Search Results for author: Saurabh Garg

Found 11 papers, 4 papers with code

Deconstructing Distributions: A Pointwise Framework of Learning

1 code implementation20 Feb 2022 Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran

In this work, we propose a new approach: we measure the performance of a collection of models when evaluated on a $\textit{single input point}$.

Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

no code implementations ICLR 2022 Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi

Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops.

Mixture Proportion Estimation and PU Learning: A Modern Approach

1 code implementation NeurIPS 2021 Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton

Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE) -- determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning -- given such an estimate, learning the desired positive-versus-negative classifier.

Mixture Proportion Estimation and PU Learning:A Modern Approach

1 code implementation NeurIPS 2021 Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton

Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE)---determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning---given such an estimate, learning the desired positive-versus-negative classifier.

RATT: Leveraging Unlabeled Data to Guarantee Generalization

1 code implementation1 May 2021 Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton

To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on the true risk; or (ii) validate empirically on holdout data.

Generalization Bounds

On Proximal Policy Optimization's Heavy-tailed Gradients

no code implementations20 Feb 2021 Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar

In this paper, we present a detailed empirical study to characterize the heavy-tailed nature of the gradients of the PPO surrogate reward function.

Continuous Control

A Unified View of Label Shift Estimation

no code implementations NeurIPS 2020 Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton

Our contributions include (i) consistency conditions for MLLS, which include calibration of the classifier and a confusion matrix invertibility condition that BBSE also requires; (ii) a unified framework, casting BBSE as roughly equivalent to MLLS for a particular choice of calibration method; and (iii) a decomposition of MLLS's finite-sample error into terms reflecting miscalibration and estimation error.

Dual Language Models for Code Switched Speech Recognition

no code implementations3 Nov 2017 Saurabh Garg, Tanmay Parekh, Preethi Jyothi

Since code-switching is a blend of two or more different languages, a standard bilingual language model can be improved upon by using structures of the monolingual language models.

Automatic Speech Recognition

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