Search Results for author: Afshin Rostamizadeh

Found 27 papers, 7 papers with code

Leveraging Importance Weights in Subset Selection

no code implementations28 Jan 2023 Gui Citovsky, Giulia Desalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang

In such a setting, an algorithm can sample examples one at a time but, in order to limit overhead costs, is only able to update its state (i. e. further train model weights) once a large enough batch of examples is selected.

Active Learning

Is margin all you need? An extensive empirical study of active learning on tabular data

no code implementations7 Oct 2022 Dara Bahri, Heinrich Jiang, Tal Schuster, Afshin Rostamizadeh

Given a labeled training set and a collection of unlabeled data, the goal of active learning (AL) is to identify the best unlabeled points to label.

Active Learning Benchmarking +1

Learning with Labeling Induced Abstentions

no code implementations NeurIPS 2021 Kareem Amin, Giulia Desalvo, Afshin Rostamizadeh

Consider a setting where we wish to automate an expensive task with a machine learning algorithm using a limited labeling resource.

Active Learning BIG-bench Machine Learning +1

Batch Active Learning at Scale

1 code implementation NeurIPS 2021 Gui Citovsky, Giulia Desalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar

The ability to train complex and highly effective models often requires an abundance of training data, which can easily become a bottleneck in cost, time, and computational resources.

Active Learning

Churn Reduction via Distillation

no code implementations ICLR 2022 Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh

In real-world systems, models are frequently updated as more data becomes available, and in addition to achieving high accuracy, the goal is to also maintain a low difference in predictions compared to the base model (i. e. predictive "churn").

Active Covering

no code implementations4 Jun 2021 Heinrich Jiang, Afshin Rostamizadeh

We show under standard non-parametric assumptions that a classical support estimator can be repurposed as an offline algorithm attaining an excess query cost of $\widetilde{\Theta}(n^{D/(D+1)})$ compared to the optimal learner, where $n$ is the number of datapoints and $D$ is the dimension.

Active Learning

Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms

1 code implementation ICLR 2021 Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh

Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective.

Federated Learning

An Analysis of SVD for Deep Rotation Estimation

2 code implementations NeurIPS 2020 Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia

Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto $O(n)$ or $SO(n)$.

3D Pose Estimation 3D Rotation Estimation

Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels

1 code implementation2 Dec 2019 Shuang Song, David Berthelot, Afshin Rostamizadeh

This analysis can be used to measure the relative value of labeled/unlabeled data at different points of the learning curve, where we find that although the incremental value of labeled data can be as much as 20x that of unlabeled, it quickly diminishes to less than 3x once more than 2, 000 labeled example are observed.

Active Learning

The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation

no code implementations28 Jun 2019 Jean-François Kagy, Tolga Kayadelen, Ji Ma, Afshin Rostamizadeh, Jana Strnadova

We tested in a live setting the use of active learning for selecting text sentences for human annotations used in training a Thai segmentation machine learning model.

Active Learning

Categorical Feature Compression via Submodular Optimization

no code implementations30 Apr 2019 Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni, Afshin Rostamizadeh

To achieve this, we introduce a novel re-parametrization of the mutual information objective, which we prove is submodular, and design a data structure to query the submodular function in amortized $O(\log n )$ time (where $n$ is the input vocabulary size).

Feature Compression

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling

1 code implementation26 Jun 2018 Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

Our experiments show that there is indeed additional structure beyond sparsity in the real datasets; our method is able to discover it and exploit it to create excellent reconstructions with fewer measurements (by a factor of 1. 1-3x) compared to the previous state-of-the-art methods.

Extreme Multi-Label Classification Multi-Label Learning +1

Massively Parallel Hyperparameter Tuning

no code implementations ICLR 2018 Lisha Li, Kevin Jamieson, Afshin Rostamizadeh, Katya Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar

Modern machine learning models are characterized by large hyperparameter search spaces and prohibitively expensive training costs.

Foundations of Coupled Nonlinear Dimensionality Reduction

no code implementations29 Sep 2015 Mehryar Mohri, Afshin Rostamizadeh, Dmitry Storcheus

The generalization error bound is based on a careful analysis of the empirical Rademacher complexity of the relevant hypothesis set.

Generalization Bounds Supervised dimensionality reduction

Repeated Contextual Auctions with Strategic Buyers

no code implementations NeurIPS 2014 Kareem Amin, Afshin Rostamizadeh, Umar Syed

Motivated by real-time advertising exchanges, we analyze the problem of pricing inventory in a repeated posted-price auction.

Matrix Coherence and the Nystrom Method

no code implementations9 Aug 2014 Ameet Talwalkar, Afshin Rostamizadeh

Crucial to the performance of this technique is the assumption that a matrix can be well approximated by working exclusively with a subset of its columns.

Matrix Completion

Learning Prices for Repeated Auctions with Strategic Buyers

no code implementations NeurIPS 2013 Kareem Amin, Afshin Rostamizadeh, Umar Syed

Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism.

Perceptron Mistake Bounds

no code implementations1 May 2013 Mehryar Mohri, Afshin Rostamizadeh

We present a brief survey of existing mistake bounds and introduce novel bounds for the Perceptron or the kernel Perceptron algorithm.

Algorithms for Learning Kernels Based on Centered Alignment

no code implementations2 Mar 2012 Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh

Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment.

General Classification Generalization Bounds +1

Learning Non-Linear Combinations of Kernels

no code implementations NeurIPS 2009 Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh

This paper studies the general problem of learning kernels based on a polynomial combination of base kernels.


Domain Adaptation: Learning Bounds and Algorithms

no code implementations19 Feb 2009 Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh

This motivates our analysis of the problem of minimizing the empirical discrepancy for various loss functions for which we also give novel algorithms.

Domain Adaptation Generalization Bounds

Domain Adaptation with Multiple Sources

no code implementations NeurIPS 2008 Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh

The problem consists of combining these hypotheses to derive a hypothesis with small error with respect to the target domain.

Domain Adaptation

Rademacher Complexity Bounds for Non-I.I.D. Processes

no code implementations NeurIPS 2008 Mehryar Mohri, Afshin Rostamizadeh

In particular, they are data-dependent and measure the complexity of a class of hypotheses based on the training sample.

Generalization Bounds

Stability Bounds for Non-i.i.d. Processes

no code implementations NeurIPS 2007 Mehryar Mohri, Afshin Rostamizadeh

We also illustrate their application in the case of several general classes of learning algorithms, including Support Vector Regression and Kernel Ridge Regression.

Generalization Bounds Learning Theory +3

Cannot find the paper you are looking for? You can Submit a new open access paper.