ICML

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Machine Learning

2024 · 2,610 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。

2024 · ICML
Implicit meta-learning may lead language models to trust more reliable sources
Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Kacper Mlodozeniec, Tegan Maharaj, David Krueger
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy
Bo Li, Wei Wang, Peng Ye
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy
Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements
Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improved Dimensionality Dependence for Zeroth-Order Optimisation over Cross-Polytopes
Weijia Shao
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improved Generalization of Weight Space Networks via Augmentations
Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
Jonathan Scott, Áine Cahill
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improved Operator Learning by Orthogonal Attention
Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Kevin Scaman, Giovanni Neglia
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Adversarial Energy-Based Model via Diffusion Process
Cong Geng, Tian Han, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Søren Hauberg, Bo Li
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Antibody Humanness Prediction using Patent Data
Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Computational Complexity in Statistical Models with Local Curvature Information
Pedram Akbarian, Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Context Understanding in Multimodal Large Language Models via Multimodal Composition Learning
Wei Li, Hehe Fan, Yongkang Wong, Yi Yang, Mohan Kankanhalli
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
Xinyu Peng, Ziyang Zheng, Wenrui Dai, Nuoqian Xiao, Chenglin Li, Junni Zou, Hongkai Xiong
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning
Yuelin Zhang, Jiacheng Cen, Jiaqi Han, Zhiqiang Zhang, Jun Zhou, Wenbing Huang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting
Da Wang, Lin Li, Wei Wei, Qixian Yu, Jianye Hao, Jiye Liang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Gradient-Guided Nested Sampling for Posterior Inference
Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault-Levasseur
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference
Yujin Han, Difan Zou
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
Joonho Lee, Jae Oh Woo, Juree Seok, Parisa Hassanzadeh, Wooseok Jang, Juyoun Son, Sima Didari, Baruch Gutow, Heng Hao, Hankyu Moon, Wenjun Hu, Yeong-Dae Kwon, Taehee Lee, Seungjai Min
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Interpretation Faithfulness for Vision Transformers
Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Ratsch, Vincent Fortuin
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Neural Logic Machines via Failure Reflection
Zhiming Li, Yushi Cao, Yan Zheng, Xu Liu, Bozhi Wu, Tianlin Li, Xiufeng Xu, Junzhe Jiang, Yon Shin Teo, Shang-Wei Lin, Yang Liu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Open-Ended Text Generation via Adaptive Decoding
Wenhong Zhu, Hongkun Hao, Zhiwei He, Yiming Ai, Rui Wang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining
Aaron Jiaxun Li, Robin Netzorg, Zhihan Cheng, Zhuoqin Zhang, Bin Yu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization
Nayeong Kim, Juwon Kang, Sungsoo Ahn, Jungseul Ok, Suha Kwak
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving SAM Requires Rethinking its Optimization Formulation
Wanyun Xie, Fabian Latorre, Kimon Antonakopoulos, Thomas Pethick, Volkan Cevher
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games
Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Sharpness-Aware Minimization by Lookahead
Runsheng Yu, Youzhi Zhang, James Kwok
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Token-Based World Models with Parallel Observation Prediction
Lior Cohen, Kaixin Wang, Bingyi Kang, Shie Mannor
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving Transformers with Dynamically Composable Multi-Head Attention
Da Xiao, Qingye Meng, Shengping Li, Xingyuan Yuan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Improving fine-grained understanding in image-text pre-training
Ioana Bica, Anastasija Ilic, Matthias Bauer, Goker Erdogan, Matko Bošnjak, Christos Kaplanis, Alexey A. Gritsenko, Matthias Minderer, Charles Blundell, Razvan Pascanu, Jovana Mitrovic
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In value-based deep reinforcement learning, a pruned network is a good network
Johan Samir Obando Ceron, Aaron Courville, Pablo Samuel Castro
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
Sili Huang, Jifeng Hu, Hechang Chen, Lichao Sun, Bo Yang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Eddie Bergman, Frank Hutter
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-Context Language Learning: Architectures and Algorithms
Ekin Akyürek, Bailin Wang, Yoon Kim, Jacob Andreas
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-Context Learning Agents Are Asymmetric Belief Updaters
Johannes A. Schubert, Akshay Kumar Jagadish, Marcel Binz, Eric Schulz
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-Context Principle Learning from Mistakes
Tianjun Zhang, Aman Madaan, Luyu Gao, Steven Zheng, Swaroop Mishra, Yiming Yang, Niket Tandon, Uri Alon
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-Context Reinforcement Learning for Variable Action Spaces
Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Sergey Kolesnikov
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
Shiqi Chen, Miao Xiong, Junteng Liu, Zhengxuan Wu, Teng Xiao, Siyang Gao, Junxian He
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-Context Unlearning: Language Models as Few-Shot Unlearners
Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-context Convergence of Transformers
Yu Huang, Yuan Cheng, Yingbin Liang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-context Learning on Function Classes Unveiled for Transformers
Zhijie Wang, Bo Jiang, Shuai Li
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
Sheng Liu, Haotian Ye, Lei Xing, James Y. Zou
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Incentivized Learning in Principal-Agent Bandit Games
Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Incorporating Information into Shapley Values: Reweighting via a Maximum Entropy Approach
Darya Biparva, Donatello Materassi
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Incorporating probabilistic domain knowledge into deep multiple instance learning
Ghadi S. Al Hajj, Aliaksandr Hubin, Chakravarthi Kanduri, Milena Pavlovic, Knut Dagestad Rand, Michael Widrich, Anne Schistad Solberg, Victor Greiff, Johan Pensar, Günter Klambauer, Geir Kjetil Sandve
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Incremental Topological Ordering and Cycle Detection with Predictions
Samuel Mccauley, Benjamin Moseley, Aidin Niaparast, Shikha Singh
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Indirectly Parameterized Concrete Autoencoders
Alfred Nilsson, Klas Wijk, Sai Bharath Chandra Gutha, Erik Englesson, Alexandra Hotti, Carlo Saccardi, Oskar Kviman, Jens Lagergren, Ricardo Vinuesa Motilva, Hossein Azizpour
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning
Xinran Li, Zifan Liu, Shibo Chen, Jun Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Individual Fairness in Graph Decomposition
Kamesh Munagala, Govind S. Sankar
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints
Oscar Smee, Fred Roosta
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
InferCept: Efficient Intercept Support for Augmented Large Language Model Inference
Reyna Abhyankar, Zijian He, Vikranth Srivatsa, Hao Zhang, Yiying Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing
Gabriel Arpino, Xiaoqi Liu, Ramji Venkataramanan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
Allen Tran, Aurelien Bibaut, Nathan Kallus
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
Xueyu Hu, Ziyu Zhao, Shuang Wei, Ziwei Chai, Qianli Ma, Guoyin Wang, Xuwu Wang, Jing Su, Jingjing Xu, Ming Zhu, Yao Cheng, Jianbo Yuan, Jiwei Li, Kun Kuang, Yang Yang, Hongxia Yang, Fei Wu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Infinite-Horizon Distributionally Robust Regret-Optimal Control
Taylan Kargin, Joudi Hajar, Vikrant Malik, Babak Hassibi
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization
Zhengyang Hu, Song Kang, Qunsong Zeng, Kaibin Huang, Yanchao Yang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing
Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Information Flow in Self-Supervised Learning
Zhiquan Tan, Jingqin Yang, Weiran Huang, Yang Yuan, Yifan Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Information-Directed Pessimism for Offline Reinforcement Learning
Alec Koppel, Sujay Bhatt, Jiacheng Guo, Joe Eappen, Mengdi Wang, Sumitra Ganesh
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks
Guanhua Zhang, Moritz Hardt
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Initial Guessing Bias: How Untrained Networks Favor Some Classes
Emanuele Francazi, Aurelien Lucchi, Marco Baity-Jesi
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining
Boxin Wang, Wei Ping, Lawrence Mcafee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
InstructSpeech: Following Speech Editing Instructions via Large Language Models
Rongjie Huang, Ruofan Hu, Yongqi Wang, Zehan Wang, Xize Cheng, Ziyue Jiang, Zhenhui Ye, Dongchao Yang, Luping Liu, Peng Gao, Zhou Zhao
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models
Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Instruction Tuning for Secure Code Generation
Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin Vechev
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Integrated Hardware Architecture and Device Placement Search
Irene Wang, Jakub Tarnawski, Amar Phanishayee, Divya Mahajan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Integrating Global Context Contrast and Local Sensitivity for Blind Image Quality Assessment
Xudong Li, Runze Hu, Jingyuan Zheng, Yan Zhang, Shengchuan Zhang, Xiawu Zheng, Ke Li, Yunhang Shen, Yutao Liu, Pingyang Dai, Rongrong Ji
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
Manuel Brenner, Florian Hess, Georgia Koppe, Daniel Durstewitz
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C Hughes
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng, Florence Regol, Mark Coates
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Interaction-based Retrieval-augmented Diffusion Models for Protein-specific 3D Molecule Generation
Zhilin Huang, Ling Yang, Xiangxin Zhou, Chujun Qin, Yijie Yu, Xiawu Zheng, Zikun Zhou, Wentao Zhang, Yu Wang, Wenming Yang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Interplay of ROC and Precision-Recall AUCs: Theoretical Limits and Practical Implications in Binary Classification
Martin Mihelich, François Castagnos, Charles Dognin
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
Jacob Yoke Hong Si, Wendy Yusi Cheng, Michael Cooper, Rahul Krishnan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Interpretability Illusions in the Generalization of Simplified Models
Dan Friedman, Andrew Kyle Lampinen, Lucas Dixon, Danqi Chen, Asma Ghandeharioun
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Interpretable Deep Clustering for Tabular Data
Jonathan Svirsky, Ofir Lindenbaum
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Interpreting Equivariant Representations
Andreas Abildtrup Hansen, Anna Calissano, Aasa Feragen
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Interpreting and Improving Diffusion Models from an Optimization Perspective
Frank Permenter, Chenyang Yuan
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Interpreting and Improving Large Language Models in Arithmetic Calculation
Wei Zhang, Chaoqun Wan, Yonggang Zhang, Yiu-Ming Cheung, Xinmei Tian, Xu Shen, Jieping Ye
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models
Bai Xiaofan, Chaoxiang He, Xiaojing Ma, Bin Benjamin Zhu, Hai Jin
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Intersectional Unfairness Discovery
Gezheng Xu, Qi Chen, Charles Ling, Boyu Wang, Changjian Shui
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Invariant Risk Minimization Is A Total Variation Model
Zhao-Rong Lai, Weiwen Wang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects
Aaron Fisher
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning
Donghu Kim, Hojoon Lee, Kyungmin Lee, Dongyoon Hwang, Jaegul Choo
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
Weijia Zhang, Chenlong Yin, Hao Liu, Xiaofang Zhou, Hui Xiong
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
Shusheng Xu, Wei Fu, Jiaxuan Gao, Wenjie Ye, Weilin Liu, Zhiyu Mei, Guangju Wang, Chao Yu, Yi Wu
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
Mira Juergens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
Fabian Falck, Ziyu Wang, Christopher C. Holmes
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
Lei Zhao, Mengdi Wang, Yu Bai
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Is Kernel Prediction More Powerful than Gating in Convolutional Neural Networks?
Lorenz K Muller
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?
Huy Nguyen, Pedram Akbarian, Nhat Ho
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Isometric Representation Learning for Disentangled Latent Space of Diffusion Models
Jaehoon Hahm, Junho Lee, Sunghyun Kim, Joonseok Lee
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF
Banghua Zhu, Michael Jordan, Jiantao Jiao
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint
Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang
Machine Learning
日本大学: 関与なし（対象大学）
2024 · ICML
Iterative Regularized Policy Optimization with Imperfect Demonstrations
Gong Xudong, Feng Dawei, Kele Xu, Yuanzhao Zhai, Chengkang Yao, Weijia Wang, Bo Ding, Huaimin Wang
Machine Learning
日本大学: 関与なし（対象大学）