80 citations · 273 across the 37 of their papers we have counts for
6 papers · 1 filter
Extreme Stochastic Variational Inference: Distributed and Asynchronous
Jiong Zhang, Parameswaran Raman, Shihao Ji +3
Stochastic variational inference (SVI), the state-of-the-art algorithm for scaling variational inference to large-datasets, is inherently serial. Moreover, it requires the paramete…
DFacTo: Distributed Factorization of Tensors
Joon Hee Choi, S. V. N. Vishwanathan
We present a technique for significantly speeding up Alternating Least Squares (ALS) and Gradient Descent (GD), two widely used algorithms for tensor factorization. By exploiting p…
Distributed Stochastic Optimization of the Regularized Risk
Shin Matsushima, Hyokun Yun, Xinhua Zhang +1
Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task. When working with massive data, it is desirable to perform s…
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data
Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan
We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classificat…
Efficiently Sampling Multiplicative Attribute Graphs Using a Ball-Dropping Process
Hyokun Yun, S. V. N. Vishwanathan
We introduce a novel and efficient sampling algorithm for the Multiplicative Attribute Graph Model (MAGM - Kim and Leskovec (2010)}). Our algorithm is \emph{strictly} more efficien…
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs
Hyokun Yun, S. V. N. Vishwanathan
We describe the first sub-quadratic sampling algorithm for the Multiplicative Attribute Graph Model (MAGM) of Kim and Leskovec (2010). We exploit the close connection between MAGM…