Search Results for author: Mengjiao Yang

Found 22 papers, 14 papers with code

Scalable Diffusion for Materials Generation

no code implementations18 Oct 2023 Mengjiao Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk

Lastly, we show that conditional generation with UniMat can scale to previously established crystal datasets with up to millions of crystals structures, outperforming random structure search (the current leading method for structure discovery) in discovering new stable materials.

Formation Energy

Video Language Planning

no code implementations16 Oct 2023 Yilun Du, Mengjiao Yang, Pete Florence, Fei Xia, Ayzaan Wahid, Brian Ichter, Pierre Sermanet, Tianhe Yu, Pieter Abbeel, Joshua B. Tenenbaum, Leslie Kaelbling, Andy Zeng, Jonathan Tompson

We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pretrained on Internet-scale data.

Learning Interactive Real-World Simulators

no code implementations9 Oct 2023 Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Leslie Kaelbling, Dale Schuurmans, Pieter Abbeel

Applications of a real-world simulator range from controllable content creation in games and movies, to training embodied agents purely in simulation that can be directly deployed in the real world.

Video Captioning

Probabilistic Adaptation of Text-to-Video Models

no code implementations2 Jun 2023 Mengjiao Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum, Pieter Abbeel

Large text-to-video models trained on internet-scale data have demonstrated exceptional capabilities in generating high-fidelity videos from arbitrary textual descriptions.

Language Modelling Large Language Model

Dichotomy of Control: Separating What You Can Control from What You Cannot

1 code implementation24 Oct 2022 Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum

While return-conditioning is at the heart of popular algorithms such as decision transformer (DT), these methods tend to perform poorly in highly stochastic environments, where an occasional high return can arise from randomness in the environment rather than the actions themselves.

Reinforcement Learning (RL)

Making Linear MDPs Practical via Contrastive Representation Learning

no code implementations14 Jul 2022 Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai

It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations.

Representation Learning

Multi-Game Decision Transformers

1 code implementation30 May 2022 Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, Igor Mordatch

Specifically, we show that a single transformer-based model - with a single set of weights - trained purely offline can play a suite of up to 46 Atari games simultaneously at close-to-human performance.

Atari Games Offline RL

Chain of Thought Imitation with Procedure Cloning

1 code implementation22 May 2022 Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum

Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior.

Imitation Learning Robot Manipulation

CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning

2 code implementations NAACL 2022 Siddharth Verma, Justin Fu, Mengjiao Yang, Sergey Levine

Conventionally, generation of natural language for dialogue agents may be viewed as a statistical learning problem: determine the patterns in human-provided data and generate appropriate responses with similar statistical properties.

Chatbot Offline RL +2

TRAIL: Near-Optimal Imitation Learning with Suboptimal Data

1 code implementation ICLR 2022 Mengjiao Yang, Sergey Levine, Ofir Nachum

In this work, we answer this question affirmatively and present training objectives that use offline datasets to learn a factored transition model whose structure enables the extraction of a latent action space.

Imitation Learning

Combiner: Full Attention Transformer with Sparse Computation Cost

2 code implementations NeurIPS 2021 Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai

However, the key limitation of transformers is their quadratic memory and time complexity $\mathcal{O}(L^2)$ with respect to the sequence length in attention layers, which restricts application in extremely long sequences.

Image Generation Language Modelling

Provable Representation Learning for Imitation with Contrastive Fourier Features

1 code implementation NeurIPS 2021 Ofir Nachum, Mengjiao Yang

In imitation learning, it is common to learn a behavior policy to match an unknown target policy via max-likelihood training on a collected set of target demonstrations.

Atari Games Contrastive Learning +2

Benchmarks for Deep Off-Policy Evaluation

3 code implementations ICLR 2021 Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine

Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making.

Benchmarking Continuous Control +3

Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach

1 code implementation EMNLP 2021 Haoming Jiang, Bo Dai, Mengjiao Yang, Tuo Zhao, Wei Wei

An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments.

Model-based Reinforcement Learning Off-policy evaluation +2

Representation Matters: Offline Pretraining for Sequential Decision Making

no code implementations ICLR Workshop SSL-RL 2021 Mengjiao Yang, Ofir Nachum

The recent success of supervised learning methods on ever larger offline datasets has spurred interest in the reinforcement learning (RL) field to investigate whether the same paradigms can be translated to RL algorithms.

Imitation Learning Offline RL +1

Offline Policy Selection under Uncertainty

1 code implementation12 Dec 2020 Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans

More importantly, we show how the belief distribution estimated by BayesDICE may be used to rank policies with respect to any arbitrary downstream policy selection metric, and we empirically demonstrate that this selection procedure significantly outperforms existing approaches, such as ranking policies according to mean or high-confidence lower bound value estimates.

Off-Policy Evaluation via the Regularized Lagrangian

no code implementations NeurIPS 2020 Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans

The recently proposed distribution correction estimation (DICE) family of estimators has advanced the state of the art in off-policy evaluation from behavior-agnostic data.

Off-policy evaluation

Energy-Based Processes for Exchangeable Data

1 code implementation ICML 2020 Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans

Recently there has been growing interest in modeling sets with exchangeability such as point clouds.

Denoising Point Cloud Generation

Benchmarking Attribution Methods with Relative Feature Importance

2 code implementations23 Jul 2019 Mengjiao Yang, Been Kim

Despite active development, quantitative evaluation of feature attribution methods remains difficult due to the lack of ground truth: we do not know which input features are in fact important to a model.

Benchmarking Feature Importance

GraphIt - A High-Performance DSL for Graph Analytics

5 code implementations2 May 2018 Yunming Zhang, Mengjiao Yang, Riyadh Baghdadi, Shoaib Kamil, Julian Shun, Saman Amarasinghe

This paper introduces GraphIt, a new DSL for graph computations that generates fast implementations for algorithms with different performance characteristics running on graphs with different sizes and structures.

Programming Languages

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