ICML Papers
5,975 papers found • Page 105 of 120
Practical Performance Guarantees for Pipelined DNN Inference
Aaron Archer, Matthew Fahrbach, Kuikui Liu et al.
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input
Andi Peng, Yuying Sun, Tianmin Shu et al.
Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms
Elvis Dohmatob, Meyer Scetbon
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
Haoyu Li, Shichang Zhang, Longwen Tang et al.
Predicting Dose-Response Curves with Deep Neural Networks
Pedro A. Campana, Paul Prasse, Tobias Scheffer
Predicting Lagrangian Multipliers for Mixed Integer Linear Programs
Francesco Demelas, Joseph Roux, Mathieu Lacroix et al.
Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg
Yi Feng, Georgios Piliouras, Xiao Wang
Prediction-powered Generalization of Causal Inferences
Ilker Demirel, Ahmed Alaa, Anthony Philippakis et al.
Predictive Coding beyond Correlations
Tommaso Salvatori, Luca Pinchetti, Amine M'Charrak et al.
Predictive Dynamic Fusion
Bing Cao, Yinan Xia, Yi Ding et al.
Predictive Linear Online Tracking for Unknown Targets
Anastasios Tsiamis, Aren Karapetyan, Yueshan Li et al.
Predictive Performance Comparison of Decision Policies Under Confounding
Luke Guerdan, Amanda Coston, Ken Holstein et al.
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
Fahim Tajwar, Anikait Singh, Archit Sharma et al.
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
Songtao Liu, Hanjun Dai, Yue Zhao et al.
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, xiyao wang et al.
Premise Order Matters in Reasoning with Large Language Models
Xinyun Chen, Ryan Chi, Xuezhi Wang et al.
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Charlie Hou, Akshat Shrivastava, Hongyuan Zhan et al.
Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains
Jiale Zhao, Wanru Zhuang, Jia Song et al.
Preventing Model Collapse in Gaussian Process Latent Variable Models
Ying Li, Zhidi Lin, Feng Yin et al.
Pricing with Contextual Elasticity and Heteroscedastic Valuation
Jianyu Xu, Yu-Xiang Wang
Principled Gradient-Based MCMC for Conditional Sampling of Text
Li Du, Afra Amini, Lucas Torroba Hennigen et al.
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
Han Shen, Zhuoran Yang, Tianyi Chen
Principled Preferential Bayesian Optimization
Wenjie Xu, Wenbin Wang, Yuning Jiang et al.
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
Shirin Shoushtari, JIAMING LIU, Edward Chandler et al.
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann et al.
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
Ruijie Zheng, Ching-An Cheng, Hal Daumé et al.
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna et al.
Privacy Attacks in Decentralized Learning
Abdellah El Mrini, Edwige Cyffers, Aurélien Bellet
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
Shanglun Feng, Florian Tramer
Privacy Preserving Adaptive Experiment Design
Jiachun Li, Kaining Shi, David Simchi-Levi
Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent
Konstantin Donhauser, Javier Abad, Neha Hulkund et al.
Privacy-Preserving Embedding via Look-up Table Evaluation with Fully Homomorphic Encryption
Jae-yun Kim, Saerom Park, Joohee Lee et al.
Privacy-Preserving Instructions for Aligning Large Language Models
Da Yu, Peter Kairouz, Sewoong Oh et al.
Privacy Profiles for Private Selection
Antti Koskela, Rachel Redberg, Yu-Xiang Wang
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems
Roie Reshef, Kfir Levy
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans et al.
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
Changyu Gao, Andrew Lowy, Xingyu Zhou et al.
Privately Learning Smooth Distributions on the Hypercube by Projections
Clément Lalanne, Sébastien Gadat
Private Truly-Everlasting Robust-Prediction
Uri Stemmer
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
Hilal Asi, Vitaly Feldman, Jelani Nelson et al.
Proactive Detection of Voice Cloning with Localized Watermarking
Robin San Roman, Pierre Fernandez, Hady Elsahar et al.
Proactive DP: A Multiple Target Optimization Framework for DP-SGD
Marten van Dijk, Nhuong Nguyen, Toan N. Nguyen et al.
Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
Hengyi Wang, Shiwei Tan, Hao Wang
Probabilistic Constrained Reinforcement Learning with Formal Interpretability
YANRAN WANG, QIUCHEN QIAN, David Boyle
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Yifan Chen, Mark Goldstein, Mengjian Hua et al.
Probabilistic Generating Circuits - Demystified
Sanyam Agarwal, Markus Bläser
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
Stephen Zhao, Rob Brekelmans, Alireza Makhzani et al.
Probabilistic Modeling of Interpersonal Coordination Processes
Paulo Soares, Adarsh Pyarelal, Meghavarshini Krishnaswamy et al.
Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
Kejing Lu, Chuan Xiao, Yoshiharu Ishikawa