ICML Papers
5,975 papers found • Page 30 of 120
IMTS is Worth Time $\times$ Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction
Zhangyi Hu, Jiemin Wu, Hua XU et al.
iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
Nicolas Lell, Ansgar Scherp
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games
Tong Yang, Bo Dai, Lin Xiao et al.
In-Context Adaptation to Concept Drift for Learned Database Operations
Jiaqi Zhu, Shaofeng Cai, Shen et al.
In-Context Deep Learning via Transformer Models
Weimin Wu, Maojiang Su, Jerry Yao-Chieh Hu et al.
In-Context Denoising with One-Layer Transformers: Connections between Attention and Associative Memory Retrieval
Matthew Smart, Alberto Bietti, Anirvan Sengupta
In-Context Fine-Tuning for Time-Series Foundation Models
Matthew Faw, Rajat Sen, Yichen Zhou et al.
In-Context Learning and Occam's Razor
Eric Elmoznino, Tom Marty, Tejas Kasetty et al.
In-Context Learning as Conditioned Associative Memory Retrieval
Weimin Wu, Teng-Yun Hsiao, Jerry Yao-Chieh Hu et al.
In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention
Jianliang He, Xintian Pan, Siyu Chen et al.
In-Context Reinforcement Learning From Suboptimal Historical Data
Juncheng Dong, Moyang Guo, Ethan Fang et al.
Incorporating Arbitrary Matrix Group Equivariance into KANs
Lexiang Hu, Yisen Wang, Zhouchen Lin
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems
Yujun Kim, Jaeyoung Cha, Chulhee Yun
Independence Tests for Language Models
Sally Zhu, Ahmed Ahmed, Rohith Kuditipudi et al.
Inducing, Detecting and Characterising Neural Modules: A Pipeline for Functional Interpretability in Reinforcement Learning
Anna Soligo, Pietro Ferraro, David Boyle
Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations
Kexuan Shi, Hai Chen, Leheng Zhang et al.
Inductive Moment Matching
Linqi (Alex) Zhou, Stefano Ermon, Jiaming Song
InfAlign: Inference-aware language model alignment
Ananth Balashankar, Ziteng Sun, Jonathan Berant et al.
Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang et al.
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models
Patrick Leask, Neel Nanda, Noura Al Moubayed
Info-Coevolution: An Efficient Framework for Data Model Coevolution
Ziheng Qin, Hailun Xu, Wei Yew et al.
InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory
Feifei Li, Mi Zhang, Zhaoxiang Wang et al.
Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting
Min Chen, Guansong Pang, Wenjun Wang et al.
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective
Yuanhong Zhang, Muyao Yuan, Weizhan Zhang et al.
InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference
Tianyu Cui, Song-Jun Xu, Artem Moskalev et al.
INRFlow: Flow Matching for INRs in Ambient Space
Yuyang Wang, Anurag Ranjan, Joshua M Susskind et al.
Instance Correlation Graph-based Naive Bayes
Chengyuan Li, Liangxiao Jiang, Wenjun Zhang et al.
Instance-Optimal Pure Exploration for Linear Bandits on Continuous Arms
Sho Takemori, Yuhei Umeda, Aditya Gopalan
Instruct2See: Learning to Remove Any Obstructions Across Distributions
Junhang Li, Yu Guo, Xian et al.
Instruction-Following Pruning for Large Language Models
Bairu Hou, Qibin Chen, Jianyu Wang et al.
Integer Programming for Generalized Causal Bootstrap Designs
Jennifer Brennan, Sébastien Lahaie, Adel Javanmard et al.
Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models
Yang Zheng, Wen Li, Zhaoqiang Liu
Integration-free Kernels for Equivariant Gaussian Process Modelling
Tim Steinert, David Ginsbourger, August Lykke-Møller et al.
Interaction-Aware Gaussian Weighting for Clustered Federated Learning
Alessandro Licciardi, Davide Leo, Eros Fanì et al.
Interchangeable Token Embeddings for Extendable Vocabulary and Alpha-Equivalence
İlker Işık, Ramazan Gokberk Cinbis, Ebru Gol
Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors
Jing Huang, Junyi Tao, Thomas Icard et al.
Interpolating Neural Network-Tensor Decomposition (INN-TD): a scalable and interpretable approach for large-scale physics-based problems
Jiachen Guo, Xiaoyu Xie, Chanwook Park et al.
Interpreting CLIP with Hierarchical Sparse Autoencoders
Vladimir Zaigrajew, Hubert Baniecki, Przemysław Biecek
Interpreting the Repeated Token Phenomenon in Large Language Models
Itay Yona, Ilia Shumailov, Jamie Hayes et al.
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces
ERIC EATON, Marcel Hussing, Michael Kearns et al.
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models
Hang Guo, Yawei Li, Tao Dai et al.
Introducing 3D Representation for Dense Volume-to-Volume Translation via Score Fusion
Xiyue Zhu, Dou Kwark, Ruike Zhu et al.
Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning
Changsheng Wang, Yihua Zhang, jinghan jia et al.
Invariant Deep Uplift Modeling for Incentive Assignment in Online Marketing via Probability of Necessity and Sufficiency
Zexu Sun, Qiyu Han, Hao Yang et al.
Inverse Bridge Matching Distillation
Nikita Gushchin, David Li, Daniil Selikhanovych et al.
Inverse Flow and Consistency Models
Yuchen Zhang, Jian Zhou
Inverse Optimization via Learning Feasible Regions
Ke Ren, Peyman Mohajerin Esfahani, Angelos Georghiou
Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
Idan Achituve, Hai Victor Habi, Amir Rosenfeld et al.
Inverse problems with experiment-guided AlphaFold
Sai Advaith Maddipatla, Nadav Bojan, Meital Bojan et al.
Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors
Jingyang Ke, Feiyang Wu, Jiyi Wang et al.