Tengyu Ma

verfied profile

∙ 0 followers
Princeton University ∙ Stanford University

Assistant Professor of Computer Science and Statistics at Stanford University

Featured Co-authors

Sergey Levine
240 publications
Xi Chen
162 publications
Trevor Darrell
141 publications
Yi Zhang
116 publications
Stefano Ermon
111 publications
Qiang Liu
105 publications
Percy Liang
104 publications
Chelsea Finn
86 publications
Jiawei Zhang
80 publications
David P. Woodruff
64 publications
James Zou
59 publications

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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
Individual Calibration with Randomized Forecasting

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

read it
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

read it
Federated Accelerated Stochastic Gradient Descent

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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
Verified Uncertainty Calibration

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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it