 8 papers with code

Distributionally Robust Submodular Maximization

no code implementations14 Feb 2018 Matthew Staib, Bryan Wilder, Stefanie Jegelka

We also show compelling empirical evidence that DRO improves generalization to the unknown stochastic submodular function.

Algorithmic Social Intervention

no code implementations14 Mar 2018 Bryan Wilder

Social and behavioral interventions are a critical tool for governments and communities to tackle deep-rooted societal challenges such as homelessness, disease, and poverty.

Decision Making Decision Making Under Uncertainty

Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

no code implementations14 Sep 2018 Bryan Wilder, Bistra Dilkina, Milind Tambe

These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision.

Combinatorial Optimization

Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

no code implementations5 Feb 2019 Jackson A. Killian, Bryan Wilder, Amit Sharma, Daksha Shah, Vinod Choudhary, Bistra Dilkina, Milind Tambe

Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.

End-to-End Game-Focused Learning of Adversary Behavior in Security Games

no code implementations3 Mar 2019 Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, Milind Tambe

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary.

Group-Fairness in Influence Maximization

1 code implementation3 Mar 2019 Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, Yair Zick

Influence maximization is a widely used model for information dissemination in social networks.

Computer Science and Game Theory Social and Information Networks

SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

3 code implementations29 May 2019 Po-Wei Wang, Priya L. Donti, Bryan Wilder, Zico Kolter

We demonstrate that by integrating this solver into end-to-end learning systems, we can learn the logical structure of challenging problems in a minimally supervised fashion.

Game of Sudoku Logical Reasoning

End to end learning and optimization on graphs

1 code implementation NeurIPS 2019 Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe

However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization.

Link Prediction

Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling

1 code implementation8 Jul 2019 Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe

A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network.

Graph Sampling

MIPaaL: Mixed Integer Program as a Layer

no code implementations12 Jul 2019 Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe

It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization.

Decision Making

Learning to Complement Humans

no code implementations1 May 2020 Bryan Wilder, Eric Horvitz, Ece Kamar

A rising vision for AI in the open world centers on the development of systems that can complement humans for perceptual, diagnostic, and reasoning tasks.

BIG-bench Machine Learning Medical Diagnosis

Fuzzy c-Means Clustering for Persistence Diagrams

1 code implementation4 Jun 2020 Thomas Davies, Jack Aspinall, Bryan Wilder, Long Tran-Thanh

We end with experiments on two datasets that utilise both the topological and fuzzy nature of our algorithm: pre-trained model selection in machine learning and lattices structures from materials science.

BIG-bench Machine Learning Clustering +2

Automatically Learning Compact Quality-aware Surrogates for Optimization Problems

2 code implementations NeurIPS 2020 Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe

Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.

Portfolio Optimization

Tracking disease outbreaks from sparse data with Bayesian inference

no code implementations12 Sep 2020 Bryan Wilder, Michael J. Mina, Milind Tambe

For example, case counts may be sparse when only a small fraction of infections are caught by a testing program.

Bayesian Inference Epidemiology +1

End-to-End Constrained Optimization Learning: A Survey

no code implementations30 Mar 2021 James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Bryan Wilder

This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems.

BIG-bench Machine Learning

Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses

no code implementations30 Mar 2022 Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe

Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better on that specific task.

Decision Making

Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation

no code implementations6 Feb 2023 Aditya Mate, Bryan Wilder, Aparna Taneja, Milind Tambe

We consider the task of evaluating policies of algorithmic resource allocation through randomized controlled trials (RCTs).

counterfactual

Ideal Abstractions for Decision-Focused Learning

no code implementations29 Mar 2023 Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz

We present a methodology for formulating simplifying abstractions in machine learning systems by identifying and harnessing the utility structure of decisions.

Decision Making Management

Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize

no code implementations26 May 2023 Sanket Shah, Andrew Perrault, Bryan Wilder, Milind Tambe

In this paper, we propose solutions to these issues, avoiding the aforementioned assumptions and utilizing the ML model's features to increase the sample efficiency of learning loss functions.

Decision Making Decision Making Under Uncertainty

Auditing Fairness by Betting

1 code implementation NeurIPS 2023 Ben Chugg, Santiago Cortes-Gomez, Bryan Wilder, Aaditya Ramdas

Whereas previous work relies on a fixed-sample size, our methods are sequential and allow for the continuous monitoring of incoming data, making them highly amenable to tracking the fairness of real-world systems.

Fairness valid

Computationally Assisted Quality Control for Public Health Data Streams

2 code implementations29 Jun 2023 Ananya Joshi, Kathryn Mazaitis, Roni Rosenfeld, Bryan Wilder

However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams.

Decision Making Outlier Detection

Federated Epidemic Surveillance

no code implementations5 Jul 2023 Ruiqi Lyu, Bryan Wilder, Roni Rosenfeld

The surveillance of a pandemic is a challenging task, especially when crucial data is distributed and stakeholders cannot or are unwilling to share.

Outlier Ranking in Large-Scale Public Health Streams

no code implementations2 Jan 2024 Ananya Joshi, Tina Townes, Nolan Gormley, Luke Neureiter, Roni Rosenfeld, Bryan Wilder

Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks.

Outlier Detection

Auditing Fairness under Unobserved Confounding

no code implementations18 Mar 2024 Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder

A fundamental problem in decision-making systems is the presence of inequity across demographic lines.

Decision Making Fairness

Cannot find the paper you are looking for? You can Submit a new open access paper.