papers

Publications (7)

cs.LG2020

Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users

Joshua Lockhart, Samuel Assefa, Tucker Balch +1

Document classification is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing feedback-retrain loop with the team that maintain it.…

q-fin.CP2019

On the Importance of Opponent Modeling in Auction Markets

Mahmoud Mahfouz, Angelos Filos, Cyrine Chtourou +5

The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions. D…

cs.AI2022

Simulation Intelligence: Towards a New Generation of Scientific Methods

Alexander Lavin, David Krakauer, Hector Zenil +21

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of comput…

cs.LG2021

Tradeoffs in Streaming Binary Classification under Limited Inspection Resources

Parisa Hassanzadeh, Danial Dervovic, Samuel Assefa +2

Institutions are increasingly relying on machine learning models to identify and alert on abnormal events, such as fraud, cyber attacks and system failures. These alerts often need…

stat.ML2021

Copula Flows for Synthetic Data Generation

Sanket Kamthe, Samuel Assefa, Marc Deisenroth

The ability to generate high-fidelity synthetic data is crucial when available (real) data is limited or where privacy and data protection standards allow only for limited use of t…

cs.AI2021

Non-Parametric Stochastic Sequential Assignment With Random Arrival Times

Danial Dervovic, Parisa Hassanzadeh, Samuel Assefa +1

We consider a problem wherein jobs arrive at random times and assume random values. Upon each job arrival, the decision-maker must decide immediately whether or not to accept the j…