Distributionally robust optimization with decision-dependent information discovery

(*) Q. Jin, A. Georghiou, P. Vayanos, G. Hanasusanto

Under review at Mathematical Programming Special Issue on "Stochastic programming and distributionally robust optimization with decision-dependent uncertainty", March 2024.

Learning optimal classification trees robust to distribution shifts

(*) N. Justin, (*) S. Aghaei, A. Gómez, P. Vayanos

Major revision at Operations Research, April 2024

Learning optimal and fair policies for online allocation of scarce societal resources from data collected in deployment

(*) B. Tang, Ç. Koçyiğit, E. Rice, P. Vayanos

Revise and resubmit at Management Science, March 2024

Highlighted as "Committee's Choice" presentation at INFORMS Annual Meeting 2021

ODTlearn: a package for learning optimal decision trees for prediction and prescription

(*) P. Vossler, (*) S. Aghaei, (*) N. Justin, (*) N. Jo, A. Gómez, P. Vayanos.

Revise and resubmit at Journal of Machine Learning Research, July 2023.

Learning optimal prescriptive trees from observational data

(*) N. Jo, (*) S. Aghaei, A. Gómez, P. Vayanos

Under second round of review at Management Science (after major revision), June 2023

Earned Nathan Jo the USC Discovery Scholar distinction

Earned Nathan Jo the USC university-wide Discovery Scholar Prize Competition

INFORMS Undergraduate Operations Research Prize Award 2021, Finalist

Robust active preference elicitation

(previous title: Active preference elicitation via adjustable robust optimization)

P. Vayanos, (*) Y. Ye, (*) D. McElfresh, J. Dickerson, E. Rice

Under major revision at Management Science (second round), July 2022.

Robust optimization with decision-dependent information discovery

P. Vayanos, A. Georghiou, (*) H. Yu

Under major revision at Management Science (third round), May 2023. 

Data-driven learning in dynamic pricing using adaptive optimization

D. Bertsimas and P. Vayanos 

Working Paper, 2017.

Strong optimal classification trees

(*) S. Aghaei, A. Gómez, P. Vayanos

Accepted for publication at Operations Research, October 2023.

ROC++: Robust Optimization in C++

P. Vayanos, (*) Q. Jin, G. Elissaios

INFORMS Journal on Computing, 34(6):2873-2888, 2022.

note: featured article

A community-partnered approach to social network data collection for a large and partial network

M. Izenberg, R. Brown, C. Siebert, R. Heinz, (*) A. Rahmattalabi, P. Vayanos

Field Methods, 35(2), 2022.

Cost-sharing mechanism design for ride-sharing

S. Hu, M.M. Dessouky, N.A. Uhan, P. Vayanos. 

Transportation Research Part B, June 2021.

Associations between the Vulnerability Index-Service Prioritization Decision Assistance Tool and returns to homelessness among single adults in the United States

L. Petry, H.-T. Hsu, C. Hill, M. Morton, P. Vayanos, E. Rice

Cityscape, 23(2), 293-324, 2021.

Correlates of housing sustainability among youth placed into permanent supportive housing and rapid re-housing: a survival analysis

 H.-T. Hsu, C. Hill, M. Holguin, L. Petry, (*) D. McElfresh, P. Vayanos, M. Morton, E. Rice

Journal of Adolescent Health, March 2021.

Understanding wait times in rapid rehousing among homeless youth: a competing risk survival analysis

H.-T.Hsu, E. Rice, J. Wilson, S. Semborski, P. Vayanos, M. Morton

The Journal of Primary Prevention, 2019.

Linking homelessness vulnerability assessments to housing placements and outcomes for youth

E. Rice, M. Holguin, H.-T.Hsu, M. Morton, P. Vayanos, M. Tambe, and (*) H. Chan

Cityscape, 20(3), Office of Policy Development and Research (PD&R) of the US Department of Housing and Urban Development (HUD), 2018.

Robust multiclass queuing theory for wait time estimation in resource allocation systems

C. Bandi, N. Trichakis and P. Vayanos

Management Science, 65(1), pp. 152-187, 2018.

Chance-constrained optimization for refinery blend planning under uncertainty

Y. Yang, P. Vayanos, and P.I. Barton

Industrial & Engineering Chemistry Research, 56 (42), pp. 12139–12150, 2017.

A constraint sampling approach for multi-stage robust optimization

P. Vayanos, D. Kuhn and B. Rustem

Automatica, 48(3):459-471, 2012.

Characterisation of signal modality: exploiting signal nonlinearity in machine learning and signal processing

B. Jelfs, S. Javidi, P. Vayanos, and D.P. Mandic

Journal of Signal Processing Systems, 61(1):105-115, 2009.

Online detection of the modality of complex-valued real world signals

D.P. Mandic, P. Vayanos, M. Chen, and S.L. Goh

International Journal of Neural Systems, 18(2):67-74, 2008.

Robust multi-stakeholder preference elicitation and aggregation

(*) C. Johnston, (*) S. Blessenhohl, P. Vayanos

In preparation for submission to Operations Research, 2023

Conserving biodiversity via adjustable robust optimization

(*) Y. Ye, (*) C. Doehring, A. Georghiou, H. Robinson, P. Vayanos

In preparation for submission to Management Science, 2023

ExplOR: Bringing STEM Education to Underserved Communities

(*) C. Johnston, (*) A. Rahmattalabi, (*) B. Tang, (*) N. Justin, P. Vayanos. 

In preparation for submission to INFORMS Transactions in Education, 2023

Highlighted as "Committee's Choice" presentation at INFORMS Annual Meeting 2021

Note: acceptance rates for these conferences are in the order of 25% or less

Learning fair policies for multi-stage selection problems from observational data

(*) Z. Jia, G.A. Hanasusanto, P. Vayanos, W. Xie

Accepted for publication in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'24), 2023.

Deploying a robust active preference elicitation algorithm: experiment design, interface, and evaluation for COVID-19 Patient Prioritization

(*) C.M. Johnston, (*) P. Vossler, (*) S. Blessenohl, P. Vayanos

Accepted for publication in Proceedings of the ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO'23), 2023.

note: acceptance rate ~18% in year of submission

Learning optimal fair classification trees: trade-offs between fairness, interpretability, and accuracy

(*) N. Jo, (*) S. Aghaei, (*) J. Benson, A. Gómez, P. Vayanos

In Proceedings of AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2023

note: acceptance rate 28.9% in year of submission

Fairness in contextual resource allocation systems: metrics and incompatibility results

(*) N. Jo,  (*) B. Tang, (*) K. Dullerud, (*) S. Aghaei, E. Rice, P. Vayanos

In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023

Nathan Jo and Bill Tang contributed equally (joint first authors)

note: acceptance rate ~19.6% in year of submission

Learning resource allocation policies from observational data with an application to homeless services delivery

(*) A. Rahmattalabi, P. Vayanos, (*) K. Dullerud, E. Rice.

ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022

Highlighted as "Committee's Choice" presentation at INFORMS Annual Meeting 2021

note: acceptance rate 25.2% in year of submission

Fair influence maximization: a welfare optimization approach

(*) A. Rahmattalabi, S. Jabbari, H. Lakkaraju, P. Vayanos, M. Izenberg, R. Brown, E. Rice, M. Tambe.

In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021.

note: acceptance rate ~21% in year of submission

Exploiting bounded rationality in risk-based cyber camouflage games

(*) O. Thakoor, S. Jabbari, P. Aggarwal, C. Gonzales, M. Tambe, P. Vayanos.

In Proceedings of the 11th International Conference, GameSec, 2020.

Best Paper Award at GameSec 2020

Exploring algorithmic fairness in robust graph covering problems 

(*) A. Rahmattalabi, P. Vayanos, A. Fulginiti, E. Rice, B. Wilder, A. Yadav, M. Tambe

In Proceedings of the 33rd Conference on Neural Information Processing Systems, NeurIPS, 2019.

note: acceptance rate ~21% in year of submission

The street-level realities of data practices in homeless services provision

(*) N. Karusala, (*) J. Wilson, P. Vayanos, E. Rice

In Proceedings of the 22nd ACM on Human-Computer Interaction 3, CSCW, 2019.

Cyber camouflage games for strategic deception

(*) O. Thakoor, M. Tambe, P. Vayanos, H. Xu, C. Kiekintveld, F. Fang

In Proceedings of the 10th International Conference, GameSec, 2019.

Learning optimal and fair decision trees for non-discriminative decision-making

(*) S. Aghaei, (*) M.J. Azizi, P. Vayanos

In Proceedings of 33rd AAAI Conference on Artificial Intelligence, 2019.

note: acceptance rate ~16% in year of submission

From empirical analysis to public policy: evaluating housing systems for homeless youth

(*) H. Chan, E. Rice, P. Vayanos, M. Tambe, and M. Morton

In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2018.

The price of usability: designing operationalizable strategies for security games

(**) S. M. Mc Carthy, (*) C. M. Laan, K. Wang, P. Vayanos, A. Sinha, and M. Tambe

In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI), 2018.

Designing fair, efficient, and interpretable policies for prioritizing homeless youth for housing resources

(*) M. J. Azizi, P. Vayanos, B. Wilder, E. Rice and M. Tambe

In Proceedings of the 15th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2018.

Invited to Constraints journal fast track for outstanding papers.

Deceiving cyber adversaries: a game theoretic approach

A. Schlenker, M. Tambe, L. Tran-Thanh, P. Vayanos, Y. Vorobeychik, O. Thakoor, H. Xu, and F. Fang

In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018.

Equilibrium refinement in security games with arbitrary scheduling constraints

K. Wang, Q. Guo, P. Vayanos, M. Tambe, and B. An

In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018.

Strategic coordination of human patrollers and mobile sensors with signaling for security games

H. Xu, K. Wang, P. Vayanos, M. Tambe

In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018.

Evidence from the past: AI decision aids to improve housing systems for homeless youth 

(*) H. Chan, E. Rice, P. Vayanos, M. Tambe, and M. Morton

In Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI) 2017 Fall Symposium Series, 2017.

Explanations systems for influential maximizations algorithms

A. Yadav, A. Rahmattalabi, E. Kamar, P. Vayanos, M. Tambe, V. L. Noronha

In Proceedings of the 3rd International Workshop on Social Influence Analysis, 2017.

Staying ahead of the game: adaptive robust optimization for dynamic allocation of threat screening resources

(**) S.M. Mc Carthy, P. Vayanos and M. Tambe

In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), pp. 3770-3776, 2017.

Decision rules for information discovery in multi-stage stochastic programming

P. Vayanos, D. Kuhn, and B. Rustem

In Proceedings of the 50th IEEE Conference on Decision and Control, pp. 7368-7373, 2011.

Hedging electricity swing options in incomplete markets

P. Vayanos, W. Wiesemann, and D. Kuhn

In Proceedings of the 18th IFAC World Congress, pp.846-853, 2011.

Online tracking of the degree of nonlinearity within complex signals

D.P. Mandic, P. Vayanos, S. Javidi, B. Jelfs, and K. Aihara

In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2061-2064, 2008.

Collaborative adaptive learning using hybrid filters

D.P. Mandic, P. Vayanos, C. Boukis, B. Jelfs, S.L. Goh, T. Gautama, and T. Rutkowski

In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 3:921-924, 2007.

Online detection of the nature of complex-valued signals

P. Vayanos, S.L. Goh, and D.P. Mandic

In Proceedings of the 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, pp. 173-178, 2006.

An online method for detecting nonlinearity within a signal

B. Jelfs, P. Vayanos, M. Chen, S.L. Goh, C. Boukis, T. Gautama, T.M. Rutkowski, T. Kuh, and D.P. Mandic

In Proceedings of the 10th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems, 4253:1216-1223, 2006.

Conserving biodiversity via adjustable robust optimization

(*) Y. Ye, (*) C. Doehring, A. Georghiou, H. Robinson, P. Vayanos

International Conference on Autonomous Agents and Multiagent Systems (AAMAS) Workshop on Autonomous Agents for Social Good, 2022.

Learning optimal prescriptive trees from observational data

(*) N. Jo, (*) S. Aghaei, A. Gómez, P. Vayanos

AAAI Workshop on AI for Behavior Change, 2022

Optimal robust classification trees

(*) N. Justin, (*) S. Aghaei, A. Gómez, P. Vayanos

AAAI Workshop on Adversarial Machine Learning and Beyond, 2022

Preference elicitation and aggregation to aid with patient triage during the COVID-19 pandemic

(*) C. Johnston, (*) S. Blessenhohl, P. Vayanos

International Conference on Machine Learning (ICML) Workshop on Participatory Approaches to Machine Learning, 2020.

Preference elicitation and aggregation to aid with patient triage during the COVID-19 pandemic

(*) C. Johnston, (*) S. Blessenhohl, P. Vayanos

Harvard CRCS Workshop on AI for Social Good, 2020.

Fairness in public health preventative interventions

(*) A. Rahmattalabi, S. Jabbari, H. Lakkaraju, P. Vayanos, M. Tambe

In 34th AAAI Conference on Artificial Intelligence (AAAI), Health Intelligence Workshop, 2020. 

Robust Active Preference Elicitation

(*) D. McElfresh, P. Vayanos, J. Dickerson, E. Rice

Revenue Management & Pricing Conference, 2019. 

Evidence from the past: AI decision aids to improve housing systems for homeless youth

(*) H. Chan, E. Rice, P. Vayanos, M. Tambe, and M. Morton

In Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI) 2017 Fall Symposium Series, 2017. 

Explanation systems for influence maximization algorithms

A. Yadav, (*) A. Rahmattalabi, E. Kamar, P. Vayanos, M. Tambe, and V.L. Noronha 

In Proceedings of the 3rd International Workshop on Social Influence Analysis, 2017. 

Optimal robust classification trees

(*) N. Justin, (*) S. Aghaei, A. Gómez, P. Vayanos

Extended abstract in Proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2022.

Learning optimal fair classification trees

(*) N. Jo, (*) S. Aghaei, (*) J. Benson, A. Gómez, P. Vayanos

Extended abstract in Proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2022.

Learning optimal classification trees: strong max-flow formulations

(*) S. Aghaei, A. Gomez, P. Vayanos

Extended abstract in Proceedings of the 17th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2020.

Robust peer-monitoring on graphs with an application to suicide prevention in social networks

(*) A. Rahmattalabi, P. Vayanos, A. Fulginiti, M. Tambe

Extended abstract at 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2019. 

General-sum cyber deception games under partial attacker valuation information

(*) O. Thakoor, M. Tambe, P. Vayanos, H. Xu, C. Kiekintveld

Extended abstract at 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2019. 

Collaborative adaptive filters for online knowledge extraction and information fusion

B. Jelfs, P. Vayanos, S. Javidi, S.L. Goh, and D.P. Mandic

Chapter in Signal Processing Techniques for Knowledge Extraction and Information Fusion, pp. 1-20. Springer, 2008.

Exploiting nonlinearity in adaptive signal processing

P. Vayanos, M. Chen, B. Jelfs, and D.P. Mandic Chapter in Advances in Nonlinear Speech Processing, volume 4885 of Lecture Notes in Computer Science, pp. 57-77. Springer, 2007.

Decision rule approximations for dynamic optimization under uncertainty, Imperial College London, 2013.