Search Results for author: Bohan Wu

Found 10 papers, 3 papers with code

Monte Carlo inference for semiparametric Bayesian regression

no code implementations8 Jun 2023 Daniel R. Kowal, Bohan Wu

Data transformations are essential for broad applicability of parametric regression models.

Gaussian Processes regression

Semiparametric discrete data regression with Monte Carlo inference and prediction

1 code implementation23 Oct 2021 Daniel R. Kowal, Bohan Wu

These data commonly exhibit complex distributional features such as zero-inflation, over-/under-dispersion, boundedness, and heaping, which render many parametric models inadequate.

regression Variable Selection

Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks

no code implementations21 Sep 2021 Bohan Wu, Suraj Nair, Li Fei-Fei, Chelsea Finn

In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks.

Model-based Reinforcement Learning reinforcement-learning +1

On the Opportunities and Risks of Foundation Models

3 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 Koh, 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

Semiparametric count data regression for self-reported mental health

no code implementations16 Jun 2021 Daniel R. Kowal, Bohan Wu

STAR is deployed to study the factors associated with self-reported mental health and demonstrates substantial improvements in goodness-of-fit compared to existing count data regression models.

Nutrition regression +1

Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction

no code implementations CVPR 2021 Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, Chelsea Finn

Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction.

Video Prediction

SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks

no code implementations10 Mar 2020 Bohan Wu, Feng Xu, Zhanpeng He, Abhi Gupta, Peter K. Allen

This paper aims to address this scalability challenge with a robust, sample-efficient, and general meta-IRL algorithm, SQUIRL, that performs a new but related long-horizon task robustly given only a single video demonstration.

reinforcement-learning Reinforcement Learning (RL)

MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning

no code implementations10 Sep 2019 Bohan Wu, Iretiayo Akinola, Jacob Varley, Peter Allen

When this methodology is used to realize grasps from coarse initial positions provided by a vision-only planner, the system is made dramatically more robust to calibration errors in the camera-robot transform.

reinforcement-learning Reinforcement Learning (RL)

Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes

no code implementations8 Mar 2019 Bohan Wu, Iretiayo Akinola, Peter K. Allen

Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observation and action spaces.

reinforcement-learning Reinforcement Learning (RL) +1

Model Primitive Hierarchical Lifelong Reinforcement Learning

1 code implementation4 Mar 2019 Bohan Wu, Jayesh K. Gupta, Mykel J. Kochenderfer

Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult.

Hierarchical Reinforcement Learning Meta-Learning +2

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