Search Results for author: Minshuo Chen

Found 43 papers, 3 papers with code

Gradient Guidance for Diffusion Models: An Optimization Perspective

no code implementations23 Apr 2024 Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang

To remedy this issue, we consider a modified form of gradient guidance based on a forward prediction loss, which leverages the pre-trained score function to preserve the latent structure in generated samples.

An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization

no code implementations11 Apr 2024 Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang

In this paper, we review emerging applications of diffusion models, understanding their sample generation under various controls.

Diffusion Model for Data-Driven Black-Box Optimization

no code implementations20 Mar 2024 Zihao Li, Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Yinyu Ye, Minshuo Chen, Mengdi Wang

In this paper, we focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization over complex structured variables.

Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory

no code implementations18 Mar 2024 Hengyu Fu, Zhuoran Yang, Mengdi Wang, Minshuo Chen

Conditional diffusion models serve as the foundation of modern image synthesis and find extensive application in fields like computational biology and reinforcement learning.

Image Generation reinforcement-learning

Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models

no code implementations3 Mar 2024 Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, Yuting Wei

Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties.

Image Generation

Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks

no code implementations16 Oct 2023 Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang

In fact, human preference data are now used with classic reinforcement learning algorithms such as actor-critic methods, which involve evaluating an intermediate policy over a reward learned from human preference data with distribution shift, known as off-policy evaluation (OPE).

Off-policy evaluation reinforcement-learning

Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds

no code implementations25 Sep 2023 Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao

As a result, by properly choosing the network size and hyperparameters, NPMD can find an $\epsilon$-optimal policy with $\widetilde{O}(\epsilon^{-\frac{d}{\alpha}-2})$ samples in expectation, where $\alpha\in(0, 1]$ indicates the smoothness of environment.

Policy Gradient Methods Reinforcement Learning (RL)

Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight

no code implementations6 Jul 2023 Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai

This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be exponentially hard in the worst-case.

Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks

no code implementations4 Jul 2023 Kaiqi Zhang, Zixuan Zhang, Minshuo Chen, Yuma Takeda, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang

Convolutional residual neural networks (ConvResNets), though overparameterized, can achieve remarkable prediction performance in practice, which cannot be well explained by conventional wisdom.

Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories

no code implementations26 Jun 2023 Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao

Existing theories on deep nonparametric regression have shown that when the input data lie on a low-dimensional manifold, deep neural networks can adapt to the intrinsic data structures.


Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations

no code implementations2 Jun 2023 Minshuo Chen, Jie Meng, Yu Bai, Yinyu Ye, H. Vincent Poor, Mengdi Wang

We present algorithms and establish near-optimal regret upper and lower bounds, of the form $\tilde{\mathcal{O}}(\sqrt{{\rm poly}(H) SAK})$, for RL in the delayed and missing observation settings.

Reinforcement Learning (RL)

Counterfactual Generative Models for Time-Varying Treatments

no code implementations25 May 2023 Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu

Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others.

counterfactual Decision Making +2

AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

2 code implementations18 Mar 2023 Qingru Zhang, Minshuo Chen, Alexander Bukharin, Nikos Karampatziakis, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao

Therefore, many fine-tuning methods are proposed to learn incremental updates of pre-trained weights in a parameter efficient way, e. g., low-rank increments.

Question Answering Text Generation

On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds

no code implementations25 Feb 2023 Biraj Dahal, Alex Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao

Many existing experiments have demonstrated that generative networks can generate high-dimensional complex data from a low-dimensional easy-to-sample distribution.

Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data

no code implementations14 Feb 2023 Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang

Furthermore, the generated distribution based on the estimated score function captures the data geometric structures and converges to a close vicinity of the data distribution.

Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint

no code implementations9 Jun 2022 Hao liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao

Overparameterized neural networks enjoy great representation power on complex data, and more importantly yield sufficiently smooth output, which is crucial to their generalization and robustness.

Image Classification

Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks

no code implementations6 Jun 2022 Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao

We consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks.

Off-policy evaluation

A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks

no code implementations4 May 2022 Jie Wang, Minshuo Chen, Tuo Zhao, Wenjing Liao, Yao Xie

Based on the approximation theory of neural networks, we show that the neural network IPM test has the type-II risk in the order of $n^{-(s+\beta)/d}$, which is in the same order of the type-II risk as the H\"older IPM test.

Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network

no code implementations6 Jan 2022 Siawpeng Er, Edward Liu, Minshuo Chen, Yan Li, Yuqi Liu, Tuo Zhao, Hua Wang

This paper presents a deep learning assisted synthesis approach for direct end-to-end generation of RF/mm-wave passive matching network with 3D EM structures.

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces

no code implementations1 Jan 2022 Hao liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao

Learning operators between infinitely dimensional spaces is an important learning task arising in wide applications in machine learning, imaging science, mathematical modeling and simulations, etc.

Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL

1 code implementation NeurIPS 2021 Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao

Theoretically, under a weak coverage assumption that the experience dataset contains enough information about the optimal policy, we prove that for an episodic mean-field MDP with a horizon $H$ and $N$ training trajectories, SAFARI attains a sub-optimality gap of $\mathcal{O}(H^2d_{\rm eff} /\sqrt{N})$, where $d_{\rm eff}$ is the effective dimension of the function class for parameterizing the value function, but independent on the number of agents.

Multi-agent Reinforcement Learning

Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect

no code implementations ICLR 2022 Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao

Moreover, we rigorously establish an implicit bias of GD induced by such a large learning rate, termed 'balancing', meaning that magnitudes of $X$ and $Y$ at the limit of GD iterations will be close even if their initialization is significantly unbalanced.

Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks

no code implementations7 Sep 2021 Hao liu, Minshuo Chen, Tuo Zhao, Wenjing Liao

Most of existing statistical theories on deep neural networks have sample complexities cursed by the data dimension and therefore cannot well explain the empirical success of deep learning on high-dimensional data.

Binary Classification

Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization

1 code implementation ACL 2021 Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen

The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ``lottery tickets'', and training a certain collection of them (i. e., a subnetwork) can match the performance of the full model.

Model Compression Multi-Task Learning

Differentiable Top-k with Optimal Transport

no code implementations NeurIPS 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

Finding the k largest or smallest elements from a collection of scores, i. e., top-k operation, is an important model component widely used in information retrieval, machine learning, and data mining.

Information Retrieval Retrieval

Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks

no code implementations3 Nov 2020 Minshuo Chen, Hao liu, Wenjing Liao, Tuo Zhao

Our theory shows that deep neural networks are adaptive to the low-dimensional geometric structures of the covariates, and partially explains the success of deep learning for causal inference.

Causal Inference

Differentiable Top-$k$ with Optimal Transport

no code implementations NeurIPS Workshop LMCA 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

The top-$k$ operation, i. e., finding the $k$ largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.

Information Retrieval Retrieval

How Important is the Train-Validation Split in Meta-Learning?

no code implementations12 Oct 2020 Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham Kakade, Huan Wang, Caiming Xiong

A common practice in meta-learning is to perform a train-validation split (\emph{train-val method}) where the prior adapts to the task on one split of the data, and the resulting predictor is evaluated on another split.


Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers

no code implementations25 Aug 2020 David Munzer, Siawpeng Er, Minshuo Chen, Yan Li, Naga S. Mannem, Tuo Zhao, Hua Wang

We propose using machine learning models for the direct synthesis of on-chip electromagnetic (EM) passive structures to enable rapid or even automated designs and optimizations of RF/mm-Wave circuits.

BIG-bench Machine Learning

Towards Understanding Hierarchical Learning: Benefits of Neural Representations

no code implementations NeurIPS 2020 Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher

When the trainable network is the quadratic Taylor model of a wide two-layer network, we show that neural representation can achieve improved sample complexities compared with the raw input: For learning a low-rank degree-$p$ polynomial ($p \geq 4$) in $d$ dimension, neural representation requires only $\tilde{O}(d^{\lceil p/2 \rceil})$ samples, while the best-known sample complexity upper bound for the raw input is $\tilde{O}(d^{p-1})$.

Differentiable Top-k Operator with Optimal Transport

no code implementations16 Feb 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

The top-k operation, i. e., finding the k largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.

Information Retrieval Retrieval

Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks

no code implementations10 Feb 2020 Minshuo Chen, Wenjing Liao, Hongyuan Zha, Tuo Zhao

Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning.

Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds

no code implementations NeurIPS 2019 Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao

The network size scales exponentially in the approximation error, with an exponent depending on the intrinsic dimension of the data and the smoothness of the function.

On Generalization Bounds of a Family of Recurrent Neural Networks

no code implementations ICLR 2019 Minshuo Chen, Xingguo Li, Tuo Zhao

We remark: (1) Our generalization bound for vanilla RNNs is significantly tighter than the best of existing results; (2) We are not aware of any other generalization bounds for MGU, LSTM, and Conv RNNs in the exiting literature; (3) We demonstrate the advantages of these variants in generalization.

Generalization Bounds PAC learning

Towards Understanding the Importance of Shortcut Connections in Residual Networks

no code implementations NeurIPS 2019 Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao

We show, however, that gradient descent combined with proper normalization, avoids being trapped by the spurious local optimum, and converges to a global optimum in polynomial time, when the weight of the first layer is initialized at 0, and that of the second layer is initialized arbitrarily in a ball.

Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery

no code implementations NeurIPS 2019 Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao

It therefore demonstrates the adaptivity of deep ReLU networks to low-dimensional geometric structures of data, and partially explains the power of deep ReLU networks in tackling high-dimensional data with low-dimensional geometric structures.


On Scalable and Efficient Computation of Large Scale Optimal Transport

no code implementations ICLR Workshop DeepGenStruct 2019 Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha

Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses.

Domain Adaptation

On Computation and Generalization of GANs with Spectrum Control

no code implementations28 Dec 2018 Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao

Specifically, we propose a new reparameterization approach for the weight matrices of the discriminator in GANs, which allows us to directly manipulate the spectra of the weight matrices through various regularizers and constraints, without intensively computing singular value decompositions.

Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization

no code implementations NeurIPS 2018 Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao

Specifically, our goal is to estimate the principle component of time series data with respect to the covariance matrix of the stationary distribution.

Dimensionality Reduction Stochastic Optimization +2

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