{"id":28425328,"url":"https://github.com/datamllab/labnews","last_synced_at":"2026-01-29T22:03:24.905Z","repository":{"id":222160294,"uuid":"756146334","full_name":"datamllab/labnews","owner":"datamllab","description":null,"archived":false,"fork":false,"pushed_at":"2024-02-12T16:54:32.000Z","size":6,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-06-26T04:35:41.824Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/datamllab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-02-12T03:44:38.000Z","updated_at":"2024-06-10T17:42:38.000Z","dependencies_parsed_at":"2024-02-12T18:11:56.414Z","dependency_job_id":null,"html_url":"https://github.com/datamllab/labnews","commit_stats":null,"previous_names":["datamllab/labnews"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/datamllab/labnews","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamllab%2Flabnews","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamllab%2Flabnews/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamllab%2Flabnews/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamllab%2Flabnews/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/datamllab","download_url":"https://codeload.github.com/datamllab/labnews/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamllab%2Flabnews/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28886882,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-29T21:06:44.224Z","status":"ssl_error","status_checked_at":"2026-01-29T21:06:42.160Z","response_time":59,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-06-05T10:37:00.703Z","updated_at":"2026-01-29T22:03:24.887Z","avatar_url":"https://github.com/datamllab.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Data Analytics Lab at Rice University\n\n\u003e 🚧 work in progress...\n\n- [x] Add recent discussions.\n- [x] Add selected recent work titles.\n- [ ] Add author information.\n- [ ] Add tl;dr.\n- [ ] Add repo links.\n\n---\n\n## Recent Discussions\n\n\n\n\u003e We believe in having open conversations for better scientific discourse. Here are some recent posts related to our work on social media (by us or a third party), where we find many of such discussions quite insightful. OpenReview offers an excellent place to digest papers from a non-author perspective; social media allows us to do exactly that for preprints.\n\u003e We'd try our best to engage in such posts. Of course, you are always welcome to email us or open up an issue anytime.\n\n\n[[r/MachineLearning](https://www.reddit.com/r/MachineLearning/comments/1ap3b65/), [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/1ap3bkt/), [Twitter/X](), [LinkedIn](https://www.linkedin.com/posts/shaochen-henry-zhong-96a941249_kv-cache-is-huge-and-bottlenecks-llm-inference-activity-7162844534454824960-8IJ3)] KV Cache is huge and bottlenecks LLM inference. We quantize them to 2bit in a finetuning-free + plug-and-play fashion. `authors`\n\n[[r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/18x8g6c/llm_maybe_longlm_selfextend_llm_context_window/), [Twitter/X #1](https://x.com/cwolferesearch/status/1748393116338409890?s=20) [#2](https://x.com/arankomatsuzaki/status/1742367971857883383?s=20) [#3](https://x.com/rohanpaul_ai/status/1751884202877042956?s=20) [#4](https://x.com/_akhaliq/status/1742371015362052461?s=20), [kexue.fm](https://kexue.fm/archives/9948)] LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning. `third-party`\n\n\n\n\n\n---\n## Selected Recent Work\n\n\u003e Some recent work from us that might worth your attention.\n\n### Prepints\n\nKIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache [[paper]](https://arxiv.org/abs/2402.02750)`llm` `efficiency`\n\nCompress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt [[paper]](https://arxiv.org/abs/2305.11186) `llm` `efficiency` \n\nLLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning [[paper]](https://arxiv.org/abs/2401.01325)`llm` `long context` \n\n\n\n\nGrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length [[paper]](https://arxiv.org/abs/2310.00576) `llm` `long context` \n\nLETA: Learning Transferable Attribution for Generic Vision Explainer [[paper]](https://arxiv.org/abs/2312.15359)`vision` `xai` \n\nLarge Language Models As Faithful Explainers [[paper]](https://arxiv.org/abs/2402.04678) `llm` `xai` \n\n\nOn the Equivalence of Graph Convolution and Mixup [[paper]](https://arxiv.org/abs/2310.00183) `graph` \n\nChasing Fairness in Graphs: A GNN Architecture Perspective [[paper]](https://arxiv.org/abs/2312.12369) `graph` `trustworthy` \n\nEditable Graph Neural Network for Node Classifications [[paper]](https://arxiv.org/abs/2305.15529) `graph` `trustworthy`\n\nHarnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond [[paper]](https://arxiv.org/abs/2304.13712) `llm` `survey`\n\nData-centric Artificial Intelligence: A Survey [[paper]](https://arxiv.org/abs/2303.10158) `dcai` `survey`\n\n---   \n\n### CACM 2024\n\nThe Science of Detecting LLM-Generated Texts `llm` `security`\n\n---\n\n### ICLR 2024\n\nFFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods `graph` `benchmark` `trustworthy` \n\n---   \n\n### NeurIPS 2023\n\nWinner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model `llm` `efficiency` \n\nOne Less Reason for Filter Pruning: Gaining Free Adversarial Robustness with Structured Grouped Kernel Pruning `efficiency` `trustworthy` `security` \n\nSetting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots `llm` `security` \n\nChasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach `trustworthy` \n\nFair Graph Distillation `graph` `trustworthy` \n\n---   \n\n### ICDM 2023 \nDouble wins: Boosting accuracy and efficiency of graph neural networks by reliable knowledge distillation `graph` `efficiency` \n\n\n---   \n\n### AMIA 2023\n\nLLM for Patient-Trial Matching: Privacy-Aware Data Augmentation Towards Better Performance and Generalizability `healthcare` `llm` \n\nMulti-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant `healthcare` \n\n---   \n\n### CIKM 2023\n\nDiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research `healthcare` \n\nExposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks `security` \n\n---   \n\n### SDM 2023\n\nData-centric AI: Perspectives and Challenges `survey` `dcai` \n\nContext-aware Domain Adaptation for Time Series Anomaly Detection `time series` \n\nAdaptive Label Smoothing To Regularize Large-Scale Graph Training `graph` \n\n---   \n\n### ICML 2023\n\nDIVISION: Memory Efficient Training via Dual Activation Precision `vision` `efficiency` \n\nRSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations `graph` `efficiency` \n\nPME: pruning-based multi-size embedding for recommender systems `recsys` `efficiency` \n\n---   \n\n### MLSys 2023\n\nPre-trained Neural Cost Models for Efficient Embedding Table Sharding in Deep Learning Recommendation Models `recsys` `efficiency` \n\n---   \n\n### ICLR 2023\n\nCoRTX: Contrastive Framework for Real-time Explanation `xai` \n\nMLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization `graph` `efficiency` \n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatamllab%2Flabnews","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatamllab%2Flabnews","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatamllab%2Flabnews/lists"}