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ORCIDJohn P. Dickerson
John Paul Dickerson – John Dickerson 0001
Person information
- affiliation: University of Maryland, MD, USA
Other persons with the same name
- John Dickerson 0002 (aka: John E. Dickerson) — Iowa State University, Ames, IA, USA
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2020 – today
- 2023
[i80]Alex Stein, Avi Schwarzschild, Michael J. Curry, Tom Goldstein, John P. Dickerson:
Neural Auctions Compromise Bidder Information. CoRR abs/2303.00116 (2023)- [i79]Teresa Datta, John P. Dickerson:
Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook. CoRR abs/2303.06223 (2023) - 2022
- [j18]Sanmay Das, Nicholas Mattei, John P. Dickerson, Sven Koenig, Louise A. Dennis, Larry R. Medsker, Ziyu Yao, Anuj Karpatne, Alan Tsang, Matt Luckcuck:
SIGAI Annual Report: July 1 2021 - June 30 2022. AI Matters 8(3): 4-7 (2022) - [j17]Stephanie Allen
, Steven A. Gabriel, John P. Dickerson:
Using inverse optimization to learn cost functions in generalized Nash games. Comput. Oper. Res. 142: 105721 (2022) - [c90]Sahil Verma, Keegan Hines, John P. Dickerson:
Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models. AAAI 2022: 8512-8519 - [c89]I. Elizabeth Kumar, Keegan E. Hines, John P. Dickerson:
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation. AIES 2022: 357-368 - [c88]Michael J. Curry, Uro Lyi, Tom Goldstein, John P. Dickerson:
Learning Revenue-Maximizing Auctions With Differentiable Matching. AISTATS 2022: 6062-6073 - [c87]Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas:
A New Notion of Individually Fair Clustering: α-Equitable k-Center. AISTATS 2022: 6387-6408 - [c86]Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson:
Group Fairness in Bandits with Biased Feedback. AAMAS 2022: 1155-1163 - [c85]Seyed A. Esmaeili, Sharmila Duppala, Vedant Nanda, Aravind Srinivasan, John P. Dickerson:
Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual. AAMAS 2022: 1583-1585 - [c84]Samuel Dooley, Dana Turjeman, John P. Dickerson, Elissa M. Redmiles:
Field Evidence of the Effects of Privacy, Data Transparency, and Pro-social Appeals on COVID-19 App Attractiveness. CHI 2022: 622:1-622:21 - [c83]Arpit Bansal, Ping-Yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. ICML 2022: 1450-1465 - [c82]Vedant Nanda, Till Speicher, Camila Kolling, John P. Dickerson, Krishna P. Gummadi, Adrian Weller:
Measuring Representational Robustness of Neural Networks Through Shared Invariances. ICML 2022: 16368-16382 - [c81]Ryan Sullivan, Jordan K. Terry, Benjamin Black, John P. Dickerson:
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments. ICML 2022: 20744-20776 - [c80]Marina Knittel, Samuel Dooley, John P. Dickerson:
The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations. IJCAI 2022: 356-362 - [c79]Naveen Durvasula, Aravind Srinivasan, John P. Dickerson:
Forecasting Patient Outcomes in Kidney Exchange. IJCAI 2022: 5052-5058 - [c78]Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson, Brian Brubach:
Fair Labeled Clustering. KDD 2022: 327-335 - [i78]Seyed A. Esmaeili, Sharmila Duppala, Vedant Nanda, Aravind Srinivasan, John P. Dickerson:
Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual. CoRR abs/2201.06021 (2022) - [i77]Samuel Dooley, George Z. Wei, Tom Goldstein, John P. Dickerson:
Are Commercial Face Detection Models as Biased as Academic Models? CoRR abs/2201.10047 (2022) - [i76]Michael J. Curry, Tuomas Sandholm, John P. Dickerson:
Differentiable Economics for Randomized Affine Maximizer Auctions. CoRR abs/2202.02872 (2022) - [i75]Marina Knittel, Samuel Dooley, John P. Dickerson:
The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations. CoRR abs/2202.11095 (2022) - [i74]Kweku Kwegyir-Aggrey, Jessica Dai, John P. Dickerson, Keegan Hines:
Achieving Downstream Fairness with Geometric Repair. CoRR abs/2203.07490 (2022) - [i73]Ryan Sullivan, Justin K. Terry, Benjamin Black, John P. Dickerson:
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments. CoRR abs/2205.07015 (2022) - [i72]Marina Knittel, John P. Dickerson, MohammadTaghi Hajiaghayi:
Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost. CoRR abs/2205.14198 (2022) - [i71]Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson, Brian Brubach:
Fair Labeled Clustering. CoRR abs/2205.14358 (2022) - [i70]Duncan C. McElfresh, Sujay Khandagale, Jonathan Valverde, John P. Dickerson, Colin White:
On the Generalizability and Predictability of Recommender Systems. CoRR abs/2206.11886 (2022) - [i69]Vedant Nanda, Till Speicher, Camila Kolling, John P. Dickerson, Krishna P. Gummadi, Adrian Weller:
Measuring Representational Robustness of Neural Networks Through Shared Invariances. CoRR abs/2206.11939 (2022) - [i68]Arpit Bansal, Ping-yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. CoRR abs/2207.07972 (2022) - [i67]I. Elizabeth Kumar, Keegan E. Hines, John P. Dickerson:
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation. CoRR abs/2210.02516 (2022) - [i66]Rhea Sukthanker, Samuel Dooley, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum:
On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition. CoRR abs/2210.09943 (2022) - [i65]Vishnu Dutt Sharma, John P. Dickerson, Pratap Tokekar:
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information. CoRR abs/2211.04987 (2022) - [i64]Samuel Dooley, George Z. Wei, Tom Goldstein, John P. Dickerson:
Robustness Disparities in Face Detection. CoRR abs/2211.15937 (2022) - [i63]Saptarashmi Bandyopadhyay, Chenqi Zhu, Philip Daniel, Joshua Morrison, Ethan Shay, John Dickerson:
Targets in Reinforcement Learning to solve Stackelberg Security Games. CoRR abs/2211.17132 (2022) - [i62]Christine Herlihy, John P. Dickerson:
Networked Restless Bandits with Positive Externalities. CoRR abs/2212.05144 (2022) - [i61]Teresa Datta, Daniel Nissani, Max Cembalest, Akash Khanna, Haley Massa, John P. Dickerson:
Tensions Between the Proxies of Human Values in AI. CoRR abs/2212.07508 (2022) - 2021
- [j16]Sanmay Das, Nicholas Mattei, John P. Dickerson, Sven Koenig, Louise A. Dennis, Larry R. Medsker, Todd W. Neller, Iolanda Leite, Anuj Karpatne, Alan Tsang:
SIGAI annual report: July 1 2020 - June 30 2021. AI Matters 7(3): 5-11 (2021) - [j15]John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Allocation Problems in Ride-sharing Platforms: Online Matching with Offline Reusable Resources. ACM Trans. Economics and Comput. 9(3): 13:1-13:17 (2021) - [c77]Haris Aziz, Ágnes Cseh, John P. Dickerson, Duncan C. McElfresh:
Optimal Kidney Exchange with Immunosuppressants. AAAI 2021: 21-29 - [c76]Fotini Christia, Michael J. Curry, Constantinos Daskalakis, Erik D. Demaine, John P. Dickerson, MohammadTaghi Hajiaghayi, Adam Hesterberg, Marina Knittel, Aidan Milliff:
Scalable Equilibrium Computation in Multi-agent Influence Games on Networks. AAAI 2021: 5277-5285 - [c75]Duncan C. McElfresh, Lok Chan, Kenzie Doyle, Walter Sinnott-Armstrong, Vincent Conitzer, Jana Schaich Borg, John P. Dickerson:
Indecision Modeling. AAAI 2021: 5975-5983 - [c74]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas:
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints. AAAI 2021: 6822-6830 - [c73]Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson:
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning. FAccT 2021: 466-477 - [c72]Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein:
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. ICLR 2021 - [c71]Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein:
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. ICML 2021: 9389-9398 - [c70]Naveen Raman, Sanket Shah, John P. Dickerson:
Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling. IJCAI 2021: 363-369 - [c69]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. NeurIPS 2021: 6733-6746 - [c68]Jingling Li, Mozhi Zhang, Keyulu Xu, John Dickerson, Jimmy Ba:
How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? NeurIPS 2021: 9788-9803 - [c67]Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan, John Dickerson:
Fair Clustering Under a Bounded Cost. NeurIPS 2021: 14345-14357 - [c66]Neehar Peri, Michael J. Curry, Samuel Dooley, John Dickerson:
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning. NeurIPS 2021: 17532-17542 - [c65]Duncan C. McElfresh, John P. Dickerson, Ke Ren, Hoda Bidkhori:
Distributionally Robust Cycle and Chain Packing With Application To Organ Exchange. WSC 2021: 1-12 - [i60]Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein:
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. CoRR abs/2101.07922 (2021) - [i59]Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P. Dickerson, Tom Goldstein:
Technical Challenges for Training Fair Neural Networks. CoRR abs/2102.06764 (2021) - [i58]Stephanie Allen, John P. Dickerson, Steven A. Gabriel:
Using Inverse Optimization to Learn Cost Functions in Generalized Nash Games. CoRR abs/2102.12415 (2021) - [i57]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas:
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints. CoRR abs/2103.02013 (2021) - [i56]Haris Aziz, Ágnes Cseh, John P. Dickerson, Duncan C. McElfresh:
Optimal Kidney Exchange with Immunosuppressants. CoRR abs/2103.02253 (2021) - [i55]Neehar Peri, Michael J. Curry, Samuel Dooley, John P. Dickerson:
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning. CoRR abs/2106.03215 (2021) - [i54]Sahil Verma, Keegan Hines, John P. Dickerson:
Amortized Generation of Sequential Counterfactual Explanations for Black-box Models. CoRR abs/2106.03962 (2021) - [i53]Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas:
A New Notion of Individually Fair Clustering: α-Equitable k-Center. CoRR abs/2106.05423 (2021) - [i52]Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan, John P. Dickerson:
Fair Clustering Under a Bounded Cost. CoRR abs/2106.07239 (2021) - [i51]Christine Herlihy, Aviva Prins, Aravind Srinivasan, John Dickerson:
Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting. CoRR abs/2106.07677 (2021) - [i50]Sahil Verma, John P. Dickerson, Keegan Hines:
Counterfactual Explanations for Machine Learning: Challenges Revisited. CoRR abs/2106.07756 (2021) - [i49]Sahil Verma, Aditya Lahiri, John P. Dickerson, Su-In Lee:
Pitfalls of Explainable ML: An Industry Perspective. CoRR abs/2106.07758 (2021) - [i48]Michael J. Curry, Uro Lyi, Tom Goldstein, John Dickerson:
Learning Revenue-Maximizing Auctions With Differentiable Matching. CoRR abs/2106.07877 (2021) - [i47]Duncan C. McElfresh, Christian Kroer, Sergey Pupyrev, Eric Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P. Dickerson:
Matching Algorithms for Blood Donation. CoRR abs/2108.04862 (2021) - [i46]Samuel Dooley, Tom Goldstein, John P. Dickerson:
Robustness Disparities in Commercial Face Detection. CoRR abs/2108.12508 (2021) - [i45]Naveen Raman, Sanket Shah, John Dickerson:
Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling. CoRR abs/2110.03524 (2021) - [i44]Samuel Dooley, Ryan Downing, George Z. Wei, Nathan Shankar, Bradon Thymes, Gudrun Thorkelsdottir, Tiye Kurtz-Miott, Rachel Mattson, Olufemi Obiwumi, Valeriia Cherepanova, Micah Goldblum, John P. Dickerson, Tom Goldstein:
Comparing Human and Machine Bias in Face Recognition. CoRR abs/2110.08396 (2021) - [i43]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P. Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. CoRR abs/2110.14363 (2021) - [i42]Vedant Nanda, Ayan Majumdar, Camila Kolling, John P. Dickerson, Krishna P. Gummadi, Bradley C. Love, Adrian Weller:
Exploring Alignment of Representations with Human Perception. CoRR abs/2111.14726 (2021) - [i41]Hannah K. Bako, Alisha Varma, Anuoluwapo Faboro, Mahreen Haider, Favour Nerrise, John P. Dickerson, Leilani Battle:
User-Driven Programming Support for Rapid Visualization Authoring in D3. CoRR abs/2112.03179 (2021) - 2020
- [j14]Rachel Freedman, Jana Schaich Borg, Walter Sinnott-Armstrong, John P. Dickerson, Vincent Conitzer:
Adapting a kidney exchange algorithm to align with human values. Artif. Intell. 283: 103261 (2020) - [j13]Michael Albert, John P. Dickerson:
AAAI/ACM SIGAI job fair 2020: a retrospective. AI Matters 6(1): 7-8 (2020) - [j12]Sanmay Das, Nicholas Mattei, John P. Dickerson, Sven Koenig, Louise A. Dennis, Larry R. Medsker, Todd W. Neller, Iolanda Leite, Anuj Karpatne:
SIGAI annual report: July 1 2019 - June 30 2020. AI Matters 6(2): 5-9 (2020) - [j11]Avrim Blum, John P. Dickerson, Nika Haghtalab, Ariel D. Procaccia, Tuomas Sandholm, Ankit Sharma:
Ignorance Is Almost Bliss: Near-Optimal Stochastic Matching with Few Queries. Oper. Res. 68(1): 16-34 (2020) - [c64]Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman, John P. Dickerson, Aravind Srinivasan:
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours. AAAI 2020: 2210-2217 - [c63]Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein:
Universal Adversarial Training. AAAI 2020: 5636-5643 - [c62]Zeyu Zhao, John P. Dickerson:
Clearing Kidney Exchanges via Graph Neural Network Guided Tree Search (Student Abstract). AAAI 2020: 13989-13990 - [c61]Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman, John P. Dickerson, Aravind Srinivasan:
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours. AIES 2020: 131 - [c60]Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz:
Human Comprehension of Fairness in Machine Learning. AIES 2020: 152 - [c59]Lok Chan, Kenzie Doyle, Duncan C. McElfresh, Vincent Conitzer, John P. Dickerson, Jana Schaich Borg, Walter Sinnott-Armstrong:
Artificial Artificial Intelligence: Measuring Influence of AI 'Assessments' on Moral Decision-Making. AIES 2020: 214-220 - [c58]Candice Schumann, Jeffrey S. Foster, Nicholas Mattei, John P. Dickerson:
We Need Fairness and Explainability in Algorithmic Hiring. AAMAS 2020: 1716-1720 - [c57]Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. ECCV Workshops (1) 2020: 55-70 - [c56]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas:
A Pairwise Fair and Community-preserving Approach to k-Center Clustering. ICML 2020: 1178-1189 - [c55]Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz:
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics. ICML 2020: 8377-8387 - [c54]Saba Ahmadi, Faez Ahmed, John P. Dickerson, Mark D. Fuge, Samir Khuller:
An Algorithm for Multi-Attribute Diverse Matching. IJCAI 2020: 3-9 - [c53]Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein:
Detection as Regression: Certified Object Detection with Median Smoothing. NeurIPS 2020 - [c52]Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John Dickerson:
Certifying Strategyproof Auction Networks. NeurIPS 2020 - [c51]Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John Dickerson:
Probabilistic Fair Clustering. NeurIPS 2020 - [c50]Duncan C. McElfresh, Michael J. Curry, Tuomas Sandholm, John Dickerson:
Improving Policy-Constrained Kidney Exchange via Pre-Screening. NeurIPS 2020 - [c49]Duncan C. McElfresh, Christian Kroer, Sergey Pupyrev, Eric Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P. Dickerson:
Matching Algorithms for Blood Donation. EC 2020: 463-464 - [c48]Hoda Bidkhori, John Dickerson, Duncan C. McElfresh, Ke Ren:
Kidney Exchange with Inhomogeneous Edge Existence Uncertainty. UAI 2020: 161-170 - [i40]Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz:
Human Comprehension of Fairness in Machine Learning. CoRR abs/2001.00089 (2020) - [i39]Duncan C. McElfresh, Samuel Dooley, Yuan Cui, Kendra Griesman, Weiqin Wang, Tyler Will, Neil Sehgal, John P. Dickerson:
Can an Algorithm be My Healthcare Proxy? CoRR abs/2001.09742 (2020) - [i38]Lok Chan, Kenzie Doyle, Duncan C. McElfresh, Vincent Conitzer, John P. Dickerson, Jana Schaich Borg, Walter Sinnott-Armstrong:
Artificial Artificial Intelligence: Measuring Influence of AI 'Assessments' on Moral Decision-Making. CoRR abs/2001.09766 (2020) - [i37]Faez Ahmed, John Dickerson, Mark D. Fuge:
Forming Diverse Teams from Sequentially Arriving People. CoRR abs/2002.10697 (2020) - [i36]Phebe Vayanos, Duncan C. McElfresh, Yingxiao Ye, John Paul Dickerson, Eric Rice:
Active Preference Elicitation via Adjustable Robust Optimization. CoRR abs/2003.01899 (2020) - [i35]Rachel Freedman, Jana Schaich Borg, Walter Sinnott-Armstrong, John P. Dickerson, Vincent Conitzer:
Adapting a Kidney Exchange Algorithm to Align with Human Values. CoRR abs/2005.09755 (2020) - [i34]Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John P. Dickerson:
Certifying Strategyproof Auction Networks. CoRR abs/2006.08742 (2020) - [i33]Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John P. Dickerson:
Probabilistic Fair Clustering. CoRR abs/2006.10916 (2020) - [i32]Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein:
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. CoRR abs/2006.12557 (2020) - [i31]Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson:
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning. CoRR abs/2006.12621 (2020) - [i30]Vedant Nanda, Till Speicher, John P. Dickerson, Krishna P. Gummadi, Muhammad Bilal Zafar:
Unifying Model Explainability and Robustness via Machine-Checkable Concepts. CoRR abs/2007.00251 (2020) - [i29]Hoda Bidkhori, John P. Dickerson, Duncan C. McElfresh, Ke Ren:
Kidney Exchange with Inhomogeneous Edge Existence Uncertainty. CoRR abs/2007.03191 (2020) - [i28]Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John P. Dickerson, Tom Goldstein:
Detection as Regression: Certified Object Detection by Median Smoothing. CoRR abs/2007.03730 (2020) - [i27]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas:
A Pairwise Fair and Community-preserving Approach to k-Center Clustering. CoRR abs/2007.07384 (2020) - [i26]Samuel Dooley, John P. Dickerson:
The Affiliate Matching Problem: On Labor Markets where Firms are Also Interested in the Placement of Previous Workers. CoRR abs/2009.11867 (2020) - [i25]Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J. Curry, Samuel Dooley, Ping-yeh Chiang, Tom Goldstein, John P. Dickerson:
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning. CoRR abs/2010.06398 (2020) - [i24]Sahil Verma, John P. Dickerson, Keegan Hines:
Counterfactual Explanations for Machine Learning: A Review. CoRR abs/2010.10596 (2020) - [i23]Duncan C. McElfresh, Michael J. Curry, Tuomas Sandholm, John P. Dickerson:
Improving Policy-Constrained Kidney Exchange via Pre-Screening. CoRR abs/2010.12069 (2020) - [i22]Duncan C. McElfresh, Lok Chan, Kenzie Doyle, Walter Sinnott-Armstrong, Vincent Conitzer, Jana Schaich Borg, John P. Dickerson:
Indecision Modeling. CoRR abs/2012.08485 (2020) - [i21]Jingling Li, Mozhi Zhang, Keyulu Xu, John P. Dickerson, Jimmy Ba:
Noisy Labels Can Induce Good Representations. CoRR abs/2012.12896 (2020)
2010 – 2019
- 2019
- [j10]Christopher Amato, John P. Dickerson:
AAAI/ACM SIGAI job fair 2019: a retrospective. AI Matters 5(1): 5-6 (2019) - [j9]Sven Koenig, Sanmay Das, Rosemary D. Paradis, John P. Dickerson, Yolanda Gil, Katherine Guo, Benjamin Kuipers, Iolanda Leite, Hang Ma, Nicholas Mattei, Amy McGovern, Larry R. Medsker, Todd W. Neller, Marion Neumann, Plamen Petrov, Michael Rovatsos, David G. Stork:
ACM SIGAI activity report. AI Matters 5(3): 6-11 (2019) - [j8]John P. Dickerson, Ariel D. Procaccia, Tuomas Sandholm:
Failure-Aware Kidney Exchange. Manag. Sci. 65(4): 1768-1791 (2019) - [c47]Duncan C. McElfresh, Hoda Bidkhori, John P. Dickerson:
Scalable Robust Kidney Exchange. AAAI 2019: 1077-1084 - [c46]John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity. AAAI 2019: 1877-1884 - [c45]Pan Xu, Yexuan Shi, Hao Cheng, John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Yongxin Tong, Leonidas Tsepenekas:
A Unified Approach to Online Matching with Conflict-Aware Constraints. AAAI 2019: 2221-2228 - [c44]Candice Schumann, Samsara N. Counts, Jeffrey S. Foster, John P. Dickerson:
The Diverse Cohort Selection Problem. AAMAS 2019: 601-609 - [c43]