#### Filter Results:

- Full text PDF available (15)

#### Publication Year

1965

2017

- This year (2)
- Last 5 years (15)
- Last 10 years (19)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

We study nonconvex finite-sum problems and analyze stochastic variance reduced gradient (Svrg) methods for them. Svrg and related methods have recently surged into prominence for convex optimization given their edge over stochastic gradient descent (Sgd); but their theoretical analysis almost exclusively assumes convexity. In contrast, we prove… (More)

We study optimization algorithms based on variance reduction for stochastic gradient descent (SGD). Remarkable recent progress has been made in this direction through development of algorithms like SAG, SVRG, SAGA. These algorithms have been shown to outperform SGD, both theoretically and empirically. However, asynchronous versions of these algorithms—a… (More)

A single, stationary topic model such as latent Dirichlet allocation is inappropriate for modeling corpora that span long time periods, as the popularity of topics is likely to change over time. A number of models that incorporate time have been proposed, but in general they either exhibit limited forms of temporal variation, or require computationally… (More)

Mining of transliterations from comparable or parallel text can enhance natural language processing applications such as machine translation and cross language information retrieval. This paper presents an enhanced transliteration mining technique that uses a generative graph reinforcement model to infer mappings between source and target character… (More)

- Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon
- NIPS
- 2015

Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as… (More)

- Amgad Madkour, Tarek Hefni, Ahmed Hefny, Khaled S. Refaat
- 2008

Collaborative software is gaining pace as a vital means of information sharing between users. This paper discusses one of the key challenges that affect such systems which is identifying spammers. We discuss potential features that describe the system’s users and illustrate how we can use those features in order to determine potential spamming users through… (More)

We develop randomized block coordinate descent (CD) methods for linearly constrained convex optimization. Unlike other large-scale CD methods, we do not assume the constraints to be separable, but allow them be coupled linearly. To our knowledge, ours is the first CD method that allows linear coupling constraints, without making the global iteration… (More)

The Kaczmarz and Gauss-Seidel methods aim to solve a linear m × n system Xβ = y by iteratively refining the solution estimate; the former uses random rows of X to update β given the corresponding equations and the latter uses random columns of X to update corresponding coordinates in β. Interest in these methods was recently revitalized by a proof of… (More)

Topic Models such as Latent Dirichlet Allocation (LDA) have been successfully applied as a data analysis and dimensionality reduction tool. With the emergence of social networks, many datasets are available in the form of a network with typed nodes (documents, authors, URLs, publication dates, . . . ) and edges (authorship, citation, friendship, . . . ). We… (More)

- Ahmed Hefny, Kareem Darwish, Ali Alkahky
- ECIR
- 2011