Search Results for author: Megha Srivastava

Found 6 papers, 4 papers with code

LILA: Language-Informed Latent Actions

1 code implementation5 Nov 2021 Siddharth Karamcheti, Megha Srivastava, Percy Liang, Dorsa Sadigh

We introduce Language-Informed Latent Actions (LILA), a framework for learning natural language interfaces in the context of human-robot collaboration.

Imitation Learning

Question Generation for Adaptive Education

1 code implementation ACL 2021 Megha Srivastava, Noah Goodman

Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students.

Knowledge Tracing Question Generation +1

An Empirical Analysis of Backward Compatibility in Machine Learning Systems

no code implementations11 Aug 2020 Megha Srivastava, Besmira Nushi, Ece Kamar, Shital Shah, Eric Horvitz

In many applications of machine learning (ML), updates are performed with the goal of enhancing model performance.

Robustness to Spurious Correlations via Human Annotations

1 code implementation ICML 2020 Megha Srivastava, Tatsunori Hashimoto, Percy Liang

The reliability of machine learning systems critically assumes that the associations between features and labels remain similar between training and test distributions.

Common Sense Reasoning

The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariance

no code implementations31 Oct 2018 Megha Srivastava, Kalanit Grill-Spector

Because training artificial neural networks from scratch is similar to showing novel objects to humans, we seek to understand the factors influencing the tolerance of CNNs to spatial transformations.

Object Recognition

Fairness Without Demographics in Repeated Loss Minimization

1 code implementation ICML 2018 Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang

Machine learning models (e. g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e. g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss.

Fairness

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