Search Results for author: Tian Xie

Found 28 papers, 13 papers with code

Addressing Polarization and Unfairness in Performative Prediction

no code implementations24 Jun 2024 Kun Jin, Tian Xie, Yang Liu, Xueru Zhang

When machine learning (ML) models are used in applications that involve humans (e. g., online recommendation, school admission, hiring, lending), the model itself may trigger changes in the distribution of targeted data it aims to predict.


Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions

no code implementations12 May 2024 Tian Xie, Xueru Zhang

As machine learning (ML) models are increasingly used in social domains to make consequential decisions about humans, they often have the power to reshape data distributions.


Learning under Imitative Strategic Behavior with Unforeseeable Outcomes

no code implementations3 May 2024 Tian Xie, Zhiqun Zuo, Mohammad Mahdi Khalili, Xueru Zhang

Machine learning systems have been widely used to make decisions about individuals who may best respond and behave strategically to receive favorable outcomes, e. g., they may genuinely improve the true labels or manipulate observable features directly to game the system without changing labels.


Algorithmic Decision-Making under Agents with Persistent Improvement

no code implementations3 May 2024 Tian Xie, Xuwei Tan, Xueru Zhang

We also extend the model to settings where 1) agents may be dishonest and game the algorithm into making favorable but erroneous decisions; 2) honest efforts are forgettable and not sufficient to guarantee persistent improvements.

Decision Making

Non-linear Welfare-Aware Strategic Learning

no code implementations3 May 2024 Tian Xie, Xueru Zhang

Existing results on strategic learning have largely focused on the linear setting where agents with linear labeling functions best respond to a (noisy) linear decision policy.

Decision Making

Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint

no code implementations15 Mar 2024 Haoyue Tang, Tian Xie, Aosong Feng, Hanyu Wang, Chenyang Zhang, Yang Bai

Solving image inverse problems (e. g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image).

Image Restoration Super-Resolution

Optimizing Medication Decisions for Patients with Atrial Fibrillation through Path Development Network

no code implementations18 Jan 2024 Tian Xie

This study introduces a machine learning algorithm that predicts whether patients with AF should be recommended anticoagulant therapy using 12-lead ECG data.

Specificity Time Series

MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design

no code implementations16 Oct 2023 Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi Jaakkola, Jake Smith

Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry.

Denoising Diversity +1

XGen-7B Technical Report

1 code implementation7 Sep 2023 Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, Senthil Purushwalkam, Tong Niu, Wojciech Kryściński, Lidiya Murakhovs'ka, Prafulla Kumar Choubey, Alex Fabbri, Ye Liu, Rui Meng, Lifu Tu, Meghana Bhat, Chien-Sheng Wu, Silvio Savarese, Yingbo Zhou, Shafiq Joty, Caiming Xiong

Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.

2k 8k

SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition

no code implementations1 Jun 2023 Qibo Qiu, Haiming Gao, Wenxiao Wang, Zhiyi Su, Tian Xie, Wei Hua, Xiaofei He

To enhance message passing along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed, which is one of the main contributions in this paper.

Autonomous Vehicles

Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

1 code implementation13 Oct 2022 Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, Tommi Jaakkola

We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics.

A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations

2 code implementations2 Aug 2022 Tian Xie, Ha-Kyung Kwon, Daniel Schweigert, Sheng Gong, Arthur France-Lanord, Arash Khajeh, Emily Crabb, Michael Puzon, Chris Fajardo, Will Powelson, Yang Shao-Horn, Jeffrey C. Grossman

We create a public analysis library at https://github. com/TRI-AMDD/htp_md to extract multiple properties from the raw data, using both expert designed functions and machine learning models.

Label Efficient Regularization and Propagation for Graph Node Classification

no code implementations19 Apr 2022 Tian Xie, Rajgopal Kannan, C. -C. Jay Kuo

In this paper, we propose a label efficient regularization and propagation (LERP) framework for graph node classification, and present an alternate optimization procedure for its solution.

Attribute Benchmarking +4

GraphHop: An Enhanced Label Propagation Method for Node Classification

1 code implementation7 Jan 2021 Tian Xie, Bin Wang, C. -C. Jay Kuo

In Step 2, a new label vector is predicted for each node based on the label of the node itself and the aggregated label information obtained in Step 1.

Classification General Classification +3

L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis

1 code implementation19 Oct 2020 Zhentao Shi, Liangjun Su, Tian Xie

This paper tackles forecast combination with many forecasts or minimum variance portfolio selection with many assets.

Boosting Retailer Revenue by Generated Optimized Combined Multiple Digital Marketing Campaigns

no code implementations9 Sep 2020 Yafei Xu, Tian Xie, Yu Zhang

Secondly, based on the sub-modular optimization theory and the DMC pool by DMCNet, the generated combined multiple DMCs are ranked with respect to their revenue generation strength then the top three ranked campaigns are returned to the sellers' back-end management system, so that retailers can set combined multiple DMCs for their online shops just in one-shot.

Management Marketing

Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs

1 code implementation27 Jun 2019 Chaoyang He, Tian Xie, Yu Rong, Wenbing Huang, Junzhou Huang, Xiang Ren, Cyrus Shahabi

Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite graphs, which have distinct node features in two domains.

Graph Neural Network Recommendation Systems +1

Domain Representation for Knowledge Graph Embedding

no code implementations26 Mar 2019 Cunxiang Wang, Feiliang Ren, Zhichao Lin, Chenxv Zhao, Tian Xie, Yue Zhang

Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning.

Knowledge Graph Embedding Link Prediction +1

Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

1 code implementation18 Feb 2019 Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey C. Grossman

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges.

Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks

no code implementations9 Jul 2018 Tian Xie, Jeffrey C. Grossman

We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both.

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

3 code implementations Phys. Rev. Lett. 2017 Tian Xie, Jeffrey C. Grossman

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.

Band Gap Formation Energy Materials Science

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