Search Results for author: John P. Dickerson

Found 56 papers, 24 papers with code

RecRec: Algorithmic Recourse for Recommender Systems

no code implementations28 Aug 2023 Sahil Verma, Ashudeep Singh, Varich Boonsanong, John P. Dickerson, Chirag Shah

To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.

Recommendation Systems valid

Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook

no code implementations10 Mar 2023 Teresa Datta, John P. Dickerson

Deployed artificial intelligence (AI) often impacts humans, and there is no one-size-fits-all metric to evaluate these tools.

Explainable Artificial Intelligence (XAI)

Tensions Between the Proxies of Human Values in AI

no code implementations14 Dec 2022 Teresa Datta, Daniel Nissani, Max Cembalest, Akash Khanna, Haley Massa, John P. Dickerson

Motivated by mitigating potentially harmful impacts of technologies, the AI community has formulated and accepted mathematical definitions for certain pillars of accountability: e. g. privacy, fairness, and model transparency.


Networked Restless Bandits with Positive Externalities

1 code implementation9 Dec 2022 Christine Herlihy, John P. Dickerson

Restless multi-armed bandits are often used to model budget-constrained resource allocation tasks where receipt of the resource is associated with an increased probability of a favorable state transition.

Multi-Armed Bandits

Robustness Disparities in Face Detection

2 code implementations29 Nov 2022 Samuel Dooley, George Z. Wei, Tom Goldstein, John P. Dickerson

Many existing algorithmic audits examine the performance of these systems on later stage elements of facial analysis systems like facial recognition and age, emotion, or perceived gender prediction; however, a core component to these systems has been vastly understudied from a fairness perspective: face detection, sometimes called face localization.

Face Detection Fairness +1

RecXplainer: Amortized Attribute-based Personalized Explanations for Recommender Systems

no code implementations27 Nov 2022 Sahil Verma, Chirag Shah, John P. Dickerson, Anurag Beniwal, Narayanan Sadagopan, Arjun Seshadri

We evaluate RecXplainer on five real-world and large-scale recommendation datasets using five different kinds of recommender systems to demonstrate the efficacy of RecXplainer in capturing users' preferences over item attributes and using them to explain recommendations.

Attribute Recommendation Systems

Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information

no code implementations9 Nov 2022 Vishnu Dutt Sharma, John P. Dickerson, Pratap Tokekar

Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation.

Decision Making reinforcement-learning +1

Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition

2 code implementations NeurIPS 2023 Samuel Dooley, Rhea Sanjay Sukthanker, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum

Our search outputs a suite of models which Pareto-dominate all other high-performance architectures and existing bias mitigation methods in terms of accuracy and fairness, often by large margins, on the two most widely used datasets for face identification, CelebA and VGGFace2.

Face Identification Face Recognition +2

Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation

no code implementations5 Oct 2022 I. Elizabeth Kumar, Keegan E. Hines, John P. Dickerson

Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist today.


Measuring Representational Robustness of Neural Networks Through Shared Invariances

1 code implementation23 Jun 2022 Vedant Nanda, Till Speicher, Camila Kolling, John P. Dickerson, Krishna P. Gummadi, Adrian Weller

Our work offers a new view on robustness by using another reference NN to define the set of perturbations a given NN should be invariant to, thus generalizing the reliance on a reference ``human NN'' to any NN.

On the Generalizability and Predictability of Recommender Systems

1 code implementation23 Jun 2022 Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, John P. Dickerson, Colin White

By using far more meta-training data than prior work, RecZilla is able to substantially reduce the level of human involvement when faced with a new recommender system application.

Meta-Learning Recommendation Systems

Fair Labeled Clustering

no code implementations28 May 2022 Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson, Brian Brubach

To ensure group fairness in such a setting, we would desire proportional group representation in every label but not necessarily in every cluster as is done in group fair clustering.

Clustering Fairness

Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost

no code implementations27 May 2022 Marina Knittel, Max Springer, John P. Dickerson, Mohammadtaghi Hajiaghayi

We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation.

Clustering Fairness

Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments

no code implementations14 May 2022 Ryan Sullivan, J. K. Terry, Benjamin Black, John P. Dickerson

Visualizing optimization landscapes has led to many fundamental insights in numeric optimization, and novel improvements to optimization techniques.

reinforcement-learning Reinforcement Learning (RL)

The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations

no code implementations22 Feb 2022 Marina Knittel, Samuel Dooley, John P. Dickerson

We also assume the agent's preferences over entire matchings are determined by a general weighted valuation function of their (and their affiliates') matches.

Are Commercial Face Detection Models as Biased as Academic Models?

no code implementations25 Jan 2022 Samuel Dooley, George Z. Wei, Tom Goldstein, John P. Dickerson

When we compare the size of these disparities to that of commercial models, we conclude that commercial models - in contrast to their relatively larger development budget and industry-level fairness commitments - are always as biased or more biased than an academic model.

Face Detection Fairness

Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual

no code implementations16 Jan 2022 Seyed A. Esmaeili, Sharmila Duppala, Davidson Cheng, Vedant Nanda, Aravind Srinivasan, John P. Dickerson

Since fairness has become an important consideration that was ignored in the existing algorithms a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator's profit.

Fairness Vocal Bursts Valence Prediction

Robustness Disparities in Commercial Face Detection

1 code implementation27 Aug 2021 Samuel Dooley, Tom Goldstein, John P. Dickerson

Facial detection and analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade.

Face Detection

Fair Clustering Under a Bounded Cost

no code implementations NeurIPS 2021 Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan, John P. Dickerson

We derive fundamental lower bounds on the approximation of the utilitarian and egalitarian objectives and introduce algorithms with provable guarantees for them.

Clustering Fairness

Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting

1 code implementation14 Jun 2021 Christine Herlihy, Aviva Prins, Aravind Srinivasan, John P. Dickerson

Restless and collapsing bandits are often used to model budget-constrained resource allocation in settings where arms have action-dependent transition probabilities, such as the allocation of health interventions among patients.


Pitfalls of Explainable ML: An Industry Perspective

no code implementations14 Jun 2021 Sahil Verma, Aditya Lahiri, John P. Dickerson, Su-In Lee

The goal of explainable ML is to intuitively explain the predictions of a ML system, while adhering to the needs to various stakeholders.

Explainable Artificial Intelligence (XAI)

A New Notion of Individually Fair Clustering: $α$-Equitable $k$-Center

1 code implementation9 Jun 2021 Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas

Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications.

Clustering Fairness

Amortized Generation of Sequential Algorithmic Recourses for Black-box Models

1 code implementation7 Jun 2021 Sahil Verma, Keegan Hines, John P. Dickerson

We propose a novel stochastic-control-based approach that generates sequential ARs, that is, ARs that allow x to move stochastically and sequentially across intermediate states to a final state x'.

PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning

1 code implementation NeurIPS 2021 Neehar Peri, Michael J. Curry, Samuel Dooley, John P. Dickerson

In addition, we introduce a new metric to evaluate an auction allocations' adherence to such socially desirable constraints and demonstrate that our proposed method is competitive with current state-of-the-art neural-network based auction designs.


Using Inverse Optimization to Learn Cost Functions in Generalized Nash Games

1 code implementation24 Feb 2021 Stephanie Allen, John P. Dickerson, Steven A. Gabriel

As demonstrated by Ratliff et al. (2014), inverse optimization can be used to recover the objective function parameters of players in multi-player Nash games.

Technical Challenges for Training Fair Neural Networks

no code implementations12 Feb 2021 Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P. Dickerson, Tom Goldstein

As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes settings (such as facial recognition and medical imaging).

Fairness Medical Diagnosis

How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?

no code implementations NeurIPS 2021 Jingling Li, Mozhi Zhang, Keyulu Xu, John P. Dickerson, Jimmy Ba

Our framework measures a network's robustness via the predictive power in its representations -- the test performance of a linear model trained on the learned representations using a small set of clean labels.

Learning with noisy labels

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

no code implementations20 Oct 2020 Sahil Verma, Varich Boonsanong, Minh Hoang, Keegan E. Hines, John P. Dickerson, Chirag Shah

Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders.

BIG-bench Machine Learning counterfactual +1

ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning

no code implementations13 Oct 2020 Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J. Curry, Samuel Dooley, Ping-Yeh Chiang, Tom Goldstein, John P. Dickerson

Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.


The Affiliate Matching Problem: On Labor Markets where Firms are Also Interested in the Placement of Previous Workers

no code implementations24 Sep 2020 Samuel Dooley, John P. Dickerson

We model this affiliate matching problem in a generalization of the classic stable marriage setting by permitting firms to state preferences over not just which workers to whom they are matched, but also to which firms their affiliated workers are matched.


Kidney Exchange with Inhomogeneous Edge Existence Uncertainty

no code implementations7 Jul 2020 Hoda Bidkhori, John P. Dickerson, Duncan C. McElfresh, Ke Ren

To the best of our knowledge, the state-of-the-art approaches are only tractable when failure probabilities are identical.

Unifying Model Explainability and Robustness via Machine-Checkable Concepts

no code implementations1 Jul 2020 Vedant Nanda, Till Speicher, John P. Dickerson, Krishna P. Gummadi, Muhammad Bilal Zafar

Our framework defines a large number of concepts that the DNN explanations could be based on and performs the explanation-conformity check at test time to assess prediction robustness.

Probabilistic Fair Clustering

no code implementations NeurIPS 2020 Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John P. Dickerson

In fair clustering problems, vertices are endowed with a color (e. g., membership in a group), and the features of a valid clustering might also include the representation of colors in that clustering.

Clustering valid

Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning

1 code implementation17 Jun 2020 Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson

In this paper, we argue that traditional notions of fairness that are only based on models' outputs are not sufficient when the model is vulnerable to adversarial attacks.

Decision Making Face Recognition +1

Adapting a Kidney Exchange Algorithm to Align with Human Values

1 code implementation19 May 2020 Rachel Freedman, Jana Schaich Borg, Walter Sinnott-Armstrong, John P. Dickerson, Vincent Conitzer

In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ.

Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours

1 code implementation18 Dec 2019 Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman, John P. Dickerson, Aravind Srinivasan

Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e. g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver.


Group Fairness in Bandit Arm Selection

1 code implementation9 Dec 2019 Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson

We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting.


Deep k-NN Defense against Clean-label Data Poisoning Attacks

1 code implementation29 Sep 2019 Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson

Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples into the training data, causing a model to misclassify a particular test sample during inference.

Adversarial Attack Data Poisoning

An Algorithm for Multi-Attribute Diverse Matching

no code implementations7 Sep 2019 Saba Ahmadi, Faez Ahmed, John P. Dickerson, Mark Fuge, Samir Khuller

Bipartite b-matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas such as healthcare, advertising, education, and general resource allocation.


Making the Cut: A Bandit-based Approach to Tiered Interviewing

1 code implementation NeurIPS 2019 Candice Schumann, Zhi Lang, Jeffrey S. Foster, John P. Dickerson

Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits?

Scalable Robust Kidney Exchange

1 code implementation8 Nov 2018 Duncan C. McElfresh, Hoda Bidkhori, John P. Dickerson

Transactions in barter exchanges are often facilitated via a central clearinghouse that must match participants even in the face of uncertainty---over participants, existence and quality of potential trades, and so on.

Combinatorial Optimization Fairness +1

Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources

no code implementations22 Nov 2017 John P. Dickerson, Karthik A. Sankararaman, Aravind Srinivasan, Pan Xu

Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources.

The Diverse Cohort Selection Problem

1 code implementation11 Sep 2017 Candice Schumann, Samsara N. Counts, Jeffrey S. Foster, John P. Dickerson

We apply our general algorithm to a real-world problem with combinatorial structure: incorporating diversity into university admissions.

Learning to Schedule Deadline- and Operator-Sensitive Tasks

no code implementations19 Jun 2017 Hanan Rosemarin, John P. Dickerson, Sarit Kraus

The use of semi-autonomous and autonomous robotic assistants to aid in care of the elderly is expected to ease the burden on human caretakers, with small-stage testing already occurring in a variety of countries.


Operation Frames and Clubs in Kidney Exchange

no code implementations25 May 2017 Gabriele Farina, John P. Dickerson, Tuomas Sandholm

A kidney exchange is a centrally-administered barter market where patients swap their willing yet incompatible donors.

Balancing Lexicographic Fairness and a Utilitarian Objective with Application to Kidney Exchange

no code implementations27 Feb 2017 Duncan C. McElfresh, John P. Dickerson

Balancing fairness and efficiency in resource allocation is a classical economic and computational problem.


Diverse Weighted Bipartite b-Matching

1 code implementation23 Feb 2017 Faez Ahmed, John P. Dickerson, Mark Fuge

Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation.


Position-Indexed Formulations for Kidney Exchange

no code implementations6 Jun 2016 John P. Dickerson, David F. Manlove, Benjamin Plaut, Tuomas Sandholm, James Trimble

The recent introduction of chains, where a donor without a paired patient triggers a sequence of donations without requiring a kidney in return, increased the efficacy of fielded kidney exchanges---while also dramatically raising the empirical computational hardness of clearing the market in practice.


Hardness of the Pricing Problem for Chains in Barter Exchanges

no code implementations1 Jun 2016 Benjamin Plaut, John P. Dickerson, Tuomas Sandholm

One of the leading techniques has been branch and price, where column generation is used to incrementally bring cycles and chains into the optimization model on an as-needed basis.

Small Representations of Big Kidney Exchange Graphs

no code implementations25 May 2016 John P. Dickerson, Aleksandr M. Kazachkov, Ariel D. Procaccia, Tuomas Sandholm

This growth results in more lives saved, but exacerbates the empirical hardness of the $\mathcal{NP}$-complete problem of optimally matching patients to donors.

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