Kaiwen Zhou

According to our database1, Kaiwen Zhou authored at least 39 papers between 2018 and 2024.

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

Timeline

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Bibliography

2024
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations.
CoRR, 2024

Muffin or Chihuahua? Challenging Large Vision-Language Models with Multipanel VQA.
CoRR, 2024

Enhancing Evolving Domain Generalization through Dynamic Latent Representations.
CoRR, 2024

21.2 A 0.81mA, -105.2dB THD+N Class-D Audio Amplifier with Capacitive Feedforward and PWM-Aliasing Reduction for Wide-Band-Effective Linearity Improvement.
Proceedings of the IEEE International Solid-State Circuits Conference, 2024

Enhancing Evolving Domain Generalization through Dynamic Latent Representations.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes.
CoRR, 2023

Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
CoRR, 2023

ViCor: Bridging Visual Understanding and Commonsense Reasoning with Large Language Models.
CoRR, 2023

Towards Understanding Feature Learning in Out-of-Distribution Generalization.
CoRR, 2023

Understanding and Improving Feature Learning for Out-of-Distribution Generalization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation.
Proceedings of the International Conference on Machine Learning, 2023

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Novel Extrapolation Technique to Accelerate WMMSE.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
Navigation as the Attacker Wishes? Towards Building Byzantine-Robust Embodied Agents under Federated Learning.
CoRR, 2022

JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents.
CoRR, 2022

Efficient Private SCO for Heavy-Tailed Data via Clipping.
CoRR, 2022

Pareto Invariant Risk Minimization.
CoRR, 2022

An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms.
CoRR, 2022

On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions.
Proceedings of the International Conference on Machine Learning, 2022

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack.
Proceedings of the International Conference on Machine Learning, 2022

FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation.
Proceedings of the Computer Vision - ECCV 2022, 2022

Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization.
CoRR, 2021

Local Reweighting for Adversarial Training.
CoRR, 2021

2020
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning.
IEEE Trans. Knowl. Data Eng., 2020

Edit Distance Embedding using Convolutional Neural Networks.
CoRR, 2020

Amortized Nesterov's Momentum: A Robust Momentum and Its Application to Deep Learning.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Convolutional Embedding for Edit Distance.
Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020

Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Tight Convergence Rate of Gradient Descent for Eigenvalue Computation.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

2019
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning.
CoRR, 2019

Direct Acceleration of SAGA using Sampled Negative Momentum.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Norm-Range Partition: A Univiseral Catalyst for LSH based Maximum Inner Product Search (MIPS).
CoRR, 2018

Direct Acceleration of SAGA using Sampled Negative Momentum.
CoRR, 2018

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning.
CoRR, 2018

A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates.
Proceedings of the 35th International Conference on Machine Learning, 2018

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

ASVRG: Accelerated Proximal SVRG.
Proceedings of The 10th Asian Conference on Machine Learning, 2018


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