ICML Papers
5,975 papers found • Page 16 of 120
Discovering Latent Causal Graphs from Spatiotemporal Data
Kun Wang, Sumanth Varambally, Duncan Watson-Parris et al.
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning
Shurui Gui, Xiner Li, Shuiwang Ji
Discovering Spoofing Attempts on Language Model Watermarks
Thibaud Gloaguen, Nikola Jovanović, Robin Staab et al.
Discovering Symbolic Cognitive Models from Human and Animal Behavior
Pablo Samuel Castro, Nenad Tomasev, Ankit Anand et al.
Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension
Yijun Dong, Yicheng Li, Yunai Li et al.
Discrepancy Minimization in Input-Sparsity Time
Yichuan Deng, Xiaoyu Li, Zhao Song et al.
Discrete and Continuous Difference of Submodular Minimization
George Orfanides, Tim Hoheisel, Marwa El Halabi
Discrete Markov Probabilistic Models: An Improved Discrete Score-Based Framework with sharp convergence bounds under minimal assumptions
Le Tuyet Nhi PHAM, Dario Shariatian, Antonio Ocello et al.
Discrete Neural Algorithmic Reasoning
Gleb Rodionov, Liudmila Prokhorenkova
Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data
Siqi Guo, Ilgee Hong, Vicente Balmaseda et al.
Discriminative Policy Optimization for Token-Level Reward Models
Hongzhan Chen, Tao Yang, Shiping Gao et al.
Disentangled Graph Spectral Domain Adaptation
Liang Yang, Xin Chen, Jiaming Zhuo et al.
Disentangling and Integrating Relational and Sensory Information in Transformer Architectures
Awni Altabaa, John Lafferty
Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
Haoyang Li, Xin Wang, Xueling Zhu et al.
Disparate Conditional Prediction in Multiclass Classifiers
Sivan Sabato, Eran Treister, Elad Yom-Tov
Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
Yuefan Cao, Xiaoyu Li, Yingyu Liang et al.
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
Julius Von Rohrscheidt, Bastian Rieck
Distillation of Discrete Diffusion through Dimensional Correlations
Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi et al.
Distillation Scaling Laws
Dan Busbridge, Amitis Shidani, Floris Weers et al.
Distilling the Knowledge in Data Pruning
Emanuel Ben Baruch, Adam Botach, Igor Kviatkovsky et al.
DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs
Jongwoo Ko, Tianyi Chen, Sungnyun Kim et al.
Distinguishing Cause from Effect with Causal Velocity Models
Johnny Xi, Hugh Dance, Peter Orbanz et al.
Distributed Conformal Prediction via Message Passing
Haifeng Wen, Hong XING, Osvaldo Simeone
Distributed Differentially Private Data Analytics via Secure Sketching
Jakob Burkhardt, Hannah Keller, Claudio Orlandi et al.
Distributed Event-Based Learning via ADMM
Guner Dilsad ER, Sebastian Trimpe, Michael Muehlebach
Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal
Deheng Yuan, Tao Guo, Zhongyi Huang
Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning
Boyuan Wu, wang, Xianwei Lin et al.
Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold
Yilong Song, Peijin Li, Bin Gao et al.
Distributional Diffusion Models with Scoring Rules
Valentin De Bortoli, Alexandre Galashov, J Swaroop Guntupalli et al.
Distributionally Robust Active Learning for Gaussian Process Regression
Shion Takeno, Yoshito Okura, Yu Inatsu et al.
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping
Guangyi Liu, Suzan Iloglu, Michael Caldara et al.
Distributionally Robust Policy Learning under Concept Drifts
Jingyuan Wang, Zhimei Ren, Ruohan Zhan et al.
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
Yujin Oh, Pengfei Jin, Sangjoon Park et al.
DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation
Dongya Jia, Zhuo Chen, Jiawei Chen et al.
Diverging Preferences: When do Annotators Disagree and do Models Know?
Michael Zhang, Zhilin Wang, Jena Hwang et al.
Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
Nguyen Nhat Minh To, Paul Wilson, Viet Nguyen et al.
Diversified Flow Matching with Translation Identifiability
Sagar Shrestha, Xiao Fu
Diversifying Robot Locomotion Behaviors with Extrinsic Behavioral Curiosity
Zhenglin Wan, Xingrui Yu, David Bossens et al.
Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization
Michael S Yao, James Gee, Osbert Bastani
Divide and Conquer: Exploring Language-centric Tree Reasoning for Video Question-Answering
Zhaohe Liao, Jiangtong Li, Siyu Sun et al.
Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning
Zican Hu, Wei Liu, Xiaoye Qu et al.
Divide and Conquer: Learning Label Distribution with Subtasks
Haitao Wu, Weiwei Li, Xiuyi Jia
Diving into Self-Evolving Training for Multimodal Reasoning
Wei Liu, Junlong Li, Xiwen Zhang et al.
DLP: Dynamic Layerwise Pruning in Large Language Models
Yuli Chen, Bo Cheng, Jiale Han et al.
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
Alexander Bienstock, Ujjwal Kumar, Antigoni Polychroniadou
DMOSpeech: Direct Metric Optimization via Distilled Diffusion Model in Zero-Shot Speech Synthesis
Yinghao Li, Rithesh Kumar, Zeyu Jin
Do Bayesian Neural Networks Actually Behave Like Bayesian Models?
Gábor Pituk, Vik Shirvaikar, Tom Rainforth
DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning
Xiaolong Xu, Yibo Zhou, Haolong Xiang et al.
DocVXQA: Context-Aware Visual Explanations for Document Question Answering
Mohamed Ali Souibgui, Changkyu Choi, Andrey Barsky et al.
Does Data Scaling Lead to Visual Compositional Generalization?
Arnas Uselis, Andrea Dittadi, Seong Joon Oh