Lily Weng

According to our database1, Lily Weng authored at least 12 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Provable and Efficient Dataset Distillation for Kernel Ridge Regression.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023
Analyzing Generalization of Neural Networks through Loss Path Kernels.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning.
CoRR, 2022

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework.
Proceedings of the International Conference on Machine Learning, 2022

2021
Hidden Cost of Randomized Smoothing.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Fast Training of Provably Robust Neural Networks by SingleProp.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Neural Network Control Policy Verification With Persistent Adversarial Perturbation.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach.
Proceedings of the 36th International Conference on Machine Learning, 2019

POPQORN: Quantifying Robustness of Recurrent Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019


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