Papers
Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning
Jiaqi Wang, Chenxu Zhao, Lingjuan Lyu et al.
Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds
Noémie Jaquier, Leonel Rozo, Miguel González-Duque et al.
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel
Uri Gadot, Kaixin Wang, Navdeep Kumar et al.
Building Socially-Equitable Public Models
Yejia Liu, Jianyi Yang, Pengfei Li et al.
BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges
Hoyong Choi, Nohyun Ki, Hye Won Chung
ByMI: Byzantine Machine Identification with False Discovery Rate Control
Chengde Qian, Mengyuan Wang, Haojie Ren et al.
By Tying Embeddings You Are Assuming the Distributional Hypothesis
Francesco Bertolotti, Walter Cazzola
Byzantine Resilient and Fast Federated Few-Shot Learning
Ankit Pratap Singh, Namrata Vaswani
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui et al.
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Yair Schiff, Chia Hsiang Kao, Aaron Gokaslan et al.
Calibration Bottleneck: Over-compressed Representations are Less Calibratable
Deng-Bao Wang, Min-Ling Zhang
CaM: Cache Merging for Memory-efficient LLMs Inference
Yuxin Zhang, Yuxuan Du, Gen Luo et al.
Can a Few Decide for Many? The Metric Distortion of Sortition
Ioannis Caragiannis, Evi Micha, Jannik Peters
Can AI Assistants Know What They Don’t Know?
Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu et al.
Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data
Jiahan Zhang, Qi Wei, Feng Liu et al.
Can Gaussian Sketching Converge Faster on a Preconditioned Landscape?
Yilong Wang, Haishan Ye, Guang Dai et al.
Can Implicit Bias Imply Adversarial Robustness?
Hancheng Min, Rene Vidal
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi et al.
Can Machines Learn the True Probabilities?
Jinsook Kim
Can Mamba Learn How To Learn? A Comparative Study on In-Context Learning Tasks
Jongho Park, Jaeseung Park, Zheyang Xiong et al.
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
Wu Lin, Felix Dangel, Runa Eschenhagen et al.
CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources
Sikha Pentyala, Mayana Pereira, Martine De Cock
CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model
Milong Ren, Tian Zhu, Haicang Zhang
Careful with that Scalpel: Improving Gradient Surgery with an EMA
Yu-Guan Hsieh, James Thornton, Eugene Ndiaye et al.
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
Guangyi Chen, Yifan Shen, Zhenhao Chen et al.