Xiner Li

AI researcher. CS Ph.D @TAMU. Previously @Tsinghua_Uni.

[Shuiwang Ji @ShuiwangJi]

New paper alert: Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
https://t.co/lcpambsBDx

[Kelvin (Keqiang) Yan @KeqiangY]

Excited and proud mentor moment 💫! A new materials foundation model with two of my undergraduate mentees Montgomery Bohde and Andrii Kryvenko as core authors.

For Materials Foundation Models, Invariance V.S. Equivariance? Invariance + Equivariance!

[Xiner Li @hyanan16]

For more details, our paper can be accessed at:
https://t.co/Px2of7iJjg
And code is available at:
https://t.co/Ip1EoEYK7e

[Xiner Li @hyanan16]

Excited to share our paper “Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding”. My amazing coauthors: @zhaoyl18thu, @ChenyuW64562111, @gabo_scalia, @gokcen, @suragnair, @tbyanc, @ShuiwangJi, Aviv Regev, @svlevine, Masatoshi Uehara

[Xiner Li @hyanan16]

We outperformed the baseline methods Best-of-N, DPS, and SMC by generating valid samples with higher-reward

[Hannes Stark @HannesStaerk]

In the reading group tomorrow we have Xiner Li @hyanan16 presenting her work on steering diffusion models with non-differentiable reward functions 👌
https://t.co/GMFUpukIpg

Join us on zoom at 9am PT / 12pm ET / 6pm CE(S)T: https://t.co/R8d1EHxdMZ

[Mengdi Wang @MengdiWang10]

I am co-hosting a workshop on "AI for New Drug Modalities" at #NeurIPS 24 (along with Masatoshi Uehara, Aviv Regev, Su-In Lee, @dasongle , Nicola Richmond, @mariabrbic , @mo_lotfollahi, @EhsanHRA and Xiner Li)

We are currently accepting paper submissions on a broad spectrum of AI-related topics in drug discovery/biology. We welcome your submissions!
https://t.co/9VOEtprdJm

Submission Deadline: 01 Oct, 2024
Workshop Dates: 14 or 15 December, 2024
Location: Vancouver Convention Centre
Format: Up to 9-page manuscripts (non-archival), NeurIPS/ICLR format

#AI #AIDD @NeurIPSConf #LLM

[Xiner Li @hyanan16]

I am co-hosting a workshop on "AI for New Drug Modalities" at NeurIPS 24. We are currently accepting paper submissions on a broad spectrum of AI-related topics in drug discovery/biology. We welcome your submissions!
https://t.co/M8PA3fcIZm

Submission Deadline: 01 Oct, 2024

[Ziqi Wang @wzq016]

(1/8) Position bias of LLMs is problematic in many applications, such as model-based evaluation and retrieval-augmented QA. Can we eliminate it so that LLMs become more reliable and achieve better performance?

Check out our paper: https://t.co/yPiJRJHmZx

[Xiner Li @hyanan16]

Trilled to have our papers
1. Graph Structure Extrapolation for Out-of-Distribution Generalization (https://t.co/YLi1qDQYv4)
2. Position Paper: TrustLLM: Trustworthiness in Large Language Models (https://t.co/tchKSsF7N7) (https://t.co/uvV0pwk9d0)
accepted at #ICML2024 !

[Shurui Gui @ShuruiGui]

A successful poster session of our work: Active Test-Time Adaptation at #ICLR2024! Thanks @JacobHelwig for presenting on behalf of us!🥰

[Shurui Gui @ShuruiGui]

Thrilled to announce that our paper Active Test-Time Adaptation has been accepted by ICLR 2024! 🎉 Many thanks to my great advisor @ShuiwangJi and the remarkable co-author @hyanan16! Interested in this new TTA direction? Follow our project: 😄
https://t.co/bgL8Zvdu4l
#ICLR2024

[Shuiwang Ji @ShuiwangJi]

Excited to share our latest survey paper: "Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems"! 🚀 ArXiv: https://t.co/EgMPbflFBT.

[Shuiwang Ji @ShuiwangJi]

🔬 An in-depth yet intuitive discussion on symmetry, as well as explainability, out-of-distribution generalization, large language models, and uncertainty.

📖 Access categorized lists of resources to enhance learning and education.

🌐 Website: https://t.co/3p0EYy37ch.

[Shuiwang Ji @ShuiwangJi]

interested in graph OOD problems?
GOOD: A Graph Out-of-Distribution Benchmark
https://t.co/KujohqFuEo