1 code implementation • 7 May 2024 • Megha Srivastava, Cedric Colas, Dorsa Sadigh, Jacob Andreas
Modern AI systems such as self-driving cars and game-playing agents achieve superhuman performance, but often lack human-like features such as generalization, interpretability and human inter-operability.
1 code implementation • 14 Mar 2024 • Megha Srivastava, Simran Arora, Dan Boneh
The increasing compute demands of AI systems has led to the emergence of services that train models on behalf of clients lacking necessary resources.
no code implementations • 29 Feb 2024 • Kristin Lauter, Cathy Yuanchen Li, Krystal Maughan, Rachel Newton, Megha Srivastava
Motivated by cryptographic applications, we investigate two machine learning approaches to modular multiplication: namely circular regression and a sequence-to-sequence transformer model.
1 code implementation • 12 Jun 2023 • Megha Srivastava, Noah Goodman, Dorsa Sadigh
AI assistance continues to help advance applications in education, from language learning to intelligent tutoring systems, yet current methods for providing students feedback are still quite limited.
1 code implementation • 19 Dec 2022 • Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E. Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael Bernstein, Percy Liang
To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics.
1 code implementation • 25 Nov 2022 • Megha Srivastava, Erdem Biyik, Suvir Mirchandani, Noah Goodman, Dorsa Sadigh
In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft.
1 code implementation • 5 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.
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.
no code implementations • 11 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.
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.
no code implementations • 31 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.
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.