#### Filter Results:

- Full text PDF available (18)

#### Publication Year

2011

2018

- This year (7)
- Last 5 years (18)
- Last 10 years (20)

#### Publication Type

#### Co-author

#### Journals and Conferences

Learn More

- Weijie Su, Stephen P. Boyd, Emmanuel J. CandÃ¨s
- NIPS
- 2014

We derive a second-order ordinary differential equation (ODE), which is the limit of Nesterovâ€™s accelerated gradient method. This ODE exhibits approximate equivalence to Nesterovâ€™s scheme and thusâ€¦ (More)

- MaÅ‚gorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su, Emmanuel J. CandÃ¨s
- The annals of applied statistics
- 2015

We introduce a new estimator for the vector of coefficients Î² in the linear model y = XÎ² + z, where X has dimensions n Ã— p with p possibly larger than n. SLOPE, short for Sorted L-One Penalizedâ€¦ (More)

- Emmanuel J. CandÃ¨s, Weijie Su
- ArXiv
- 2015

We consider high-dimensional sparse regression problems in which we observe y = XÎ² + z, where X is an n Ã— p design matrix and z is an n-dimensional vector of independent Gaussian errors, each withâ€¦ (More)

We introduce a novel method for sparse regression and variable selection, which is inspired by modern ideas in multiple testing. Imagine we have observations from the linear model y = XÎ²+ z, then weâ€¦ (More)

- Weijie Su, Malgorzata Bogdan, Emmanuel J. CandÃ¨s
- ArXiv
- 2015

In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the important variables withâ€¦ (More)

In this note we give a proof showing that even though the number of false discoveries and the total number of discoveries are not continuous functions of the parameters, the formulas we obtain forâ€¦ (More)

- Cynthia Dwork, Weijie Su, Li Zhang
- ArXiv
- 2015

We provide the first differentially private algorithms for controlling the false discovery rate (FDR) in multiple hypothesis testing, with essentially no loss in power under certain conditions. Ourâ€¦ (More)

- Lucas Janson, Weijie Su
- 2015

We present a novel method for controlling the k-familywise error rate (k-FWER) in the linear regression setting using the knockoffs framework first introduced by Barber and CandÃ¨s. Our procedure,â€¦ (More)

- Weijie Su, Yuancheng Zhu
- ArXiv
- 2018

Stochastic gradient descent (SGD) is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large. However, despite an ever-increasingâ€¦ (More)

We consider in this work small random perturbations of a nonlinear oscillator with frictionâ€“type dissipation. We rigorously prove that under nonâ€“degenerate perturbations of multiplicative noise type,â€¦ (More)