ICLR Papers
6,124 papers found • Page 110 of 123
Probabilistic Adaptation of Black-Box Text-to-Video Models
Sherry Yang, Yilun Du, Bo Dai et al.
Probabilistically Rewired Message-Passing Neural Networks
Chendi Qian, Andrei Manolache, Kareem Ahmed et al.
Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization
Amirhossein Vahidi, Simon Schosser, Lisa Wimmer et al.
Procedural Fairness Through Decoupling Objectionable Data Generating Components
Zeyu Tang, Jialu Wang, Yang Liu et al.
PROGRAM: PROtotype GRAph Model based Pseudo-Label Learning for Test-Time Adaptation
Haopeng Sun, Lumin Xu, Sheng Jin et al.
Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts
Xinhua Cheng, Tianyu Yang, Jianan Wang et al.
Progressive Fourier Neural Representation for Sequential Video Compilation
Haeyong Kang, Jaehong Yoon, DaHyun Kim et al.
Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features
Annie Chen, Yoonho Lee, Amrith Setlur et al.
Prometheus: Inducing Fine-Grained Evaluation Capability in Language Models
Seungone Kim, Jamin Shin, yejin cho et al.
PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
Xinyuan Wang, Chenxi Li, Zhen Wang et al.
Prompt Gradient Projection for Continual Learning
Jingyang Qiao, Zhizhong Zhang, Xin Tan et al.
Prompt Learning with Quaternion Networks
Boya Shi, Zhengqin Xu, Shuai Jia et al.
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Thomas Zollo, Todd Morrill, Zhun Deng et al.
PromptTTS 2: Describing and Generating Voices with Text Prompt
Yichong Leng, ZHifang Guo, Kai Shen et al.
Proper Laplacian Representation Learning
Diego Gomez, Michael Bowling, Marlos C. Machado
Protein Discovery with Discrete Walk-Jump Sampling
Nathan Frey, Dan Berenberg, Karina Zadorozhny et al.
Protein-ligand binding representation learning from fine-grained interactions
Shikun Feng, Minghao Li, Yinjun JIA et al.
Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models
Zhilin Huang, Ling Yang, Xiangxin Zhou et al.
Protein Multimer Structure Prediction via Prompt Learning
Ziqi Gao, Xiangguo SUN, Zijing Liu et al.
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction
Yilan Zhang, Yingxue XU, Jianqi Chen et al.
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq, Qingfeng Lan, Pan Xu et al.
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes
Ruiquan Huang, Yuan Cheng, Jing Yang et al.
Provable Compositional Generalization for Object-Centric Learning
Thaddäus Wiedemer, Jack Brady, Alexander Panfilov et al.
Provable Memory Efficient Self-Play Algorithm for Model-free Reinforcement Learning
Na Li, Yuchen Jiao, Hangguan Shan et al.
Provable Offline Preference-Based Reinforcement Learning
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus et al.
Provable Reward-Agnostic Preference-Based Reinforcement Learning
Wenhao Zhan, Masatoshi Uehara, Wen Sun et al.
Provable Robust Watermarking for AI-Generated Text
Xuandong Zhao, Prabhanjan Ananth, Lei Li et al.
Provably Efficient CVaR RL in Low-rank MDPs
Yulai Zhao, Wenhao Zhan, Xiaoyan Hu et al.
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
Yu Chen, Yihan Du, Pihe Hu et al.
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations
Ruiquan Huang, Yingbin Liang, Jing Yang
Provably Robust Conformal Prediction with Improved Efficiency
Ge Yan, Yaniv Romano, Tsui-Wei Weng
Proving Test Set Contamination in Black-Box Language Models
Yonatan Oren, Nicole Meister, Niladri Chatterji et al.
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Sumeet Batra, Bryon Tjanaka, Matthew Fontaine et al.
Pseudo-Generalized Dynamic View Synthesis from a Video
Xiaoming Zhao, R Colburn, Fangchang Ma et al.
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
Hangting Ye, Wei Fan, Xiaozhuang Song et al.
PubDef: Defending Against Transfer Attacks From Public Models
Chawin Sitawarin, Jaewon Chang, David Huang et al.
Pushing Boundaries: Mixup's Influence on Neural Collapse
Quinn Fisher, Haoming Meng, Vardan Papyan
Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning
Ted Zadouri, Ahmet Üstün, Arash Ahmadian et al.
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Yuhui Xu, Lingxi Xie, Xiaotao Gu et al.
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
Haoning Wu, Zicheng Zhang, Erli Zhang et al.
QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
Jing Liu, Ruihao Gong, Xiuying Wei et al.
Quadratic models for understanding catapult dynamics of neural networks
Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan et al.
Quality-Diversity through AI Feedback
Herbie Bradley, Andrew Dai, Hannah Teufel et al.
Quantifying and Enhancing Multi-modal Robustness with Modality Preference
Zequn Yang, Yake Wei, Ce Liang et al.
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
Melanie Sclar, Yejin Choi, Yulia Tsvetkov et al.
Quantifying Network Similarity using Graph Cumulants
Gecia Bravo-Hermsdorff, Lee M. Gunderson, Pierre-André Maugis et al.
Quantifying the Plausibility of Context Reliance in Neural Machine Translation
Gabriele Sarti, Grzegorz Chrupała, Malvina Nissim et al.
Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification
Joar Skalse, Alessandro Abate
Quasi-Monte Carlo for 3D Sliced Wasserstein
Khai Nguyen, Nicola Bariletto, Nhat Ho
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
Hao Sun, Alihan Hüyük, Mihaela van der Schaar