UCLA Artificial General Intelligence Lab (uclaml)

uclaml

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Company:Department of Computer Science, UCLA

Home Page:http://web.cs.ucla.edu/~qgu

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UCLA Artificial General Intelligence Lab's repositories

SPIN

The official implementation of Self-Play Fine-Tuning (SPIN)

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Rephrase-and-Respond

Official repo of Respond-and-Respond: data, code, and evaluation

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RayS

RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)

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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)

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PDE

Official repo of Progressive Data Expansion: data, code and evaluation

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MoE

Towards Understanding the Mixture-of-Experts Layer in Deep Learning

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Frank-Wolfe-AdvML

A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks (AAAI'20)

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svrg-lowrank

fast nonconvex stochastic variance reduced gradient for low-rank matrix estimation

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CS161-Winter2020

Fundamentals of Artificial Intelligence

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CS260-Fall18

Machine Learning

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CS260-Spring2020

Machine Learning

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CW-OFUL

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions

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FedLinUCB

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits

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HF-UCRL-VTR

Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs

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LDP-UCRL-VTR

Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes

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pretrain-finetune-SGD

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift

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POWERS

Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs

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Benign-Overfitting-CNN

Benign Overfitting in Two-layer Convolutional Neural Networks

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multipass-SGD

Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime

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nonconvex-lowrank

nonconvex Low-rank matrix factorization

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RobustOFUL

Corruption-robust linear contextual bandits

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SSLGC

Selective Sampling on Graphs for Classification

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