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Bin Gao

16 papers hereh-index 458 citations16 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author7
  • middle author8
  • last author1

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • math.OC9
  • math.NA4
  • cs.ET1
  • cs.LG1
  • quant-ph1
same name
  • Bin Gao — 20 papers, h 13
  • Bin Gao — 6 papers, h 3
  • Bin Gao — 5 papers, h 24
  • Bin Gao — 5 papers, h 6
  • Bin Gao — 2 papers, h 2
  • Bin Gao — 2 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
collaborators
Showing 2024 · math.OCShow all

4 papers · 2 filters

math.OC2024

Desingularization of bounded-rank tensor sets

Bin Gao, Renfeng Peng, Ya-xiang Yuan

Low-rank tensors appear to be prosperous in many applications. However, the sets of bounded-rank tensors are non-smooth and non-convex algebraic varieties, rendering the low-rank o…

math.OC2024

Symplectic Stiefel manifold: tractable metrics, second-order geometry and Newton's methods

Bin Gao, Nguyen Thanh Son, Tatjana Stykel

Optimization under the symplecticity constraint is an approach for solving various problems in quantum physics and scientific computing. Building on the results that this optimizat…

math.OC2024

Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity

Yan Yang, Bin Gao, Ya-xiang Yuan

Bilevel reinforcement learning (RL), which features intertwined two-level problems, has attracted growing interest recently. The inherent non-convexity of the lower-level RL proble…

math.OC2024

LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspace

Yan Yang, Bin Gao, Ya-xiang Yuan

Bilevel optimization, with broad applications in machine learning, has an intricate hierarchical structure. Gradient-based methods have emerged as a common approach to large-scale…

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