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  • CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal . . .
    Multi-task learning (MTL) aims to construct a joint model for multiple tasks by sharing a common representation across domains To achieve this goal, existing optimization-centric methods either balance task gradients or modify the shared architecture
  • CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal . . .
    Abstract Multi-task learning (MTL) aims to construct a joint model for multiple tasks by sharing a common representation across domains To achieve this goal, existing optimization-centric methods either balance task gradients or modify the shared architecture
  • CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal . . .
    Causal Orthogonal Representations for Multi-Task Learning (CORE-MTL), a causally motivated representation-centric framework that encourages a structured semantic-residual factorization of the shared representation, concentrating task-relevant structure in the semantic stream while relegating nuisance variation to the residual stream is proposed Multi-task learning (MTL) aims to construct a
  • CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal . . .
    The method draws from causal representation learning to formalize the problem Under a linear-Gaussian structural causal model where inputs mix independent semantic factors and nuisance residual factors through rotation, the authors prove that gradient-balancing methods like GradNorm, PCGrad, and MGDA face an irreducible out-of-distribution
  • ICLR 2026 Conference | OpenReview
    Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks But what is your honest answer? Aiding LLM-judges with honest alternatives using steering vectors Plans for Code Release?
  • NeurIPS 2025 Papers
    San Diego Sydney Atlanta Mexico City Select Year: (2025)
  • ICLR 2026 Papers
    Can Language Models Discover Scaling Laws? Amortized Inference of Causal Models via Conditional Fixed-Point Iterations Inference-time scaling of diffusion models through classical search CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers TopoFormer: Topology Meets Attention for Graph Learning
  • Jiangmeng Lis Publications
    Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective Jiangmeng Li, Zehua Zang, Qirui Ji, Chuxiong Sun, Wenwen Qiang, Junge Zhang, Changwen Zheng, Fuchun Sun, and Hui Xiong
  • ICCV-2025-Papers README. md at main - GitHub
    Is Tracking really more challenging in First Person Egocentric Vision? Contribute to 52CV ICCV-2025-Papers development by creating an account on GitHub
  • Proceedings of Machine Learning Research | Proceedings of the 42nd . . .
    An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures Thibaut Boissin, Franck Mamalet, Thomas Fel, Agustin Martin Picard, Thomas Massena, Mathieu Serrurier; Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:4757-4790 [abs] [Download PDF] [OpenReview] [Software]





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