Quality Diversity through Human Feedback
Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, Joel Lehman
Under review &
NeurIPS 2023: Agent Learning in Open-Endedness Workshop (Spotlight)
[arXiv]

Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation
Li Ding, Masrour Zoghi, Guy Tennenholtz, Maryam Karimzadehgan
NeurIPS 2023: Workshop on Adaptive Experimental Design and Active Learning in the Real World
[arXiv] [code]

Probabilistic Lexicase Selection
Li Ding, Edward Pantridge, Lee Spector
GECCO 2023 (Oral)
[paper] [arXiv] [code]

CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild
Li Ding, Jack Terwilliger, Aishni Parab, Meng Wang, Lex Fridman, Bruce Mehler, Bryan Reimer
ACM Transactions on Computer-Human Interaction
[paper] [arXiv]

Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits
Li Ding, Lee Spector
Entropy (Special Issue: Quantum Machine Learning)
[paper]

Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance
Ryan Boldi, Li Ding, Lee Spector
NeurIPS 2023: Agent Learning in Open-Endedness Workshop
[arXiv]

Particularity
Lee Spector, Li Ding, Ryan Boldi
Genetic Programming Theory and Practice XX
[arXiv]

Optimizing Neural Networks with Gradient Lexicase Selection
Li Ding, Lee Spector
ICLR 2022
[paper] [video] [poster] [code]

Going Faster and Hence Further with Lexicase Selection
Li Ding, Ryan Boldi, Thomas Helmuth, Lee Spector
GECCO 2022 (poster)
[paper]

Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
Li Ding, Lee Spector
GECCO 2022: Quantum Optimization Workshop (Oral)
[paper] [arXiv]

Lexicase Selection at Scale
Li Ding, Ryan Boldi, Thomas Helmuth, Lee Spector
GECCO 2022: Large-Scale Evolutionary Optimization and Learning Workshop (Oral)
[paper] [arXiv]

Value of Temporal Dynamics Information in Driving Scene Segmentation
Li Ding, Jack Terwilliger, Rini Sherony, Bryan Reimer, Lex Fridman
IEEE Transactions on Intelligent Vehicles
[paper] [arXiv] [MIT DriveSeg Dataset]
Press coverage: [MIT News] [Forbes] [InfoQ] [TechCrunch]

Evolving Neural Selection with Adaptive Regularization
Li Ding, Lee Spector
GECCO 2021: NeuroEvolution at Work Workshop (Oral)
[paper] [arXiv] [video]

Perceptual Evaluation of Driving Scene Segmentation
Li Ding, Rini Sherony, Bruce Mehler, Bryan Reimer
IEEE IV 2021 (Oral)
[paper] [video]

MIT-AVT Clustered Driving Scene Dataset: Evaluating Perception Systems in Real-World Naturalistic Driving Scenarios
Li Ding, Michael Glazer, Meng Wang, Bruce Mehler, Bryan Reimer, Lex Fridman
IEEE IV 2020: NDDA Workshop (Oral)
[paper] [video]

Arguing Machines: Human Supervision of Black Box AI Systems that Make Life-Critical Decisions
Lex Fridman, Li Ding, Benedikt Jenik, Bryan Reimer
CVPR 2019: Workshop on Autonomous Driving
[paper] [arXiv] [video]

MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation
Lex Fridman, Daniel E. Brown, Michael Glazer, William Angell, Spencer Dodd, Benedikt Jenik, Jack Terwilliger, Julia Kindelsberger, Li Ding, Sean Seaman, Alea Mehler, Andrew Sipperley, Anthony Pettinato, Bobbie Seppelt, Linda Angell, Bruce Mehler, Bryan Reimer
IEEE Access
[paper] [arXiv] [video]

Object as Distribution
Li Ding, Lex Fridman
Technical Report
[arXiv]

Human Interaction with Deep Reinforcement Learning Agents in Virtual Reality
Lex Fridman, Henri Schmidt, Jack Terwilliger, Li Ding
NeurIPS 2018: Deep RL Workshop

Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment
Li Ding, Chenliang Xu
CVPR 2018
[paper] [arXiv] [poster] [code]

TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation
Li Ding, Chenliang Xu
Technical Report
[arXiv]