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ORCIDLeilani H. Gilpin
Person information
- affiliation: University of California Santa Cruz, CA, USA
- affiliation (former): MIT, CSAIL, USA
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2020 – today
- 2024
[c14]Priyesh Vakharia, Abigail Kufeldt, Max Meyers, Ian Lane, Leilani H. Gilpin:
ProSLM: A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering. NeSy (2) 2024: 291-304- [i14]Rui-Jie Zhu, Ziqing Wang, Leilani Gilpin, Jason K. Eshraghian:
Autonomous Driving with Spiking Neural Networks. CoRR abs/2405.19687 (2024) - [i13]Jonathan Wellington Morris, Vishrut Shah, Alex Besanceney, Daksh Shah, Leilani H. Gilpin:
Slug Mobile: Test-Bench for RL Testing. CoRR abs/2409.10532 (2024) - [i12]Priyesh Vakharia, Abigail Kufeldt, Max Meyers, Ian Lane, Leilani Gilpin:
ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering. CoRR abs/2409.11589 (2024) - 2023
- [j6]Adam Amos-Binks
, Dustin Dannenhauer, Leilani H. Gilpin:
The anticipatory paradigm. AI Mag. 44(2): 133-143 (2023) - [j5]Ryan Jenkins, Kristian J. Hammond, Sarah Spurlock, Leilani Gilpin:
Separating facts and evaluation: motivation, account, and learnings from a novel approach to evaluating the human impacts of machine learning. AI Soc. 38(4): 1415-1428 (2023) - [c13]Leilani H. Gilpin:
Accountability Layers: Explaining Complex System Failures by Parts. AAAI 2023: 15439 - [c12]Biagio La Rosa, Leilani Gilpin, Roberto Capobianco:
Towards a fuller understanding of neurons with Clustered Compositional Explanations. NeurIPS 2023 - [i11]Shreyan Mitra, Leilani Gilpin:
The XAISuite framework and the implications of explanatory system dissonance. CoRR abs/2304.08499 (2023) - [i10]Adam Amos-Binks, Dustin Dannenhauer, Leilani H. Gilpin:
Anticipatory Thinking Challenges in Open Worlds: Risk Management. CoRR abs/2306.13157 (2023) - [i9]Sharan Subramanian, Leilani H. Gilpin:
Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification. CoRR abs/2310.10806 (2023) - [i8]Shiyuan Huang, Siddarth Mamidanna, Shreedhar Jangam, Yilun Zhou, Leilani H. Gilpin:
Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations. CoRR abs/2310.11207 (2023) - [i7]Biagio La Rosa, Leilani H. Gilpin, Roberto Capobianco:
Towards a fuller understanding of neurons with Clustered Compositional Explanations. CoRR abs/2310.18443 (2023) - [i6]Shreyan Mitra, Leilani Gilpin:
A novel post-hoc explanation comparison metric and applications. CoRR abs/2311.10811 (2023) - 2022
- [j4]Peter R. Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Piyush Khandelwal, Varun Raj Kompella, HaoChih Lin, Patrick MacAlpine, Declan Oller, Takuma Seno, Craig Sherstan, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Rory Douglas, Dion Whitehead, Peter Dürr, Peter Stone, Michael Spranger, Hiroaki Kitano:
Outracing champion Gran Turismo drivers with deep reinforcement learning. Nat. 602(7896): 223-228 (2022) - [i5]Leilani H. Gilpin, Andrew R. Paley, Mohammed A. Alam, Sarah Spurlock, Kristian J. Hammond:
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI. CoRR abs/2207.00007 (2022) - [i4]Cosmin Badea, Leilani Gilpin:
Establishing Meta-Decision-Making for AI: An Ontology of Relevance, Representation and Reasoning. CoRR abs/2210.00608 (2022) - 2021
- [c11]Leilani H. Gilpin:
Anticipatory Thinking: A Testing and Representation Challenge for Self-Driving Cars. CISS 2021: 1-2 - [c10]Leilani H. Gilpin, Vishnu Penubarthi, Lalana Kagal:
Explaining Multimodal Errors in Autonomous Vehicles. DSAA 2021: 1-10 - 2020
- [j3]Michal Araszkiewicz, Ilaria Angela Amantea, Saurabh Chakravarty, Robert van Doesburg, Maria Dymitruk, Marie Garin, Leilani Gilpin, Daphne Odekerken, Seyedeh Sajedeh Salehi:
ICAIL Doctoral Consortium, Montreal 2019. Artif. Intell. Law 28(2): 267-280 (2020) - [i3]Jason R. Wilson, Leilani Gilpin, Irina Rabkina:
A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation. CoRR abs/2012.02904 (2020)
2010 – 2019
- 2019
- [j2]Ioana Baldini, Clark W. Barrett, Antonio Chella, Carlos Cinelli, David Gamez, Leilani H. Gilpin, Knut Hinkelmann, Dylan Holmes, Takashi Kido, Murat Kocaoglu, William F. Lawless, Alessio Lomuscio, Jamie C. Macbeth, Andreas Martin, Ranjeev Mittu, Evan Patterson, Donald Sofge, Prasad Tadepalli, Keiki Takadama, Shomir Wilson:
Reports of the AAAI 2019 Spring Symposium Series. AI Mag. 40(3): 59-66 (2019) - [c9]Leilani Gilpin:
Explaining Possible Futures for Robust Autonomous Decision-Making. COGSAT@AAAI Fall Symposium 2019 - [c8]Leilani H. Gilpin, Lalana Kagal:
An Adaptable Self-Monitoring Framework for Opaque Machines. AAMAS 2019: 1982-1984 - [i2]Leilani H. Gilpin, Cecilia Testart, Nathaniel Fruchter, Julius Adebayo:
Explaining Explanations to Society. CoRR abs/1901.06560 (2019) - 2018
- [c7]Leilani H. Gilpin:
Reasonableness Monitors. AAAI 2018: 8014-8015 - [c6]Leilani H. Gilpin, Danielle M. Olson, Tarfah Alrashed:
Perception of Speaker Personality Traits Using Speech Signals. CHI Extended Abstracts 2018 - [c5]Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal:
Explaining Explanations: An Overview of Interpretability of Machine Learning. DSAA 2018: 80-89 - [c4]Leilani H. Gilpin, Cagri Zaman, Danielle Olson, Ben Z. Yuan:
Reasonable Perception: Connecting Vision and Language Systems for Validating Scene Descriptions. HRI (Companion) 2018: 115-116 - [i1]Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal:
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning. CoRR abs/1806.00069 (2018) - 2017
- [c3]Leilani Hendrina Gilpin, Ben Ze Yuan:
Getting Up to Speed on Vehicle Intelligence. AAAI Spring Symposia 2017 - 2016
- [j1]Juan Liu, Eric Bier, Aaron Wilson, John Alexis Guerra Gómez, Tomonori Honda, Kumar Sricharan, Leilani Gilpin, Daniel Davies:
Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data. AI Mag. 37(2): 33-46 (2016) - 2015
- [c2]Juan Liu, Eric Bier, Aaron Wilson, Tomonori Honda, Kumar Sricharan, Leilani Gilpin, John Alexis Guerra Gómez, Daniel Davies:
Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data. AAAI 2015: 3912-3919 - 2014
- [c1]Leilani Gilpin, Laurent Ciarletta, Yannick Presse, Vincent Chevrier, Virginie Galtier:
Co-simulation solutions using AA4MM-FMI applied to smart space heating models. SimuTools 2014: 153-159
Coauthor Index
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last updated on 2024-11-15 19:31 CET by the dblp team
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