ICML Poster Papers
5,104 papers found • Page 8 of 103
Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models
Alina Shutova, Vladimir Malinovskii, Vage Egiazarian et al.
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention
Han Li, Fei Liu, Zhi Zheng et al.
CAD-Editor: A Locate-then-Infill Framework with Automated Training Data Synthesis for Text-Based CAD Editing
Yu Yuan, Shizhao Sun, Qi Liu et al.
Calibrated Language Models and How to Find Them with Label Smoothing
Jerry Huang, Peng Lu, QIUHAO Zeng
Calibrated Physics-Informed Uncertainty Quantification
Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi et al.
Calibrated Value-Aware Model Learning with Probabilistic Environment Models
Claas Voelcker, Anastasiia Pedan, Arash Ahmadian et al.
Calibrating Video Watch-time Predictions with Credible Prototype Alignment
Chao, Shisong Tang, Fan Li et al.
CALM: Consensus-Aware Localized Merging for Multi-Task Learning
Kunda Yan, Min Zhang, Sen Cui et al.
Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
Yuankai Luo, Lei Shi, Xiao-Ming Wu
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression
Peijie Dong, Zhenheng Tang, Xiang Liu et al.
Can DBNNs Robust to Environmental Noise for Resource-constrained Scenarios?
Wendong Zheng, Junyang Chen, Husheng Guo et al.
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?
Yujin Han, Andi Han, Wei Huang et al.
Can Large Language Models Understand Intermediate Representations in Compilers?
Hailong Jiang, Jianfeng Zhu, Yao Wan et al.
CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning
Xuankun Rong, Jianshu Zhang, Kun He et al.
Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs
Haoming Yang, Ke Ma, Xiaojun Jia et al.
Canonical Rank Adaptation: An Efficient Fine-Tuning Strategy for Vision Transformers
Lokesh Veeramacheneni, Moritz Wolter, Hilde Kuehne et al.
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective
Jiawei Huang, Bingcong Li, Christoph Dann et al.
Can Transformers Learn Full Bayesian Inference in Context?
Arik Reuter, Tim G. J. Rudner, Vincent Fortuin et al.
Can Transformers Reason Logically? A Study in SAT Solving
Leyan Pan, Vijay Ganesh, Jacob Abernethy et al.
Can We Predict Performance of Large Models across Vision-Language Tasks?
Qinyu Zhao, Ming Xu, Kartik Gupta et al.
Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy
Haoqi Wu, Wei Dai, Wang Li et al.
CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models
Guangzhi Sun, Xiao Zhan, Shutong Feng et al.
Catching Two Birds with One Stone: Reward Shaping with Dual Random Networks for Balancing Exploration and Exploitation
Haozhe Ma, Fangling Li, Jing Lim et al.
CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models
Sen Peng, Mingyue Wang, Jianfei He et al.
Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics
Tyler Kastner, Mark Rowland, Yunhao Tang et al.
Categorical Schrödinger Bridge Matching
Grigoriy Ksenofontov, Aleksandr Korotin
CateKV: On Sequential Consistency for Long-Context LLM Inference Acceleration
Haoyun Jiang, Haolin li, jianwei zhang et al.
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging
Wenju Sun, Qingyong Li, Yangliao Geng et al.
Causal Abstraction Inference under Lossy Representations
Kevin Xia, Elias Bareinboim
Causal Abstraction Learning based on the Semantic Embedding Principle
Gabriele DAcunto, Fabio Massimo Zennaro, Yorgos Felekis et al.
Causal Discovery from Conditionally Stationary Time Series
Carles Balsells-Rodas, Xavier Sumba, Tanmayee Narendra et al.
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar et al.
Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning
Minqi Yu, Jinduo Liu, Junzhong Ji
Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection
HyunGi Kim, Jisoo Mok, Dong Jun Lee et al.
Causality Inspired Federated Learning for OOD Generalization
Jiayuan Zhang, Xuefeng Liu, Jianwei Niu et al.
Causal Logistic Bandits with Counterfactual Fairness Constraints
Jiajun Chen, Jin Tian, Chris Quinn
Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel
Carlota Parés Morlans, Michelle Yi, Claire Chen et al.
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
Zebin Wang, Menghan Lin, Bolin Shen et al.
CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Yuhui Zhang, Yuchang Su, Chenyu Wang et al.
Censor Dependent Variational Inference
Chuanhui Liu, Xiao Wang
CERTAIN: Context Uncertainty-aware One-Shot Adaptation for Context-based Offline Meta Reinforcement Learning
Hongtu Zhou, Ruiling Yang, Yakun Zhu et al.
Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts
Amir Najafi, Samin Mahdizadeh Sani, Farzan Farnia
Certification for Differentially Private Prediction in Gradient-Based Training
Matthew Wicker, Philip Sosnin, Igor Shilov et al.
Certified Unlearning for Neural Networks
Anastasiia Koloskova, Youssef Allouah, Animesh Jha et al.
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
Junbo Yin, Chao Zha, Wenjia He et al.
Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
Wanyun Xie, Francesco Tonin, Volkan Cevher
Channel Normalization for Time Series Channel Identification
Seunghan Lee, Taeyoung Park, Kibok Lee
Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction
Yi He, Yiming Yang, Xiaoyuan Cheng et al.
CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation
Minghao Fu, Guo-Hua Wang, Liangfu Cao et al.
Chip Placement with Diffusion Models
Vint Lee, Minh Nguyen, Leena Elzeiny et al.