#### Filter Results:

- Full text PDF available (47)

#### Publication Year

2001

2017

- This year (5)
- Last 5 years (31)
- Last 10 years (47)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- Mehryar Mohri, Afshin Rostamizadeh, Ameet S. Talwalkar
- Adaptive computation and machine learning
- 2012

• Let X=R with orthonormal basis (e1, e2) and consider the set of concepts defined by the area inside a right triangle ABC with two sides parallel to the axes, with −−→ AB/AB = e1 and −→ AC/AC = e2, and AB/AC = α for some positive real α ∈ R+. Show, using similar methods to those used in the lecture slides for the axis-aligned rectangles, that this class… (More)

- Xiangrui Meng, Joseph K. Bradley, +13 authors Ameet S. Talwalkar
- Journal of Machine Learning Research
- 2016

Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark’s open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical,… (More)

This work introduces Divide-Factor-Combine (DFC), a parallel divide-andconquer framework for noisy matrix factorization. DFC divides a large-scale matrix factorization task into smaller subproblems, solves each subproblem in parallel using an arbitrary base matrix factorization algorithm, and combines the subproblem solutions using techniques from… (More)

- Sanjiv Kumar, Mehryar Mohri, Ameet S. Talwalkar
- Journal of Machine Learning Research
- 2012

The Nyström method is an efficient technique to generate low-rank matrix approximations and is used in several large-scale learning applications. A key aspect of this method is the procedure according to which columns are sampled from the original matrix. In this work, we explore the efficacy of a variety of fixed and adaptive sampling schemes. We also… (More)

- Sanjiv Kumar, Mehryar Mohri, Ameet S. Talwalkar
- AISTATS
- 2009

The Nyström method is an efficient technique to generate low-rank matrix approximations and is used in several large-scale learning applications. A key aspect of this method is the distribution according to which columns are sampled from the original matrix. In this work, we present an analysis of different sampling techniques for the Nyström method. Our… (More)

- Evan R. Sparks, Ameet S. Talwalkar, +6 authors Tim Kraska
- 2013 IEEE 13th International Conference on Data…
- 2013

MLI is an Application Programming Interface designed to address the challenges of building Machine Learning algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can… (More)

Machine learning (ML) and statistical techniques are key to transforming big data into actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML algorithms is often overwhelming—many users do not understand the trade-offs and challenges of parameterizing and choosing between different learning techniques. Furthermore,… (More)

The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets—which are increasingly prevalent— the computation of bootstrap-based quantities can be prohibitively demanding computationally. While variants such as subsampling and the m out of n bootstrap can be used in principle to… (More)

- Sanjiv Kumar, Mehryar Mohri, Ameet S. Talwalkar
- ICML
- 2009

This paper addresses the problem of approximate singular value decomposition of large dense matrices that arises naturally in many machine learning applications. We discuss two recently introduced sampling-based spectral decomposition techniques: the Nyström and the Column-sampling methods. We present a theoretical comparison between the two methods… (More)

- Corinna Cortes, Mehryar Mohri, Ameet S. Talwalkar
- AISTATS
- 2010

Kernel approximation is commonly used to scale kernel-based algorithms to applications containing as many as several million instances. This paper analyzes the effect of such approximations in the kernel matrix on the hypothesis generated by several widely used learning algorithms. We give stability bounds based on the norm of the kernel approximation for… (More)