Search Results for author: Kangwook Lee

Found 13 papers, 3 papers with code

Coded-InvNet for Resilient Prediction Serving Systems

no code implementations11 Jun 2021 Tuan Dinh, Kangwook Lee

Inspired by a new coded computation algorithm for invertible functions, we propose Coded-InvNet a new approach to design resilient prediction serving systems that can gracefully handle stragglers or node failures.


Permutation-Based SGD: Is Random Optimal?

no code implementations19 Feb 2021 Shashank Rajput, Kangwook Lee, Dimitris Papailiopoulos

However, for general strongly convex functions, random permutations are optimal.

SLM: Learning a Discourse Language Representation with Sentence Unshuffling

no code implementations EMNLP 2020 Haejun Lee, Drew A. Hudson, Kangwook Lee, Christopher D. Manning

We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner.

Language Modelling

Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification

1 code implementation29 Oct 2020 Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris Papailiopoulos

The techniques usually require choosing a static compression ratio, often requiring users to balance the trade-off between model accuracy and per-iteration speedup.


Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information

no code implementations16 Mar 2020 Changhun Jo, Kangwook Lee

Ahn et al. (2018) firstly characterized the optimal sample complexity in the presence of graph side information, but the results are limited due to strict, unrealistic assumptions made on the unknown latent preference matrix and the structure of user clusters.

Recommendation Systems

FR-Train: A Mutual Information-Based Approach to Fair and Robust Training

1 code implementation ICML 2020 Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh

Trustworthy AI is a critical issue in machine learning where, in addition to training a model that is accurate, one must consider both fair and robust training in the presence of data bias and poisoning.

Data Poisoning Fairness

Binary Rating Estimation with Graph Side Information

no code implementations NeurIPS 2018 Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, Changho Suh

Considering a simple correlation model between a rating matrix and a graph, we characterize the sharp threshold on the number of observed entries required to recover the rating matrix (called the optimal sample complexity) as a function of the quality of graph side information (to be detailed).

Hypergraph Spectral Clustering in the Weighted Stochastic Block Model

no code implementations23 May 2018 Kwangjun Ahn, Kangwook Lee, Changho Suh

Our main contribution lies in performance analysis of the poly-time algorithms under a random hypergraph model, which we name the weighted stochastic block model, in which objects and multi-way measures are modeled as nodes and weights of hyperedges, respectively.

Stochastic Block Model

Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings

no code implementations ICLR 2018 Kangwook Lee, Hoon Kim, Changho Suh

Recently, Shrivastava et al. (2017) propose Simulated+Unsupervised (S+U) learning: It first learns a mapping from synthetic data to real data, translates a large amount of labeled synthetic data to the ones that resemble real data, and then trains a learning model on the translated data.

Gaze Estimation

Community Recovery in Hypergraphs

no code implementations12 Sep 2017 Kwangjun Ahn, Kangwook Lee, Changho Suh

The objective of the problem is to cluster data points into distinct communities based on a set of measurements, each of which is associated with the values of a certain number of data points.

Face Clustering Motion Segmentation

Speeding Up Distributed Machine Learning Using Codes

no code implementations8 Dec 2015 Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, Kannan Ramchandran

We focus on two of the most basic building blocks of distributed learning algorithms: matrix multiplication and data shuffling.

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