Search Results for author: Weiran Yao

Found 18 papers, 5 papers with code

CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process

no code implementations25 Jan 2024 Guangyi Chen, Yifan Shen, Zhenhao Chen, Xiangchen Song, Yuewen Sun, Weiran Yao, Xiao Liu, Kun Zhang

Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning.

Causal Layering via Conditional Entropy

no code implementations19 Jan 2024 Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese

Under appropriate assumptions and conditioning, we can separate the sources or sinks from the remainder of the nodes by comparing their conditional entropy to the unconditional entropy of their noise.

Causal Discovery

Editing Arbitrary Propositions in LLMs without Subject Labels

no code implementations15 Jan 2024 Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese

On datasets of binary propositions derived from the CounterFact dataset, we show that our method -- without access to subject labels -- performs close to state-of-the-art L\&E methods which has access subject labels.

Language Modelling Large Language Model +1

DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation

no code implementations18 Dec 2023 Yu Wang, Zhiwei Liu, JianGuo Zhang, Weiran Yao, Shelby Heinecke, Philip S. Yu

With our principle, we managed to outperform GPT-Turbo-3. 5 on three datasets using 7b models e. g., Vicuna-7b and Openchat-7b on NDCG@10.

In-Context Learning Sequential Recommendation

Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization

no code implementations4 Aug 2023 Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh Murthy, Zeyuan Chen, JianGuo Zhang, Devansh Arpit, ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese

This demonstrates that using policy gradient optimization to improve language agents, for which we believe our work is one of the first, seems promising and can be applied to optimize other models in the agent architecture to enhance agent performances over time.

Language Modelling

Partial Identifiability for Domain Adaptation

no code implementations10 Jun 2023 Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang

In general, without further assumptions, the joint distribution of the features and the label is not identifiable in the target domain.

Unsupervised Domain Adaptation

On the Unlikelihood of D-Separation

no code implementations10 Mar 2023 Itai Feigenbaum, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Devansh Arpit

We then provide an analytic average case analysis of the PC Algorithm for causal discovery, as well as a variant of the SGS Algorithm we call UniformSGS.

Causal Discovery

Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving

no code implementations16 Dec 2022 Jonathan Francis, Bingqing Chen, Weiran Yao, Eric Nyberg, Jean Oh

The feasibility of collecting a large amount of expert demonstrations has inspired growing research interests in learning-to-drive settings, where models learn by imitating the driving behaviour from experts.

Autonomous Driving Density Estimation +1

Temporally Disentangled Representation Learning

no code implementations24 Oct 2022 Weiran Yao, Guangyi Chen, Kun Zhang

In this work, we establish the identifiability theories of nonparametric latent causal processes from their nonlinear mixtures under fixed temporal causal influences and analyze how distribution changes can further benefit the disentanglement.


PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

1 code implementation3 Oct 2022 Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang

To solve this problem, we propose to apply optimal transport to match the vision and text modalities.

Learning Latent Causal Dynamics

no code implementations10 Feb 2022 Weiran Yao, Guangyi Chen, Kun Zhang

Specifically, the framework factorizes unknown distribution shifts into transition distribution changes caused by fixed dynamics and time-varying latent causal relations, and by global changes in observation.

Time Series Time Series Analysis

Learning Temporally Causal Latent Processes from General Temporal Data

2 code implementations11 Oct 2021 Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang

In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures.

Causal Discovery Representation Learning +1

Learning Temporally Latent Causal Processes from General Temporal Data

2 code implementations ICLR 2022 Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang

Our goal is to find time-delayed latent causal variables and identify their relations from temporal measured variables.

Causal Discovery Disentanglement +1

From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data

no code implementations29 Sep 2020 Weiran Yao, Sean Qian

In this paper, we propose to mine Twitter messages as a probing method to understand the impacts of people's work and rest patterns in the evening/midnight of the previous day to the next-day morning traffic.

Management Traffic Prediction +2

Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction

no code implementations21 May 2020 Weiran Yao, Sean Qian

The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing.

Decision Making Management +2

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