Publications

Evolutionary Optimization of Cooperative Strategies for the Iterated Prisoner’s Dilemma

Published in IEEE Transactions on Games, 2020

Abstract: Abstract—The Iterated Prisoner’s Dilemma (IPD) has been studied in fields as diverse as economics, computer science, psychology, politics, and environmental studies. This is due, in part, to the intriguing property that its Nash Equilibrium is not globally optimal. Typically treated as a single-objective problem, a player’s goal is to maximize their own score. In some work, minimizing the opponent’s score is an additional objective. Here, we explore the role of explicitly optimizing for mutual cooperation in IPD player performance. We implement a genetic algorithm in which each member of the population evolves using one of four multi-objective fitness functions: selfish, communal, cooperative, and selfless, the last three of which use a cooperative

Recommended citation: Jessica Finocchiaro, H. David Mathias. (2020). "Evolutionary Optimization of Cooperative Strategies for the Iterated Prisoner’s Dilemma"

Fairness and Discrimination in Mechanism Design and Machine Learning

Published in AI for Social Good (AI4SG) Workshop at Harvard CRCS 2020, 2020

As fairness and discrimination concerns permeate the design of both machine learning algorithms and mechanism design problems, we discuss differences in approaches between these two fields. We aim to bridge these two communities into a cohesive narrative that encompasses both the large-scale capabilities of machine learning and group-focused fairness as well as the strategic incentives and utility-based notions of fairness from mechanism de-sign, showing their necessity in designing a fair pipeline.

Recommended citation: Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtis, "Fairness and Discrimination in Mechanism Design and Machine Learning" (2020.)

Embedding Dimension of Polyhedral Losses

Published in Conference on Learning Theory (COLT) 2020, 2020

Abstract: A common technique in supervised learning with discrete losses, such as 0-1 loss, is to optimize a convex surrogate loss over R^d, calibrated with respect to the original loss. In particular, recent work has investigated embedding the original predictions (e.g. labels) as points in R^d, showing an equivalence to using polyhedral surrogates. In this work, we study the notion of the embedding dimension of a given discrete loss: the minimum dimension d such that an embedding exists. We characterize d-embeddability for all d, with a particularly tight characterization for d=1 (embedding into the real line), and useful necessary conditions for d>1 in the form of a quadratic feasibility program. We illustrate our results with novel lower bounds for abstain loss.

Recommended citation: Jessica Finocchiaro, Rafael Frongillo, and Bo Waggoner. (2020). "Embedding Dimension of Polyhedral Losses"

An Embedding Framework for Consistent Polyhedral Surrogates

Published in Neural Information Processing Systems (NeurIPS) 2019, 2019

Abstract: We formalize and study the natural approach of designing convex surrogate loss functions via embeddings for problems such as classification or ranking. In this approach, one embeds each of the finitely many predictions (e.g. classes) as a point in Rd, assigns the original loss values to these points, and convexifies the loss in between to obtain a surrogate. We prove that this approach is equivalent, in a strong sense, to working with polyhedral (piecewise linear convex) losses. Moreover, given any polyhedral loss L, we give a construction of a link function through which L is a consistent surrogate for the loss it embeds. We go on to illustrate the power of this embedding framework with succinct proofs of consistency or inconsistency of various polyhedral surrogates in the literature.

Recommended citation: Jessica Finocchiaro, Rafael Frongillo, and Bo Waggoner. (2019). "An Embedding Framework for Consistent Polyhedral Surrogates" https://arxiv.org/abs/1907.07330

Convex Elicitation of Continuous Properties

Published in Neural Information Processing Systems, 2018

Abstract: A property or statistic of a distribution is said to be elicitable if it can be expressed as the minimizer of some loss function in expectation. Recent work shows that continuous real-valued properties are elicitable if and only if they are identifiable, meaning the set of distributions with the same property value can be described by linear constraints. From a practical standpoint, one may ask for which such properties do there exist convex loss functions. In this paper, in a finite-outcome setting, we show that in fact every elicitable real-valued property can be elicited by a convex loss function. Our proof is constructive, and leads to convex loss functions for new properties.

Recommended citation: Jessica Finocchiaro and Rafael Frongillo. (2018). "Convex Elicitation of Continuous Properties." Neural Information Processing Systems. https://papers.nips.cc/paper/8241-convex-elicitation-of-continuous-properties

Social Trends in the Iterated Prisoner’s Dilemma (Extended Abstract)

Published in Genetic and Evolutionary Computation Conference (GECCO) 2017, 2017

Abstract: In this paper, we utilize a multi-objective genetic algorithm (GA) to investigate the Iterated Prisoner’s Dilemma problem with a population of players that don’t have uniform objectives. Each of the members of our population has one of four objective pairs. We simulate a tournament similar to those in previous work to investigate patterns of convergence in objective pairs when they are free to change. We also consider the most successful objective pair within a population when members’ objective pairs are fixed.

Recommended citation: Jessica Finocchiaro, H. David Mathias. (2017). "Social Trends in the Iterated Prisoner's Dilemma." Genetic and Evolutionary Computation Conference (GECCO) 2017. https://dl.acm.org/citation.cfm?id=3082037

Investigating Social Trends in the Iterated Prisoner’s Dilemma

Published in Neural Information Processing Systems, 2017

In ethics, many academics make the assumption that all people want to be good. Evil comes in where there is a conflict of good decisions; where a decision that is good for one person contradicts the good of another. In this case, a person will make a different decision depending on their definition of the good they want to accomplish. In a society that starts with an equal proportion of selfishly good and selfessly good people, we aim to investigate the convergence of behavior through simulating the Iterated Prisoner’s Dilemma over time.

Recommended citation: Jessica Finocchiaro. (2017). "Investigating Social Trends in the Iterated Prisoner's Dilemma." Florida Southern College Honors College Bachelor's Thesis. https://repository.flsouthern.edu/bitstream/handle/11416/315/Finocchiaro_Jessica_S17.pdf?sequence=1

Egocentric Height Estimation

Published in Winter Conference on Applications in Computer Vision (WACV), 2017

Egocentric, or first-person vision which became popular in recent years with an emerge in wearable technology,is different than exocentric (third-person) vision in some distinguishable ways, one of which being that the camera-wearer is generally not visible in the video frames. Recent work has been done on action and object recognition in egocentric videos, as well as work on biometric extraction from first-person videos. Height estimation can be a useful feature for both soft-biometrics and object tracking. Here, we propose a method of estimating the height of an egocentric camera without any calibration or reference points. We used both traditional computer vision approaches and deep learning in order to determine the visual cues that results in best height estimation. Here, we introduce a framework inspired by two stream networks comprising of two Convolutional Neural Networks, one based on spatial information, and one based on information given by optical flow in a frame. Given an egocentric video as an input to the framework, our model yields a height estimate as an output. We also incorporate late fusion to learn a combination of temporal and spatial cues. Comparing our model with other methods we used as baselines, we achieve height estimates for videos with a Mean Average Error of 14.04 cm over a range of 103 cm of data, and classification accuracy for relative height (tall, medium or short) up to 93.75% where chance level is 33%.

Recommended citation: Jessica Finocchiaro, Aisha Urooj Khan, and Ali Borji. (2017). "Egocentric Height Estimation." IEEE Winter Conference on Applications in Computer Vision. https://arxiv.org/pdf/1610.02714.pdf