ICML

2022–2026 LIVE

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Machine Learning

2023 · 1,828 papers

このEditionに反映済みのmain full research papersのみ掲載しています。著者リンクは日本の大学への著者別所属根拠がある場合に限ります。

2023 · ICML
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
Julian Bitterwolf, Maximilian Müller, Matthias Hein
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
Jaejun Lee, Chanyoung Chung, Joyce Jiyoung Whang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
IncDSI: Incrementally Updatable Document Retrieval
Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi, Kilian Q Weinberger
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Incentivizing Exploration with Linear Contexts and Combinatorial Actions
Mark Sellke
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Individually Fair Learning with One-Sided Feedback
Yahav Bechavod, Aaron Roth
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Inferring Relational Potentials in Interacting Systems
Armand Comas, Yilun Du, Christian Fernandez Lopez, Sandesh Ghimire, Mario Sznaier, Joshua B. Tenenbaum, Octavia Camps
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Infinite Action Contextual Bandits with Reusable Data Exhaust
Mark Rucker, Yinglun Zhu, Paul Mineiro
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Inflow, Outflow, and Reciprocity in Machine Learning
Mukund Sundararajan, Walid Krichene
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
InfoOT: Information Maximizing Optimal Transport
Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Information-Theoretic State Space Model for Multi-View Reinforcement Learning
Hyeongjoo Hwang, Seokin Seo, Youngsoo Jang, Sungyoon Kim, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning
Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Input Perturbation Reduces Exposure Bias in Diffusion Models
Mang Ning, Enver Sangineto, Angelo Porrello, Simone Calderara, Rita Cucchiara
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Input uncertainty propagation through trained neural networks
Paul Monchot, Loic Coquelin, Sébastien Julien Petit, Sébastien Marmin, Erwan Le Pennec, Nicolas Fischer
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
Ajay Kumar Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Instrumental Variable Estimation of Average Partial Causal Effects
Yuta Kawakami, Manabu Kuroki, Jin Tian
Machine Learning
日本大学: 横浜国立大学
2023 · ICML
Integrating Prior Knowledge in Contrastive Learning with Kernel
Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset, Edouard Duchesnay, Pietro Gori
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Interactive Object Placement with Reinforcement Learning
Shengping Zhang, Quanling Meng, Qinglin Liu, Liqiang Nie, Bineng Zhong, Xiaopeng Fan, Rongrong Ji
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Internally Rewarded Reinforcement Learning
Mengdi Li, Xufeng Zhao, Jae Hee Lee, Cornelius Weber, Stefan Wermter
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Internet Explorer: Targeted Representation Learning on the Open Web
Alexander Cong Li, Ellis Langham Brown, Alexei A Efros, Deepak Pathak
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
Jiacheng Zhu, Jielin Qiu, Aritra Guha, Zhuolin Yang, Xuanlong Nguyen, Bo Li, Ding Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Interpretable Neural-Symbolic Concept Reasoning
Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frederic Precioso, Mateja Jamnik, Giuseppe Marra
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Interval Bound Interpolation for Few-shot Learning with Few Tasks
Shounak Datta, Sankha Subhra Mullick, Anish Chakrabarty, Swagatam Das
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Interventional Causal Representation Learning
Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs
Raif M. Rustamov, Subhabrata Majumdar
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning
Joar Max Viktor Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Ondrej Biza, Sjoerd Van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Thomas Kipf
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Inverse Reinforcement Learning without Reinforcement Learning
Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Steven Wu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Investigating the Role of Model-Based Learning in Exploration and Transfer
Jacob C Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Theophane Weber, Jessica B Hamrick
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks?
Dmitry Metelev, Alexander Rogozin, Dmitry Kovalev, Alexander Gasnikov
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Is Learning Summary Statistics Necessary for Likelihood-free Inference?
Yanzhi Chen, Michael U. Gutmann, Adrian Weller
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Is Overfitting Necessary for Implicit Video Representation?
Hee Min Choi, Hyoa Kang, Dokwan Oh
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Iterative Approximate Cross-Validation
Yuetian Luo, Zhimei Ren, Rina Barber
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift
Drew Prinster, Suchi Saria, Anqi Liu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Jump-Start Reinforcement Learning
Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Andrea Coletta, Svitlana Vyetrenko, Tucker Balch
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
KDEformer: Accelerating Transformers via Kernel Density Estimation
Amir Zandieh, Insu Han, Majid Daliri, Amin Karbasi
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network
Marie Guyomard, Susana Barbosa, Lionel Fillatre
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Kernel QuantTree
Diego Stucchi, Paolo Rizzo, Nicolò Folloni, Giacomo Boracchi
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation
Junyoung Park, Jeongyoun Ahn, Cheolwoo Park
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
Woojun Kim, Jeonghye Kim, Youngchul Sung
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
LEVER: Learning to Verify Language-to-Code Generation with Execution
Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-Tau Yih, Sida Wang, Xi Victoria Lin
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
LIV: Language-Image Representations and Rewards for Robotic Control
Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
LSDS++ : Dual Sampling for Accelerated k-means++
Chenglin Fan, Ping Li, Xiaoyun Li
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity
Dixian Zhu, Yiming Ying, Tianbao Yang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Label differential privacy and private training data release
Robert Istvan Busa-Fekete, Andres Munoz Medina, Umar Syed, Sergei Vassilvitskii
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
Amin Karbasi, Nikki Lijing Kuang, Yian Ma, Siddharth Mitra
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Language Instructed Reinforcement Learning for Human-AI Coordination
Hengyuan Hu, Dorsa Sadigh
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Large Language Models Can Be Easily Distracted by Irrelevant Context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H. Chi, Nathanael Schärli, Denny Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Large Language Models Struggle to Learn Long-Tail Knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Last Switch Dependent Bandits with Monotone Payoff Functions
Ayoub Foussoul, Vineet Goyal, Orestis Papadigenopoulos, Assaf Zeevi
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Latent Traversals in Generative Models as Potential Flows
Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Layered State Discovery for Incremental Autonomous Exploration
Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning
Boyin Liu, Zhiqiang Pu, Yi Pan, Jianqiang Yi, Yanyan Liang, D. Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Rui Xue, Haoyu Han, Mohamadali Torkamani, Jian Pei, Xiaorui Liu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
LeadFL: Client Self-Defense against Model Poisoning in Federated Learning
Chaoyi Zhu, Stefanie Roos, Lydia Y. Chen
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learn to Accumulate Evidence from All Training Samples: Theory and Practice
Deep Shankar Pandey, Qi Yu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learnability and Algorithm for Continual Learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Bing Liu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Affinity with Hyperbolic Representation for Spatial Propagation
Jin-Hwi Park, Jaesung Choe, Inhwan Bae, Hae-Gon Jeon
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Antidote Data to Individual Unfairness
Peizhao Li, Ethan Xia, Hongfu Liu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Belief Representations for Partially Observable Deep RL
Andrew Wang, Andrew C Li, Toryn Q. Klassen, Rodrigo Toro Icarte, Sheila A. Mcilraith
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh James Leather, Yuandong Tian
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Control by Iterative Inversion
Gal Leibovich, Guy Jacob, Or Avner, Gal Novik, Aviv Tamar
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Control-Oriented Dynamical Structure from Data
Spencer M. Richards, Jean-Jacques Slotine, Navid Azizan, Marco Pavone
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Controllable Degradation for Real-World Super-Resolution via Constrained Flows
Seobin Park, Dongjin Kim, Sungyong Baik, Tae Hyun Kim
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic
Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Deep Time-index Models for Time Series Forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Dense Correspondences between Photos and Sketches
Xuanchen Lu, Xiaolong Wang, Judith E Fan
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Distributions over Quantum Measurement Outcomes
Weiyuan Gong, Scott Aaronson
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Yiming Cui, Linjie Yang, Haichao Yu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
Dominik Schnaus, Jongseok Lee, Daniel Cremers, Rudolph Triebel
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Functional Distributions with Private Labels
Changlong Wu, Yifan Wang, Ananth Grama, Wojciech Szpankowski
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Cristian Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Globally Smooth Functions on Manifolds
Juan Cervino, Luiz F. O. Chamon, Benjamin David Haeffele, Rene Vidal, Alejandro Ribeiro
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
Binyamin Perets, Mark Kozdoba, Shie Mannor
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Instance-Specific Augmentations by Capturing Local Invariances
Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Intuitive Policies Using Action Features
Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Nicolaus Foerster
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation
Shengcao Cao, Mengtian Li, James Hays, Deva Ramanan, Yu-Xiong Wang, Liangyan Gui
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Mixtures of Gaussians with Censored Data
Wai Ming Tai, Bryon Aragam
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Mixtures of Markov Chains and MDPs
Chinmaya Kausik, Kevin Tan, Ambuj Tewari
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
Pingchuan Ma, Peter Yichen Chen, Bolei Deng, Joshua B. Tenenbaum, Tao Du, Chuang Gan, Wojciech Matusik
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Neural PDE Solvers with Parameter-Guided Channel Attention
Makoto Takamoto, Francesco Alesiani, Mathias Niepert
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation
Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lazaro-Gredilla
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Perturbations to Explain Time Series Predictions
Joseph Enguehard
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Physical Models that Can Respect Conservation Laws
Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Preconditioners for Conjugate Gradient PDE Solvers
Yichen Li, Peter Yichen Chen, Tao Du, Wojciech Matusik
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Prescriptive ReLU Networks
Wei Sun, Asterios Tsiourvas
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Rate Schedules in the Presence of Distribution Shift
Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Fengxue Zhang, Jialin Song, James C Bowden, Alexander Ladd, Yisong Yue, Thomas Desautels, Yuxin Chen
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Representations without Compositional Assumptions
Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela Van Der Schaar
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
Baorui Ma, Yu-Shen Liu, Zhizhong Han
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
Zaixi Zhang, Qi Liu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Temporally AbstractWorld Models without Online Experimentation
Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
Sicheng Zhu, Bang An, Furong Huang, Sanghyun Hong
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning Unnormalized Statistical Models via Compositional Optimization
Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
Pengfei Li, Jianyi Yang, Shaolei Ren
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning in POMDPs is Sample-Efficient with Hindsight Observability
Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning the Dynamics of Sparsely Observed Interacting Systems
Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot, Agathe Guilloux
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs
Sara Venturini, Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco
Machine Learning
日本大学: 関与なし（対象大学）