Tengyu Ma

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Princeton University ∙ Stanford University

Assistant Professor of Computer Science and Statistics at Stanford University

Featured Co-authors

Sergey Levine
256 publications
Xi Chen
172 publications
Trevor Darrell
150 publications
Yi Zhang
135 publications
Stefano Ermon
115 publications
Qiang Liu
114 publications
Percy Liang
107 publications
Chelsea Finn
93 publications
Jiawei Zhang
88 publications
Xiaodong He
76 publications
David P. Woodruff
68 publications

Label Noise SGD Provably Prefers Flat Global Minimizers

In overparametrized models, the noise in stochastic gradient descent (SG...
06/11/2021 ∙ by Alex Damian, et al. ∙ 5 ∙ share

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Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System

Recent work (Takanobu et al., 2020) proposed the system-wise evaluation ...
06/09/2021 ∙ by Zichuan Lin, et al. ∙ 0 ∙ share

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Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

Recent works in self-supervised learning have advanced the state-of-the-...
06/08/2021 ∙ by Jeff Z. HaoChen, et al. ∙ 2 ∙ share

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Why Do Local Methods Solve Nonconvex Problems?

Non-convex optimization is ubiquitous in modern machine learning. Resear...
03/24/2021 ∙ by Tengyu Ma, et al. ∙ 0 ∙ share

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Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap

This paper presents a new model-free algorithm for episodic finite-horiz...
02/09/2021 ∙ by Haike Xu, et al. ∙ 0 ∙ share

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Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature

This paper studies model-based bandit and reinforcement learning (RL) wi...
02/08/2021 ∙ by Kefan Dong, et al. ∙ 0 ∙ share

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In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness

Consider a prediction setting where a few inputs (e.g., satellite images...
12/08/2020 ∙ by Sang Michael Xie, et al. ∙ 8 ∙ share

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Meta-learning Transferable Representations with a Single Target Domain

Recent works found that fine-tuning and joint training—two popular appro...
11/03/2020 ∙ by Hong Liu, et al. ∙ 0 ∙ share

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Beyond Lazy Training for Over-parameterized Tensor Decomposition

Over-parametrization is an important technique in training neural networ...
10/22/2020 ∙ by Xiang Wang, et al. ∙ 0 ∙ share

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Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling

Document-level relation extraction (RE) poses new challenges compared to...
10/21/2020 ∙ by Wenxuan Zhou, et al. ∙ 0 ∙ share

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Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Self-training algorithms, which train a model to fit pseudolabels predic...
10/07/2020 ∙ by Colin Wei, et al. ∙ 18 ∙ share

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Entity and Evidence Guided Relation Extraction for DocRED

Document-level relation extraction is a challenging task which requires ...
08/27/2020 ∙ by Kevin Huang, et al. ∙ 0 ∙ share

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Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK

We consider the dynamic of gradient descent for learning a two-layer neu...
07/09/2020 ∙ by Yuanzhi Li, et al. ∙ 8 ∙ share

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Simplifying Models with Unlabeled Output Data

We focus on prediction problems with high-dimensional outputs that are s...
06/29/2020 ∙ by Sang Michael Xie, et al. ∙ 8 ∙ share

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Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization

Real-world large-scale datasets are heteroskedastic and imbalanced – lab...
06/29/2020 ∙ by Kaidi Cao, et al. ∙ 1 ∙ share

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Active Online Domain Adaptation

Online machine learning systems need to adapt to domain shifts. Meanwhil...
06/25/2020 ∙ by Yining Chen, et al. ∙ 9 ∙ share

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Individual Calibration with Randomized Forecasting

Machine learning applications often require calibrated predictions, e.g....
06/18/2020 ∙ by Shengjia Zhao, et al. ∙ 0 ∙ share

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Self-training Avoids Using Spurious Features Under Domain Shift

In unsupervised domain adaptation, existing theory focuses on situations...
06/17/2020 ∙ by Yining Chen, et al. ∙ 0 ∙ share

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Federated Accelerated Stochastic Gradient Descent

We propose Federated Accelerated Stochastic Gradient Descent (FedAc), a ...
06/16/2020 ∙ by Honglin Yuan, et al. ∙ 0 ∙ share

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Model-based Adversarial Meta-Reinforcement Learning

Meta-reinforcement learning (meta-RL) aims to learn from multiple traini...
06/16/2020 ∙ by Zichuan Lin, et al. ∙ 11 ∙ share

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Shape Matters: Understanding the Implicit Bias of the Noise Covariance

The noise in stochastic gradient descent (SGD) provides a crucial implic...
06/15/2020 ∙ by Jeff Z. HaoChen, et al. ∙ 9 ∙ share

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MOPO: Model-based Offline Policy Optimization

Offline reinforcement learning (RL) refers to the problem of learning po...
05/27/2020 ∙ by Tianhe Yu, et al. ∙ 15 ∙ share

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Robust and On-the-fly Dataset Denoising for Image Classification

Memorization in over-parameterized neural networks could severely hurt g...
03/24/2020 ∙ by Jiaming Song, et al. ∙ 0 ∙ share

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Optimal Regularization Can Mitigate Double Descent

Recent empirical and theoretical studies have shown that many learning a...
03/04/2020 ∙ by Preetum Nakkiran, et al. ∙ 13 ∙ share

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The Implicit and Explicit Regularization Effects of Dropout

Dropout is a widely-used regularization technique, often required to obt...
02/28/2020 ∙ by Colin Wei, et al. ∙ 6 ∙ share

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Understanding Self-Training for Gradual Domain Adaptation

Machine learning systems must adapt to data distributions that evolve ov...
02/26/2020 ∙ by Ananya Kumar, et al. ∙ 5 ∙ share

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Variable-Viewpoint Representations for 3D Object Recognition

For the problem of 3D object recognition, researchers using deep learnin...
02/08/2020 ∙ by Tengyu Ma, et al. ∙ 12 ∙ share

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Bootstrapping the Expressivity with Model-based Planning

We compare the model-free reinforcement learning with the model-based ap...
10/14/2019 ∙ by Kefan Dong, et al. ∙ 16 ∙ share

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Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin

For linear classifiers, the relationship between (normalized) output mar...
10/09/2019 ∙ by Colin Wei, et al. ∙ 7 ∙ share

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Verified Uncertainty Calibration

Applications such as weather forecasting and personalized medicine deman...
09/23/2019 ∙ by Ananya Kumar, et al. ∙ 2 ∙ share

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Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling

Imitation learning, followed by reinforcement learning algorithms, is a ...
07/12/2019 ∙ by Yuping Luo, et al. ∙ 2 ∙ share

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A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning

The aim of multi-task reinforcement learning is two-fold: (1) efficientl...
07/11/2019 ∙ by Nicholas C. Landolfi, et al. ∙ 7 ∙ share

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Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks

Stochastic gradient descent with a large initial learning rate is a wide...
07/10/2019 ∙ by Yuanzhi Li, et al. ∙ 6 ∙ share

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Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

Deep learning algorithms can fare poorly when the training dataset suffe...
06/18/2019 ∙ by Kaidi Cao, et al. ∙ 1 ∙ share

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On the Performance of Thompson Sampling on Logistic Bandits

We study the logistic bandit, in which rewards are binary with success p...
05/12/2019 ∙ by Shi Dong, et al. ∙ 10 ∙ share

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Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation

Existing Rademacher complexity bounds for neural networks rely only on n...
05/09/2019 ∙ by Colin Wei, et al. ∙ 20 ∙ share

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Fixup Initialization: Residual Learning Without Normalization

Normalization layers are a staple in state-of-the-art deep neural networ...
01/27/2019 ∙ by Hongyi Zhang, et al. ∙ 8 ∙ share

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On the Margin Theory of Feedforward Neural Networks

Past works have shown that, somewhat surprisingly, over-parametrization ...
10/12/2018 ∙ by Colin Wei, et al. ∙ 0 ∙ share

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Algorithmic Framework for Model-based Reinforcement Learning with Theoretical Guarantees

While model-based reinforcement learning has empirically been shown to s...
07/10/2018 ∙ by Huazhe Xu, et al. ∙ 0 ∙ share

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Approximability of Discriminators Implies Diversity in GANs

While Generative Adversarial Networks (GANs) have empirically produced i...
06/27/2018 ∙ by Yu Bai, et al. ∙ 0 ∙ share

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Seeing Neural Networks Through a Box of Toys: The Toybox Dataset of Visual Object Transformations

Deep convolutional neural networks (CNNs) have enjoyed tremendous succes...
06/15/2018 ∙ by Xiaohan Wang, et al. ∙ 0 ∙ share

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Optimal Design of Process Flexibility for General Production Systems

Process flexibility is widely adopted as an effective strategy for respo...
06/07/2018 ∙ by Xi Chen, et al. ∙ 0 ∙ share

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A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors

Motivations like domain adaptation, transfer learning, and feature learn...
05/14/2018 ∙ by Mikhail Khodak, et al. ∙ 0 ∙ share

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Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations

We show that the (stochastic) gradient descent algorithm provides an imp...
12/26/2017 ∙ by Yuanzhi Li, et al. ∙ 0 ∙ share

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Algorithmic Regularization in Over-parameterized Matrix Recovery

We study the problem of recovering a low-rank matrix X^ from linear meas...
12/26/2017 ∙ by Yuanzhi Li, et al. ∙ 0 ∙ share

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Learning One-hidden-layer Neural Networks with Landscape Design

We consider the problem of learning a one-hidden-layer neural network: w...
11/01/2017 ∙ by Rong Ge, et al. ∙ 0 ∙ share

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On the Optimization Landscape of Tensor Decompositions

Non-convex optimization with local search heuristics has been widely use...
06/18/2017 ∙ by Rong Ge, et al. ∙ 0 ∙ share

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Generalization and Equilibrium in Generative Adversarial Nets (GANs)

We show that training of generative adversarial network (GAN) may not ha...
03/02/2017 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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Provable learning of Noisy-or Networks

Many machine learning applications use latent variable models to explain...
12/28/2016 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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Identity Matters in Deep Learning

An emerging design principle in deep learning is that each layer of a de...
11/14/2016 ∙ by Moritz Hardt, et al. ∙ 0 ∙ share

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