12 citations · 18 across the 11 of their papers we have counts for
11 papers
DeepMTL2R: A Library for Deep Multi-task Learning to Rank
Chaosheng Dong, Peiyao Xiao, Yijia Wang +1
This paper presents DeepMTL2R, an open-source deep learning framework for Multi-task Learning to Rank (MTL2R), where multiple relevance criteria must be optimized simultaneously. D…
Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs
Hao Ban, Kaiyi Ji
Large language models are often adapted using parameter-efficient techniques such as Low-Rank Adaptation (LoRA), formulated as , where is the pre-trained para…
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
Hao Ban, Gokul Ram Subramani, Kaiyi Ji
Multi-task learning (MTL) enables a joint model to capture commonalities across multiple tasks, reducing computation costs and improving data efficiency. However, a major challenge…
LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control
Peiyao Xiao, Chaosheng Dong, Shaofeng Zou +1
Multi-task learning (MTL) has been widely adopted for its ability to simultaneously learn multiple tasks. While existing gradient manipulation methods often yield more balanced sol…
ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction
Jianan Nie, Peiyao Xiao, Kaiyi Ji +1
Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science. Unlike molecules, crystal structures exhibit infinite periodic a…
Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis
Yifan Yang, Hao Ban, Minhui Huang +2
Bilevel optimization has recently attracted considerable attention due to its abundant applications in machine learning problems. However, existing methods rely on prior knowledge…
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
Chen Wang, Kaiyi Ji, Junyi Geng +16
Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from gene…
MGDA Converges under Generalized Smoothness, Provably
Qi Zhang, Peiyao Xiao, Shaofeng Zou +1
Multi-objective optimization (MOO) is receiving more attention in various fields such as multi-task learning. Recent works provide some effective algorithms with theoretical analys…
Theoretical Study of Conflict-Avoidant Multi-Objective Reinforcement Learning
Yudan Wang, Peiyao Xiao, Hao Ban +2
Multi-task reinforcement learning (MTRL) has shown great promise in many real-world applications. Existing MTRL algorithms often aim to learn a policy that optimizes individual obj…
Fair Resource Allocation in Multi-Task Learning
Hao Ban, Kaiyi Ji
By jointly learning multiple tasks, multi-task learning (MTL) can leverage the shared knowledge across tasks, resulting in improved data efficiency and generalization performance.…
Achieving Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
Yifan Yang, Peiyao Xiao, Kaiyi Ji
In this paper, we revisit the bilevel optimization problem, in which the upper-level objective function is generally nonconvex and the lower-level objective function is strongly co…