uclaml
The artificial general intelligence lab (formerly known as statistical machine learning lab) at UCLA is led by Prof. Quanquan Gu in the computer science dept.
Department of Computer Science, UCLA
Pinned Repositories
Frank-Wolfe-AdvML
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks (AAAI'20)
MoE
Towards Understanding the Mixture-of-Experts Layer in Deep Learning
NeuralUCB
Padam
Partially Adaptive Momentum Estimation method in the paper "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" (accepted by IJCAI 2020)
PDE
Official repo of Progressive Data Expansion: data, code and evaluation
RayS
RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)
Rephrase-and-Respond
Official repo of Respond-and-Respond: data, code, and evaluation
SPIN
The official implementation of Self-Play Fine-Tuning (SPIN)
SPPO
The official implementation of Self-Play Preference Optimization (SPPO)
ucla-covid19-forecasts
uclaml's Repositories
uclaml/SPIN
The official implementation of Self-Play Fine-Tuning (SPIN)
uclaml/SPPO
The official implementation of Self-Play Preference Optimization (SPPO)
uclaml/Rephrase-and-Respond
Official repo of Respond-and-Respond: data, code, and evaluation
uclaml/RayS
RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)
uclaml/Padam
Partially Adaptive Momentum Estimation method in the paper "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" (accepted by IJCAI 2020)
uclaml/NeuralUCB
uclaml/PDE
Official repo of Progressive Data Expansion: data, code and evaluation
uclaml/MoE
Towards Understanding the Mixture-of-Experts Layer in Deep Learning
uclaml/ucla-covid19-forecasts
uclaml/Frank-Wolfe-AdvML
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks (AAAI'20)
uclaml/NeuralTS
uclaml/CS269-Winter2019
uclaml/CS161-Winter2020
Fundamentals of Artificial Intelligence
uclaml/FedLinUCB
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
uclaml/GFA-RFE
Uncertainty-Aware Reward-Free Exploration with General Function Approximation
uclaml/PhyGCN
uclaml/VACDB
Variance-aware Contextual Dueling Bandits
uclaml/Benign-Overfitting-CNN
Benign Overfitting in Two-layer Convolutional Neural Networks
uclaml/Benign_ReLU_CNN
uclaml/CS260-Fall2022
uclaml/CS260-Spring2020
Machine Learning
uclaml/CW-OFUL
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
uclaml/HF-UCRL-VTR
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
uclaml/LDP-UCRL-VTR
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes
uclaml/pretrain-finetune-SGD
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
uclaml/POWERS
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs
uclaml/RobustOFUL
Corruption-robust linear contextual bandits
uclaml/multipass-SGD
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
uclaml/SSL_Pseudo_Labeler
uclaml/SSLGC
Selective Sampling on Graphs for Classification