René's URL Explorer Experiment


Title: Junyoung Park

Open Graph Title: Junyoung Park

X Title: Junyoung Park

Mail addresses
junyoungpark.ml@gmail.com

Opengraph URL: https://junyoungpark.github.io/

Generator: Jekyll v3.10.0

direct link

Domain: junyoungpark.github.io

Hey, it has json ld scripts: 
{"@context":"https://schema.org","@type":"WebSite","headline":"Junyoung Park","name":"Junyoung Park","publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"https://junyoungpark.github.io/assets/img/junyoung.png"}},"url":"https://junyoungpark.github.io/"}
NoneIE=edge
og:localeen_US
og:site_nameJunyoung Park
og:typewebsite
twitter:cardsummary

Links:

Skip to contenthttps://junyoungpark.github.io#content
Junyoung Parkhttps://junyoungpark.github.io#overview
Abouthttps://junyoungpark.github.io#overview
Research Focushttps://junyoungpark.github.io#research
Publicationshttps://junyoungpark.github.io#publications
Newshttps://junyoungpark.github.io#news
Educationhttps://junyoungpark.github.io#education
Qualcomm AI Researchhttps://www.qualcomm.com/research/artificial-intelligence
CVhttps://junyoungpark.github.io/assets/file/CV_junyoungpark.pdf
Google Scholarhttps://scholar.google.com/citations?user=az8czv8AAAAJ
GitHubhttps://github.com/Junyoungpark
query-oriented KV selection (QuoKA)https://arxiv.org/abs/2602.08722
KV cache eviction for long contexts (KeyDiff)https://arxiv.org/abs/2504.15364
recursive speculative decodinghttps://arxiv.org/abs/2402.14160
draft-model alignment for speculative decodinghttps://arxiv.org/abs/2403.00858
parallel autoregressive policies for multi-agent optimization (PARCO)https://arxiv.org/abs/2409.03811
an RL library for combinatorial optimization (RL4CO)https://arxiv.org/abs/2306.17100
QuoKA: Query-Oriented KV Selection for Efficient LLM Prefillhttps://arxiv.org/abs/2602.08722
[paper]https://arxiv.org/abs/2602.08722
KeyDiff: Key Similarity-Based KV Cache Eviction for Long-Context LLM Inference in Resource-Constrained Environmentshttps://arxiv.org/abs/2504.15364
[paper]https://arxiv.org/abs/2504.15364
PARCO: Parallel Autoregressive Models for Multi-Agent Combinatorial Optimizationhttps://arxiv.org/abs/2409.03811
[paper]https://arxiv.org/abs/2409.03811
RL4CO: A Unified Reinforcement Learning for Combinatorial Optimization Libraryhttps://arxiv.org/abs/2306.17100
[paper]https://arxiv.org/abs/2306.17100
[website]https://rl4co.ai4co.org
CAOTE: KV Caching through Attention Output Error-Based Token Evictionhttps://openreview.net/forum?id=yolbP0NoZv
Routefinder: Towards Foundation Models for Vehicle Routing Problemshttps://arxiv.org/abs/2406.15007
[paper]https://arxiv.org/abs/2406.15007
On Speculative Decoding for Multimodal Large Language Modelshttps://arxiv.org/abs/2404.08856
[paper]https://arxiv.org/abs/2404.08856
Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMshttps://arxiv.org/abs/2403.00858
[paper]https://arxiv.org/abs/2403.00858
Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacementhttps://arxiv.org/abs/2402.14160
[paper]https://arxiv.org/abs/2402.14160
Generating Dispatching Rules for the Interrupting Swap-Allowed Blocking Job Shop Scheduling Problem Using Graph Neural Network and Reinforcement Learninghttps://asmedigitalcollection.asme.org/manufacturingscience/article-abstract/146/1/011009/1168977/
[paper]https://asmedigitalcollection.asme.org/manufacturingscience/article-abstract/146/1/011009/1168977/
Neuro CROSS Exchange: Learning to CROSS Exchange to Solve Realistic Vehicle Routing Problemshttps://arxiv.org/pdf/2206.02771.pdf
[paper]https://arxiv.org/pdf/2206.02771.pdf
Learning Context-Aware Adaptive Solvers to Accelerate Convex Quadratic Programminghttps://arxiv.org/pdf/2211.12443.pdf
[paper]https://arxiv.org/pdf/2211.12443.pdf
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimizationhttps://arxiv.org/pdf/2205.13209.pdf
[paper]https://arxiv.org/pdf/2205.13209.pdf
[code]https://github.com/alstn12088/Sym-NCO
Convergent Graph Solvershttps://openreview.net/pdf?id=ItkxLQU01lD
[paper]https://openreview.net/pdf?id=ItkxLQU01lD
[code]https://github.com/Junyoungpark/CGS
Learning to Schedule Job-Shop Problems: Representation and Policy Learning Using Graph Neural Network and Reinforcement Learninghttps://www.tandfonline.com/doi/epdf/10.1080/00207543.2020.1870013?needAccess=true&role=button
[paper]https://www.tandfonline.com/doi/epdf/10.1080/00207543.2020.1870013?needAccess=true&role=button
[top-cited 2021/22]https://junyoungpark.github.io/assets/file/certificate.pdf
Graph Neural Ordinary Differential Equationshttps://arxiv.org/pdf/1911.07532.pdf
[paper]https://arxiv.org/pdf/1911.07532.pdf
[code]https://github.com/Zymrael/gde
Physics-Induced Graph Neural Network: An Application to Wind-Farm Power Estimationhttps://www.sciencedirect.com/science/article/abs/pii/S0360544219315555
[paper]https://www.sciencedirect.com/science/article/abs/pii/S0360544219315555
[code]https://github.com/Junyoungpark/PGNN
[slides]https://github.com/Junyoungpark/PGNN/blob/main/wind_farm_presentation.pdf
CAOTEhttps://openreview.net/forum?id=yolbP0NoZv
QuoKAhttps://arxiv.org/abs/2504.15364
KeyDiffhttps://arxiv.org/abs/2504.15364
PARCOhttps://arxiv.org/abs/2409.03811
Routefinderhttps://arxiv.org/abs/2406.15007
RL4COhttps://arxiv.org/abs/2306.17100
rl4co.ai4co.orghttps://rl4co.ai4co.org
Recursive Speculative Decodinghttps://arxiv.org/abs/2402.14160
KAISThttp://ie.kaist.ac.kr/
Jinkyoo Parkhttp://silab.kaist.ac.kr
KAISThttp://ie.kaist.ac.kr/

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