Search Results for author:
Found 16 papers, 6 papers with code
The Benefit of Being Bayesian in Online Conformal Prediction
By converting the target confidence levels into quantile levels, the problem can be reduced to predicting the quantiles (in hindsight) of a sequentially revealed data sequence.
Turbo your multi-modal classification with contrastive learning
Contrastive learning has become one of the most impressive approaches for multi-modal representation learning.
Adapting Conformal Prediction to Distribution Shifts Without Labels
Conformal prediction (CP) enables machine learning models to output prediction sets with guaranteed coverage rate, assuming exchangeable data.
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning
A key challenge in lifelong reinforcement learning (RL) is the loss of plasticity, where previous learning progress hinders an agent's adaptation to new tasks.
Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks
As the model structure adheres to the flow routing mechanisms and hydraulic constraints in urban drainage networks, it provides an interpretable and effective solution for data-driven surrogate modelling.
Discounted Adaptive Online Learning: Towards Better Regularization
We study online learning in adversarial nonstationary environments.
Efficient Dynamic-NeRF Based Volumetric Video Coding with Rate Distortion Optimization
Volumetric videos, benefiting from immersive 3D realism and interactivity, hold vast potential for various applications, while the tremendous data volume poses significant challenges for compression.
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
In this work, we provide a different perspective based on online learning that underscores the importance of Adam's algorithmic components.
Improving Adaptive Online Learning Using Refined Discretization
Motivated by the pursuit of instance optimality, we propose a new algorithm that simultaneously achieves ($i$) the AdaGrad-style second order gradient adaptivity; and ($ii$) the comparator norm adaptivity also known as "parameter freeness" in the literature.
Optimal Comparator Adaptive Online Learning with Switching Cost
Practical online learning tasks are often naturally defined on unconstrained domains, where optimal algorithms for general convex losses are characterized by the notion of comparator adaptivity.
Generative Compression for Face Video: A Hybrid Scheme
As the latest video coding standard, versatile video coding (VVC) has shown its ability in retaining pixel quality.
PDE-Based Optimal Strategy for Unconstrained Online Learning
Unconstrained Online Linear Optimization (OLO) is a practical problem setting to study the training of machine learning models.
PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters.
Ranked #1 on
Chinese Spell Checking
on SIGHAN 2015
Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory
Next, considering a related problem called online learning with memory, we construct a novel strongly adaptive algorithm that uses our first contribution as a building block.
Provable Hierarchical Imitation Learning via EM
Due to recent empirical successes, the options framework for hierarchical reinforcement learning is gaining increasing popularity.
Bias-Compensated Normalized Maximum Correntropy Criterion Algorithm for System Identification with Noisy Input
This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment.
