Zobrazeno 11 - 20
of 449
pro vyhledávání: '"Lu, Songtao"'
Neuro-symbolic learning (NSL) models complex symbolic rule patterns into latent variable distributions by neural networks, which reduces rule search space and generates unseen rules to improve downstream task performance. Centralized NSL learning inv
Externí odkaz:
http://arxiv.org/abs/2308.15324
The integration of external personalized context information into document-grounded conversational systems has significant potential business value, but has not been well-studied. Motivated by the concept of personalized context-aware document-ground
Externí odkaz:
http://arxiv.org/abs/2308.13760
Bilevel optimization has recently regained interest owing to its applications in emerging machine learning fields such as hyperparameter optimization, meta-learning, and reinforcement learning. Recent results have shown that simple alternating (impli
Externí odkaz:
http://arxiv.org/abs/2306.02422
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities
Recently, min-max optimization problems have received increasing attention due to their wide range of applications in machine learning (ML). However, most existing min-max solution techniques are either single-machine or distributed algorithms coordi
Externí odkaz:
http://arxiv.org/abs/2303.02532
We first propose a decentralized proximal stochastic gradient tracking method (DProxSGT) for nonconvex stochastic composite problems, with data heterogeneously distributed on multiple workers in a decentralized connected network. To save communicatio
Externí odkaz:
http://arxiv.org/abs/2302.14252
Publikováno v:
The Eleventh International Conference on Learning Representations, 2023
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs. Examples include \textit{graph sparsification} that sample
Externí odkaz:
http://arxiv.org/abs/2302.02922
Many real-world problems not only have complicated nonconvex functional constraints but also use a large number of data points. This motivates the design of efficient stochastic methods on finite-sum or expectation constrained problems. In this paper
Externí odkaz:
http://arxiv.org/abs/2212.09513
The graph neural network (GNN) models have presented impressive achievements in numerous machine learning tasks. However, many existing GNN models are shown to be vulnerable to adversarial attacks, which creates a stringent need to build robust GNN a
Externí odkaz:
http://arxiv.org/abs/2210.01002
In recent years, decentralized bilevel optimization problems have received increasing attention in the networking and machine learning communities thanks to their versatility in modeling decentralized learning problems over peer-to-peer networks (e.g
Externí odkaz:
http://arxiv.org/abs/2207.13283
Autor:
Lu, Songtao
Nonconvex constrained optimization problems can be used to model a number of machine learning problems, such as multi-class Neyman-Pearson classification and constrained Markov decision processes. However, such kinds of problems are challenging becau
Externí odkaz:
http://arxiv.org/abs/2207.05650