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
213 publications
Xi Chen
138 publications
Trevor Darrell
134 publications
Stefano Ermon
100 publications
Yi Zhang
100 publications
Percy Liang
91 publications
Qiang Liu
83 publications
Jiawei Zhang
70 publications
Chelsea Finn
70 publications
David P. Woodruff
57 publications
Jason D. Lee
52 publications

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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Finding Approximate Local Minima Faster than Gradient Descent

We design a non-convex second-order optimization algorithm that is guara...
11/03/2016 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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A Non-generative Framework and Convex Relaxations for Unsupervised Learning

We give a novel formal theoretical framework for unsupervised learning w...
10/04/2016 ∙ by Elad Hazan, et al. ∙ 0 ∙ share

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Gradient Descent Learns Linear Dynamical Systems

We prove that gradient descent efficiently converges to the global optim...
09/16/2016 ∙ by Moritz Hardt, et al. ∙ 0 ∙ share

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Provable Algorithms for Inference in Topic Models

Recently, there has been considerable progress on designing algorithms w...
05/27/2016 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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Matrix Completion has No Spurious Local Minimum

Matrix completion is a basic machine learning problem that has wide appl...
05/24/2016 ∙ by Rong Ge, et al. ∙ 0 ∙ share

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Linear Algebraic Structure of Word Senses, with Applications to Polysemy

Word embeddings are ubiquitous in NLP and information retrieval, but it'...
01/14/2016 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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Communication Lower Bounds for Statistical Estimation Problems via a Distributed Data Processing Inequality

We study the tradeoff between the statistical error and communication co...
06/24/2015 ∙ by Mark Braverman, et al. ∙ 0 ∙ share

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Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms

Tensor rank and low-rank tensor decompositions have many applications in...
04/21/2015 ∙ by Rong Ge, et al. ∙ 0 ∙ share

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Simple, Efficient, and Neural Algorithms for Sparse Coding

Sparse coding is a basic task in many fields including signal processing...
03/02/2015 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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RAND-WALK: A Latent Variable Model Approach to Word Embeddings

Semantic word embeddings represent the meaning of a word via a vector, a...
02/12/2015 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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More Algorithms for Provable Dictionary Learning

In dictionary learning, also known as sparse coding, the algorithm is gi...
01/03/2014 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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