activity
20182023
most citedDoes Data Augmentation Lead to Positive Margin?

6 citations · 20 across the 11 of their papers we have counts for

collaborators

11 papers

cs.LG2023

Monotone deep Boltzmann machines

Zhili Feng, Ezra Winston, J. Zico Kolter

Deep Boltzmann machines (DBMs), one of the first ``deep'' learning methods ever studied, are multi-layered probabilistic models governed by a pairwise energy function that describe…

cs.CV2023

Text Descriptions are Compressive and Invariant Representations for Visual Learning

Zhili Feng, Anna Bair, J. Zico Kolter

Modern image classification is based upon directly predicting classes via large discriminative networks, which do not directly contain information about the intuitive visual featur…

cs.LG2021★ 3 cited

Learning-Augmented -means Clustering

Jon C. Ergun, Zhili Feng, Sandeep Silwal +2

-means clustering is a well-studied problem due to its wide applicability. Unfortunately, there exist strong theoretical limits on the performance of any algorithm for the -m…

cs.LG2021

Non-PSD Matrix Sketching with Applications to Regression and Optimization

Zhili Feng, Fred Roosta, David P. Woodruff

A variety of dimensionality reduction techniques have been applied for computations involving large matrices. The underlying matrix is randomly compressed into a smaller one, while…

cs.LG2021★ 2 cited

Provable Adaptation across Multiway Domains via Representation Learning

Zhili Feng, Shaobo Han, Simon S. Du

This paper studies zero-shot domain adaptation where each domain is indexed on a multi-dimensional array, and we only have data from a small subset of domains. Our goal is to produ…

cs.DS2019★ 1 cited

Dimensionality Reduction for Sum-of-Distances Metric

Zhili Feng, Praneeth Kacham, David P. Woodruff

We give a dimensionality reduction procedure to approximate the sum of distances of a given set of points in to any "shape" that lies in a -dimensional subspace. Here,…

cs.CL2019★ 4 cited

A Structured Learning Approach to Temporal Relation Extraction

Qiang Ning, Zhili Feng, Dan Roth

Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on,…

cs.CL2019★ 2 cited

Joint Reasoning for Temporal and Causal Relations

Qiang Ning, Zhili Feng, Hao Wu +1

Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must be before its effect in time, temporal and cau…

cs.CL2019★ 1 cited

CogCompTime: A Tool for Understanding Time in Natural Language Text

Qiang Ning, Ben Zhou, Zhili Feng +2

Automatic extraction of temporal information in text is an important component of natural language understanding. It involves two basic tasks: (1) Understanding time expressions th…

cs.LG2019★ 6 cited

Does Data Augmentation Lead to Positive Margin?

Shashank Rajput, Zhili Feng, Zachary Charles +2

Data augmentation (DA) is commonly used during model training, as it significantly improves test error and model robustness. DA artificially expands the training set by applying ra…

cs.LG2018★ 1 cited

Online learning with graph-structured feedback against adaptive adversaries

Zhili Feng, Po-Ling Loh

We derive upper and lower bounds for the policy regret of -round online learning problems with graph-structured feedback, where the adversary is nonoblivious but assumed to have…