29 citations · 139 across the 13 of their papers we have counts for
13 papers
Probabilistic Adaptation of Text-to-Video Models
Mengjiao Yang, Yilun Du, Bo Dai +3
Large text-to-video models trained on internet-scale data have demonstrated exceptional capabilities in generating high-fidelity videos from arbitrary textual descriptions. However…
Learning Universal Policies via Text-Guided Video Generation
Yilun Du, Mengjiao Yang, Bo Dai +5
A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impress…
Dichotomy of Control: Separating What You Can Control from What You Cannot
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel +1
Future- or return-conditioned supervised learning is an emerging paradigm for offline reinforcement learning (RL), where the future outcome (i.e., return) associated with an observ…
Making Linear MDPs Practical via Contrastive Representation Learning
Tianjun Zhang, Tongzheng Ren, Mengjiao Yang +3
It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations. This motivates much of the recent theoretical study…
Offline RL for Natural Language Generation with Implicit Language Q Learning
Charlie Snell, Ilya Kostrikov, Yi Su +2
Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by…
Chain of Thought Imitation with Procedure Cloning
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel +1
Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning proble…
Multi-Game Decision Transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang +8
A longstanding goal of the field of AI is a method for learning a highly capable, generalist agent from diverse experience. In the subfields of vision and language, this was largel…
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems
Charlie Snell, Mengjiao Yang, Justin Fu +2
Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control. While supervised learning with large language models is capable of pro…
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
Mengjiao Yang, Sergey Levine, Ofir Nachum
The aim in imitation learning is to learn effective policies by utilizing near-optimal expert demonstrations. However, high-quality demonstrations from human experts can be expensi…
Combiner: Full Attention Transformer with Sparse Computation Cost
Hongyu Ren, Hanjun Dai, Zihang Dai +4
Transformers provide a class of expressive architectures that are extremely effective for sequence modeling. However, the key limitation of transformers is their quadratic memory a…
Provable Representation Learning for Imitation with Contrastive Fourier Features
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. In this wo…
Benchmarks for Deep Off-Policy Evaluation
Justin Fu, Mohammad Norouzi, Ofir Nachum +10
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. The ability…
HpGAN: Sequence Search with Generative Adversarial Networks
Mingxing Zhang, Zhengchun Zhou, Lanping Li +3
Sequences play an important role in many engineering applications and systems. Searching sequences with desired properties has long been an interesting but also challenging researc…