129 citations · 372 across the 27 of their papers we have counts for
27 papers
Active Inverse Learning in Stackelberg Trajectory Games
William Ward, Yue Yu, Jacob Levy +3
Game-theoretic inverse learning is the problem of inferring a player's objectives from their actions. We formulate an inverse learning problem in a Stackelberg game between a leade…
Higher Order Gauge Equivariant CNNs on Riemannian Manifolds and Applications
Gianfranco Cortes, Yue Yu, Robin Chen +3
With the advent of group equivariant convolutions in deep networks literature, spherical CNNs with -equivariant layers have been developed to cope with data that ar…
Soft-Bellman Equilibrium in Affine Markov Games: Forward Solutions and Inverse Learning
Shenghui Chen, Yue Yu, David Fridovich-Keil +1
Markov games model interactions among multiple players in a stochastic, dynamic environment. Each player in a Markov game maximizes its expected total discounted reward, which depe…
INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation
Ning Liu, Yue Yu, Huaiqian You +1
Neural operators, which emerge as implicit solution operators of hidden governing equations, have recently become popular tools for learning responses of complex real-world physica…
Polynomial Distributions and Transformations
Yue Yu, Pavel Loskot
Polynomials are common algebraic structures, which are often used to approximate functions including probability distributions. This paper proposes to directly define polynomial di…
Transferability Estimation Based On Principal Gradient Expectation
Huiyan Qi, Lechao Cheng, Jingjing Chen +4
Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks. The standard approach is pre-training followed by fine-tuning…
Cost Design in Atomic Routing Games
Yue Yu, Shenghui Chen, David Fridovich-Keil +1
An atomic routing game is a multiplayer game on a directed graph. Each player in the game chooses a path -- a sequence of links that connect its origin node to its destination node…
Physics-Informed Deep Neural Operator Networks
Somdatta Goswami, Aniruddha Bora, Yue Yu +1
Standard neural networks can approximate general nonlinear operators, represented either explicitly by a combination of mathematical operators, e.g., in an advection-diffusion-reac…
Extrapolated Proportional-Integral Projected Gradient Method for Conic Optimization
Yue Yu, Purnanand Elango, Behçet Açıkmeşe +1
Conic optimization is the minimization of a convex quadratic function subject to conic constraints. We introduce a novel first-order method for conic optimization, named \emph{extr…
Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network
Huaiqian You, Yue Yu, Marta D'Elia +2
Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machin…
Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence
Ruoxi Chen, Haibo Jin, Haibin Zheng +2
The vulnerabilities of deep learning models towards adversarial attacks have attracted increasing attention, especially when models are deployed in security-critical domains. Numer…
Proportional-Integral Projected Gradient Method for Infeasibility Detection in Conic Optimization
Yue Yu, Ufuk Topcu
A constrained optimization problem is primal infeasible if its constraints cannot be satisfied, and dual infeasible if the constraints of its dual problem cannot be satisfied. We p…
DAGs with No Curl: An Efficient DAG Structure Learning Approach
Yue Yu, Tian Gao, Naiyu Yin +1
Recently directed acyclic graph (DAG) structure learning is formulated as a constrained continuous optimization problem with continuous acyclicity constraints and was solved iterat…
PanGu-: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
Wei Zeng, Xiaozhe Ren, Teng Su +35
Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as GPT-3 have demon…
On Controllability and Persistency of Excitation in Data-Driven Control: Extensions of Willems' Fundamental Lemma
Yue Yu, Shahriar Talebi, Henk J. van Waarde +3
Willems' fundamental lemma asserts that all trajectories of a linear time-invariant system can be obtained from a finite number of measured ones, assuming that controllability and…
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
Dennis Wei, Tian Gao, Yue Yu
This paper re-examines a continuous optimization framework dubbed NOTEARS for learning Bayesian networks. We first generalize existing algebraic characterizations of acyclicity to…
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
Lu Zhang, Mo Yu, Tian Gao +1
Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend e…
Proportional-Integral Projected Gradient Method for Model Predictive Control
Yue Yu, Purnanand Elango, Behçet Açikmeşe
Recently there has been an increasing interest in primal-dual methods for model predictive control (MPC), which require minimizing the (augmented) Lagrangian at each iteration. We…
RC Circuits based Distributed Conditional Gradient Method
Yue Yu, Behçet Açıkmeşe
We consider distributed optimization on undirected connected graphs. We propose a novel distributed conditional gradient method with (O(1/\sqrt{k})) convergence. Compared with exis…
RLC Circuits based Distributed Mirror Descent Method
Yue Yu, Behçet Açıkmeşe
We consider distributed optimization with smooth convex objective functions defined on an undirected connected graph. Inspired by mirror descent mehod and RLC circuits, we propose…
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Yue Yu, Jie Chen, Tian Gao +1
Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a challenging combinatorial problem, owing to the intractable search space superexponential…
Tolling for Constraint Satisfaction in Markov Decision Process Congestion Games
Sarah H. Q. Li, Yue Yu, Daniel Calderone +2
Markov decision process (MDP) congestion game is an extension of classic congestion games, where a continuous population of selfish agents solves Markov decision processes with con…
Stochastic Bregman Parallel Direction Method of Multipliers for Distributed Optimization
Yue Yu, Behçet Açıkmeşe
Bregman parallel direction method of multipliers (BPDMM) efficiently solves distributed optimization over a network, which arises in a wide spectrum of collaborative multi-agent le…
Exploring Fast and Communication-Efficient Algorithms in Large-scale Distributed Networks
Yue Yu, Jiaxiang Wu, Junzhou Huang
The communication overhead has become a significant bottleneck in data-parallel network with the increasing of model size and data samples. In this work, we propose a new algorithm…