Search Results for author: Siddharth Karamcheti

Found 16 papers, 7 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

On the Opportunities and Risks of Foundation Models

no code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

1 code implementation ACL 2021 Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, Christopher D. Manning

Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition.

Active Learning Object Recognition +3

Targeted Data Acquisition for Evolving Negotiation Agents

no code implementations14 Jun 2021 Minae Kwon, Siddharth Karamcheti, Mariano-Florentino Cuellar, Dorsa Sadigh

This trend additionally holds when comparing agents using our targeted data acquisition framework to variants of agents trained with a mix of supervised learning and reinforcement learning, or to agents using tailored reward functions that explicitly optimize for utility and Pareto-optimality.

reinforcement-learning

Learning Visually Guided Latent Actions for Assistive Teleoperation

1 code implementation2 May 2021 Siddharth Karamcheti, Albert J. Zhai, Dylan P. Losey, Dorsa Sadigh

In this work, we develop assistive robots that condition their latent embeddings on visual inputs.

ELLA: Exploration through Learned Language Abstraction

1 code implementation NeurIPS 2021 Suvir Mirchandani, Siddharth Karamcheti, Dorsa Sadigh

Building agents capable of understanding language instructions is critical to effective and robust human-AI collaboration.

Learning Adaptive Language Interfaces through Decomposition

no code implementations EMNLP (intexsempar) 2020 Siddharth Karamcheti, Dorsa Sadigh, Percy Liang

Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings.

Semantic Parsing

Generating Interactive Worlds with Text

no code implementations20 Nov 2019 Angela Fan, Jack Urbanek, Pratik Ringshia, Emily Dinan, Emma Qian, Siddharth Karamcheti, Shrimai Prabhumoye, Douwe Kiela, Tim Rocktaschel, Arthur Szlam, Jason Weston

We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.

Common Sense Reasoning

Finding Generalizable Evidence by Learning to Convince Q\&A Models

no code implementations IJCNLP 2019 Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, Kyunghyun Cho

We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed.

Question Answering

Finding Generalizable Evidence by Learning to Convince Q&A Models

1 code implementation12 Sep 2019 Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, Kyunghyun Cho

We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed.

Question Answering

Learning to Speak and Act in a Fantasy Text Adventure Game

no code implementations IJCNLP 2019 Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston

We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.

Improving Grey-Box Fuzzing by Modeling Program Behavior

no code implementations21 Nov 2018 Siddharth Karamcheti, Gideon Mann, David Rosenberg

While these fuzzers have been able to find vulnerabilities in many widely used programs, they are not efficient; of the millions of inputs executed by AFL in a typical fuzzing run, only a handful discover unseen behavior or trigger a crash.

Adaptive Grey-Box Fuzz-Testing with Thompson Sampling

no code implementations24 Aug 2018 Siddharth Karamcheti, Gideon Mann, David Rosenberg

Fuzz testing, or "fuzzing," refers to a widely deployed class of techniques for testing programs by generating a set of inputs for the express purpose of finding bugs and identifying security flaws.

Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities

1 code implementation21 Apr 2017 Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex

In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular.

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