1 code implementation • 5 Dec 2023 • Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan

For product search, PEFA improves the Recall@100 of the fine-tuned ERMs by an average of 5. 3% and 14. 5%, for PEFA-XS and PEFA-XL, respectively.

no code implementations • 19 Sep 2022 • Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan

Such features naturally arise in merchandised recommendation systems; for instance, "user clicked this item" as a feature is predictive of "user purchased this item" in the offline data, but is clearly not available during online serving.

no code implementations • 29 Apr 2020 • Parameswaran Raman, S. V. N. Vishwanathan

Traditional algorithms for FM which work on a single-machine are not equipped to handle this scale and therefore, using a distributed algorithm to parallelize the computation across a cluster is inevitable.

no code implementations • 29 Aug 2019 • Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan, W. Bruce Croft

In this paper, we formulate the problem of personalized product search and conduct large-scale experiments with search logs sampled from a commercial e-commerce search engine.

no code implementations • 22 Oct 2018 • Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S. V. N. Vishwanathan

We further apply our algorithm to optimize a message that promotes adoption of an Amazon service.

no code implementations • 22 Apr 2017 • Michał Dereziński, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer

We propose Batch-Expansion Training (BET), a framework for running a batch optimizer on a gradually expanding dataset.

no code implementations • 17 Sep 2016 • Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan

We present applications of our framework to online learning of Huffman trees and permutations.

no code implementations • 31 May 2016 • Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon

Moreover, it requires the parameters to fit in the memory of a single processor; this is problematic when the number of parameters is in billions.

1 code implementation • 16 Apr 2016 • Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan

Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging.

no code implementations • NeurIPS 2015 • Pinar Yanardag, S. V. N. Vishwanathan

In this paper, we propose a general smoothing framework for graph kernels by taking \textit{structural similarity} into account, and apply it to derive smoothed variants of popular graph kernels.

1 code implementation • 21 Nov 2015 • Shihao Ji, S. V. N. Vishwanathan, Nadathur Satish, Michael J. Anderson, Pradeep Dubey

One way to understand BlackOut is to view it as an extension of the DropOut strategy to the output layer, wherein we use a discriminative training loss and a weighted sampling scheme.

no code implementations • KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2015 • Pinar Yanardag, S. V. N. Vishwanathan

In this paper, we present Deep Graph Kernels (DGK), a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning.

Ranked #2 on Malware Clustering on Android Malware Dataset

2 code implementations • EMNLP 2016 • Shihao Ji, Hyokun Yun, Pinar Yanardag, Shin Matsushima, S. V. N. Vishwanathan

Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses.

no code implementations • 7 Apr 2015 • Vasil S. Denchev, Nan Ding, Shin Matsushima, S. V. N. Vishwanathan, Hartmut Neven

If actual quantum optimization were to be used with this algorithm in the future, we would expect equivalent or superior results at much smaller time and energy costs during training.

1 code implementation • 16 Dec 2014 • Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon

Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one needs to deal with a large number of topics (typically in the order of thousands).

no code implementations • 17 Jun 2014 • Shin Matsushima, Hyokun Yun, Xinhua Zhang, S. V. N. Vishwanathan

Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task.

no code implementations • NeurIPS 2014 • 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.

no code implementations • 3 Mar 2014 • Pinar Yanardag, S. V. N. Vishwanathan

This vector representation can be used in a variety of applications, such as, for computing similarity between graphs.

no code implementations • 11 Feb 2014 • 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 classification.

no code implementations • 1 Jan 2014 • Dinesh Govindaraj, Tao Wang, S. V. N. Vishwanathan

Our model seamlessly incorporates the effect of externalities (quality of other search results displayed in response to a user query), user fatigue, as well as pre and post-click relevance of a sponsored search result.

1 code implementation • 1 Dec 2013 • Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S. V. N. Vishwanathan, Inderjit Dhillon

One of the key features of NOMAD is that the ownership of a variable is asynchronously transferred between processors in a decentralized fashion.

Distributed, Parallel, and Cluster Computing

no code implementations • NeurIPS 2011 • Nan Ding, Yuan Qi, S. V. N. Vishwanathan

Approximate inference is an important technique for dealing with large, intractable graphical models based on the exponential family of distributions.

no code implementations • NeurIPS 2010 • Zhaonan Sun, Nawanol Ampornpunt, Manik Varma, S. V. N. Vishwanathan

Our objective is to train $p$-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Sequential Minimal Optimization (SMO) algorithm.

no code implementations • NeurIPS 2010 • Novi Quadrianto, James Petterson, Tibério S. Caetano, Alex J. Smola, S. V. N. Vishwanathan

We propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available.

no code implementations • NeurIPS 2010 • Nan Ding, S. V. N. Vishwanathan

We extend logistic regression by using t-exponential families which were introduced recently in statistical physics.

no code implementations • NeurIPS 2010 • Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

By exploiting the structure of the objective function we can devise an algorithm that converges in $O(1/\sqrt{\epsilon})$ iterations.

no code implementations • Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics 2009 • Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten Borgwardt.

State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges.

Ranked #84 on Graph Classification on PROTEINS

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