ICML

2021–2025 確定版

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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
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers
Sitan Chen, Giannis Daras, Alex Dimakis
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Resurrecting Recurrent Neural Networks for Long Sequences
Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, Soham De
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Rethink DARTS Search Space and Renovate a New Benchmark
Jiuling Zhang, Zhiming Ding
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Rethinking Backdoor Attacks
Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
Fang Wu, Siyuan Li, Xurui Jin, Yinghui Jiang, Dragomir Radev, Zhangming Niu, Stan Z. Li
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues
Jiaxuan Chen, Yu Qi, Gang Pan
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster $\text{L}$-/$\text{L}^\natural$-Convex Function Minimization
Shinsaku Sakaue, Taihei Oki
Machine Learning
日本大学: 東京大学
2023 · ICML
Rethinking Weak Supervision in Helping Contrastive Learning
Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Retrieval-Augmented Multimodal Language Modeling
Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Richard James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Wen-Tau Yih
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Retrosynthetic Planning with Dual Value Networks
Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Bellman Errors for Offline Model Selection
Joshua P Zitovsky, Daniel De Marchi, Rishabh Agarwal, Michael Rene Kosorok
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Junyuan Hong, Yi Zeng, Shuyang Yu, Lingjuan Lyu, Ruoxi Jia, Jiayu Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization
Yun-Hsuan Lien, Ping-Chun Hsieh, Yu-Shuen Wang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees
Anastasia Koloskova, Hadrien Hendrikx, Sebastian U Stich
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature
Khang Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, Nhat Ho, Stanley Osher, Tan Minh Nguyen
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Pseudo-Label for Single-Positive Multi-Label Learning
Biao Liu, Ning Xu, Jiaqi Lv, Xin Geng
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Sampling for Combinatorial Optimization
Haoran Sun, Katayoon Goshvadi, Azade Nova, Dale Schuurmans, Hanjun Dai
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Simple Regret: Fast Rates for Returning a Good Arm
Yao Zhao, Connor Stephens, Csaba Szepesvari, Kwang-Sung Jun
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Structured Variational Autoencoders
Yixiu Zhao, Scott Linderman
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
Zexi Li, Tao Lin, Xinyi Shang, Chao Wu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
Asuman E. Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Reward-Mixing MDPs with Few Latent Contexts are Learnable
Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Rigid Body Flows for Sampling Molecular Crystal Structures
Jonas Köhler, Michele Invernizzi, Pim De Haan, Frank Noe
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Budget Pacing with a Single Sample
Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Camera Pose Refinement for Multi-Resolution Hash Encoding
Hwan Heo, Taekyung Kim, Jiyoung Lee, Jaewon Lee, Soohyun Kim, Hyunwoo J. Kim, Jin-Hwa Kim
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Collaborative Learning with Linear Gradient Overhead
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stephan Clémençon
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Explanation for Free or At the Cost of Faithfulness
Zeren Tan, Yang Tian
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning
Junghoon Kim, Taejoon Kim, David Love, Christopher Brinton
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
Louis Béthune, Paul Novello, Guillaume Coiffier, Thibaut Boissin, Mathieu Serrurier, Quentin Vincenot, Andres Troya-Galvis
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Perception through Equivariance
Chengzhi Mao, Lingyu Zhang, Abhishek Vaibhav Joshi, Junfeng Yang, Hao Wang, Carl Vondrick
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Satisficing MDPs
Haolin Ruan, Siyu Zhou, Zhi Chen, Chin Pang Ho
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Situational Reinforcement Learning in Face of Context Disturbances
Jinpeng Zhang, Yufeng Zheng, Chuheng Zhang, Li Zhao, Lei Song, Yuan Zhou, Jiang Bian
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Speech Recognition via Large-Scale Weak Supervision
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine Mcleavey, Ilya Sutskever
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Subtask Learning for Compositional Generalization
Kishor Jothimurugan, Steve Hsu, Osbert Bastani, Rajeev Alur
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Weak Supervision with Variational Auto-Encoders
Francesco Tonolini, Nikolaos Aletras, Yunlong Jiao, Gabriella Kazai
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?
Ruisi Cai, Zhenyu Zhang, Zhangyang Wang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust and Scalable Bayesian Online Changepoint Detection
Matias Altamirano, Francois-Xavier Briol, Jeremias Knoblauch
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robust and private stochastic linear bandits
Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robustly Learning a Single Neuron via Sharpness
Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Robustness in Multimodal Learning under Train-Test Modality Mismatch
Brandon Mckinzie, Vaishaal Shankar, Joseph Yitan Cheng, Yinfei Yang, Jonathon Shlens, Alexander T Toshev
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch
Xunyi Zhao, Théotime Le Hellard, Lionel Eyraud-Dubois, Julia Gusak, Olivier Beaumont
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Rotation and Translation Invariant Representation Learning with Implicit Neural Representations
Sehyun Kwon, Joo Young Choi, Ernest K. Ryu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Run-off Election: Improved Provable Defense against Data Poisoning Attacks
Keivan Rezaei, Kiarash Banihashem, Atoosa Chegini, Soheil Feizi
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SAAL: Sharpness-Aware Active Learning
Yoon-Yeong Kim, Youngjae Cho, Joonho Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon
Atish Agarwala, Yann Dauphin
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation
Shikun Sun, Longhui Wei, Junliang Xing, Jia Jia, Qi Tian
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SE(3) diffusion model with application to protein backbone generation
Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi Jaakkola
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
Junran Wu, Xueyuan Chen, Bowen Shi, Shangzhe Li, Ke Xu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance
Amit Attia, Tomer Koren
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SGD with Large Step Sizes Learns Sparse Features
Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SLAMB: Accelerated Large Batch Training with Sparse Communication
Hang Xu, Wenxuan Zhang, Jiawei Fei, Yuzhe Wu, Tingwen Xie, Jun Huang, Yuchen Xie, Mohamed Elhoseiny, Panos Kalnis
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
Zichong Li, Yanbo Xu, Simiao Zuo, Haoming Jiang, Chao Zhang, Tuo Zhao, Hongyuan Zha
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
Dongseok Shim, Seungjae Lee, H. Jin Kim
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
Iris A.M. Huijben, Arthur Andreas Nijdam, Sebastiaan Overeem, Merel M Van Gilst, Ruud Van Sloun
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning.
Tanguy Marchand, Regis Loeb, Ulysse Marteau-Ferey, Jean Ogier Du Terrail, Arthur Pignet
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning
Souradip Chakraborty, Amrit Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Safe Offline Reinforcement Learning with Real-Time Budget Constraints
Qian Lin, Bo Tang, Zifan Wu, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
Seyed Amir Hossein Saberi, Amir Najafi, Abolfazl Motahari, Babak Khalaj
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Sample Complexity of Probability Divergences under Group Symmetry
Ziyu Chen, Markos Katsoulakis, Luc Rey-Bellet, Wei Zhu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation
Ilan Naiman, Nimrod Berman, Omri Azencot
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault-Levasseur
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Sampling-based Nyström Approximation and Kernel Quadrature
Satoshi Hayakawa, Harald Oberhauser, Terry Lyons
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scalable Adaptive Computation for Iterative Generation
Allan Jabri, David J. Fleet, Ting Chen
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
Siddharth Nayak, Kenneth Choi, Wenqi Ding, Sydney Dolan, Karthik Gopalakrishnan, Hamsa Balakrishnan
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scalable Safe Policy Improvement via Monte Carlo Tree Search
Alberto Castellini, Federico Bianchi, Edoardo Zorzi, Thiago D. Simão, Alessandro Farinelli, Matthijs T. J. Spaan
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation
Jeffrey Willette, Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scaling Laws for Generative Mixed-Modal Language Models
Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, Luke Zettlemoyer
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scaling Laws for Multilingual Neural Machine Translation
Patrick Fernandes, Behrooz Ghorbani, Xavier Garcia, Markus Freitag, Orhan Firat
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scaling Laws for Reward Model Overoptimization
Leo Gao, John Schulman, Jacob Hilton
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scaling Spherical CNNs
Carlos Esteves, Jean-Jacques Slotine, Ameesh Makadia
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scaling Vision Transformers to 22 Billion Parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd Van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Scaling of Class-wise Training Losses for Post-hoc Calibration
Seungjin Jung, Seungmo Seo, Yonghyun Jeong, Jongwon Choi
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models
Shenghua Wan, Yucen Wang, Minghao Shao, Ruying Chen, De-Chuan Zhan
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
Taoan Huang, Aaron M Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Second-Order Optimization with Lazy Hessians
Nikita Doikov, El Mahdi Chayti, Martin Jaggi
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Second-order regression models exhibit progressive sharpening to the edge of stability
Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Secure Federated Correlation Test and Entropy Estimation
Qi Pang, Lun Wang, Shuai Wang, Wenting Zheng, Dawn Song
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching
Liren Yu, Jiaming Xu, Xiaojun Lin
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation
Huaishao Luo, Junwei Bao, Youzheng Wu, Xiaodong He, Tianrui Li
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Khai Nguyen, Dang Nguyen, Nhat Ho
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Self-Interpretable Time Series Prediction with Counterfactual Explanations
Jingquan Yan, Hao Wang
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains
Vishwaraj Doshi, Jie Hu, Do Young Eun
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations
Weiwei Lin, Chenhang He, Man-Wai Mak, Youzhi Tu
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Self-supervised learning of Split Invariant Equivariant representations
Quentin Garrido, Laurent Najman, Yann Lecun
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
Pranjal Aggarwal, Ameet Deshpande, Karthik R Narasimhan
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees.
Ioannis Panageas, Stratis Skoulakis, Luca Viano, Xiao Wang, Volkan Cevher
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Semi-Dual Unbalanced Quadratic Optimal Transport: fast statistical rates and convergent algorithm.
Adrien Vacher, François-Xavier Vialard
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Semi-Offline Reinforcement Learning for Optimized Text Generation
Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jie Cao, Yi Liu, Rui Yan
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model
Young-Geun Choi, Gi-Soo Kim, Yunseo Choi, Wooseong Cho, Myunghee Cho Paik, Min-Hwan Oh
Machine Learning
日本大学: 関与なし（対象大学）
2023 · ICML
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover
Machine Learning
日本大学: 関与なし（対象大学）