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cheng yu hsieh ph d student uw cse cheng yu hsieh ph d student uw cse email google scholar twitter i am a final year ph d student in computer science engineering at the university of washington working with ranjay krishna and alex ratner on tackling challenges in today s large scale machine learning environment i am grateful to be supported by the google phd fellowship previously i recevied my b s and m s from national taiwan university where i was fortunate to work with hsuan tien lin prior to joining uw i spent wonderful time visiting carnegie mellon university and univeristy of california los angeles where i worked with pradeep ravikumar and cho jui hsieh research interests my research goal is to democratize ai development by making both data and model scaling more efficient and effective in today s large scale environment based on four complementary areas of work tackling different aspects of data and model scaling challenges on data side i study 1 how to efficiently curate large datasets and 2 how to effectively align model behavior through data on model side i tackle 3 how to efficiently deploy large models and 4 how to effectively adapt large models to downstream applications preprints graph based captioning enhancing visual descriptions by interconnecting region captions 2024 yu guan hsieh cheng yu hsieh shih ying yeh louis béthune hadi pour ansari pavan kumar anasosalu vasu chun liang li ranjay krishna oncel tuzel marco cuturi publications realedit reddit edits as a large scale empirical dataset for image transformations peter sushko ayana bharadwaj zhi yang lim vasily ilin ben caffee dongping chen mohammadreza salehi cheng yu hsieh ranjay krishna cvpr 2025 perception tokens enhance visual reasoning in multimodal language models mahtab bigverdi zelun luo cheng yu hsieh ethan shen dongping chen linda g shapiro ranjay krishna cvpr 2025 synthetic visual genome jae sung park zixian ma linjie li chenhao zheng cheng yu hsieh ximing lu khyathi chandu quan kong norimasa kobori ali farhadi yejin choi ranjay krishna cvpr 2025 nvila efficient frontier visual language models nvila team cvpr 2025 the unmet promise of synthetic training images using retrieved real images performs better scott geng cheng yu hsieh vivek ramanujan matthew wallingford chun liang li pang wei koh ranjay krishna neurips 2024 datacomp lm in search of the next generation of training sets for language models datacomp lm team neurips 2024 lookback lens detecting and mitigating contextual hallucinations in large language models using only attention maps yung sung chuang linlu qiu cheng yu hsieh ranjay krishna yoon kim james glass emnlp 2024 is c4 dataset enough for pruning an investigation of calibration data for llm pruning abhinav bandari lu yin cheng yu hsieh ajay jaiswal tianlong chen li shen ranjay krishna shiwei liu emnlp 2024 the hard positive truth about vision language compositionality amita kamath cheng yu hsieh kai wei chang ranjay krishna eccv 2024 found in the middle calibrating positional attention bias improves long context utilization cheng yu hsieh yung sung chuang chun liang li zifeng wang long le abhishek kumar james r glass alexander ratner chen yu lee ranjay krishna tomas pfister acl findings 2024 outlier weighed layerwise sparsity owl a missing secret sauce for pruning llms to high sparsity lu yin you wu zhenyu zhang cheng yu hsieh yaqing wang yiling jia gen li ajay jaiswal mykola pechenizkiy yi liang michael bendersky zhangyang wang shiwei liu icml 2024 sugarcrepe fixing hackable benchmarks for vision language compositionality cheng yu hsieh jieyu zhang zixian ma aniruddha kembhavi ranjay krishna neurips 2023 tool documentation enables zero shot tool usage with large language models cheng yu hsieh si an chen chun liang li yasuhisa fujii alexander ratner chen yu lee ranjay krishna tomas pfister technical report 2023 distilling step by step outperforming larger language models with less training data and smaller model sizes cheng yu hsieh chun liang li chih kuan yeh hootan nakhost yasuhisa fujii alexander ratner ranjay krishna chen yu lee tomas pfister acl findings 2023 nemo guiding and contextualizing weak supervision for interactive data programming cheng yu hsieh jieyu zhang and alexander ratner vldb 2023 a survey on programmatic weak supervision jieyu zhang cheng yu hsieh yue yu chao zhang and alexander ratner technical report 2022 understanding programmatic weak supervision via source aware influence function jieyu zhang haonan wang cheng yu hsieh and alexander ratner neurips 2022 evaluations and methods for explanation through robustness analysis cheng yu hsieh chih kuan yeh xuanqing liu pradeep ravikumar seungyeon kim sanjiv kumar and cho jui hsieh iclr 2021 on the in fidelity and sensitivity of explanations chih kuan yeh cheng yu hsieh arun sai suggala david inouye and pradeep ravikumar neurips 2019 a pseudo label method for coarse to fine multi label learning with limited supervision cheng yu hsieh miao xu gang niu hsuan tien lin and masashi sugiyama learning from limited labeled data iclr 2019 a deep model with local surrogate loss for general cost sensitive multi label learning cheng yu hsieh yi an lin and hsuan tien lin aaai 2018 automatic bridge bidding using deep reinforcement learning chih kuan yeh cheng yu hsieh and hsuan tien lin ieee transactions on games 2018 professional experience research intern apple machine learning research mentor hadi pouransari and pavan kumar anasosalu vasu spring 2024 present student researcher google cloud ai research mentor chen yu lee and chun liang li summer 2022 winter 2024 research intern riken aip mentor masashi sugiyama april 2018 july 2018 hosted on github pages theme by orderedlist