Papers
Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation
Yiming Qin, Zhu Xu, Yang Liu
Applying the Character-Role Narrative Framework with LLMs to Investigate Environmental Narratives in Scientific Editorials and Tweets
Francesca Grasso, Stefano Locci, Manfred Stede
Applying Transformer Architectures to Detect Cynical Comments in Spanish Social Media
Samuel Gonzalez-Lopez, Steven Bethard, Rogelio Platt-Molina et al.
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
Approximate Bayesian Inference via Bitstring Representations
Aleksanteri Sladek, Martin Trapp, Arno Solin
Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning
Zengxia Guo, Bohui An, Zhongqi Lu
Approximate Differential Privacy of the $\ell_2$ Mechanism
Matthew Joseph, Alex Kulesza, Alexander Yu
Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment
Yuang Cai, Yuyu Yuan, Jinsheng Shi et al.
Approximate Equivariance in Reinforcement Learning
Jung Yeon Park, Sujay Bhatt, Sihan Zeng et al.
Approximate Forest Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees
Nate Veldt, Thomas Stanley, Benjamin W Priest et al.
Approximate Global Convergence of Independent Learning in Multi-Agent Systems
Ruiyang Jin, Zaiwei Chen, Yiheng Lin et al.
Approximate information maximization for bandit games
Alex Barbier Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson et al.
Approximate Lifted Model Construction
Malte Luttermann, Jan Speller, Marcel Gehrke et al.
Approximately Correct Label Distribution Learning
Weiwei Li, Haitao Wu, Yunan Lu et al.
Approximately EFX and fPO Allocations for Bivalued Chores
Zehan Lin, Xiaowei Wu, Shengwei Zhou
Approximate State Abstraction for Markov Games
Hiroki Ishibashi, Kenshi Abe, Atsushi Iwasaki
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with $O(\sqrtT)$ Regret
Yeoneung Kim, Gihun Kim, Jiwhan Park et al.
Approximate Verification of Strategic Abilities under Imperfect Information Using Local Models
Damian Kurpiewski, Wojciech Jamroga, Yan Kim
Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence
Dharmesh Tailor, Alvaro Correia, Eric Nalisnick et al.
Approximating Latent Manifolds in Neural Networks via Vanishing Ideals
Nico Pelleriti, Max Zimmer, Elias Samuel Wirth et al.
Approximating Metric Magnitude of Point Sets
Rayna Andreeva, James Ward, Primoz Skraba et al.
Approximating Optimal Labelings for Temporal Connectivity
Daniele Carnevale, Gianlorenzo D'Angelo, Martin Olsen
Approximating the Total Variation Distance between Gaussians
Arnab Bhattacharyya, Weiming Feng, Piyush Srivastava
Approximating the total variation distance between spin systems
Weiming Feng, Hongyang Liu, Minji Yang