PEML co-optimizes continuous prompts and low-rank adaptations to deliver up to 6.67% average accuracy gains over existing multi-task PEFT methods on GLUE, SuperGLUE, and other benchmarks.
SIAM Journal on Optimization , volume =
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DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.
CT-AGD accelerates first-order optimization in deep learning by using finite-difference curvature estimates and noise-mitigation heuristics, achieving equivalent accuracy with 33% fewer training epochs and overhead comparable to Adam.
citing papers explorer
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PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts
PEML co-optimizes continuous prompts and low-rank adaptations to deliver up to 6.67% average accuracy gains over existing multi-task PEFT methods on GLUE, SuperGLUE, and other benchmarks.
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Distributionally Robust Multi-Objective Optimization
DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.
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Accelerated Gradient Descent for Faster Convergence with Minimal Overhead
CT-AGD accelerates first-order optimization in deep learning by using finite-difference curvature estimates and noise-mitigation heuristics, achieving equivalent accuracy with 33% fewer training epochs and overhead comparable to Adam.