Cong Fang
助理教授
Researches machine learning and optimization, including deep learning theory.
Cong Fang's lab at Peking works on learning theory & optimization. 20 papers since 2023, 20 at top venues, mostly NeurIPS and ICML. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 100%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 3 papers2026 · T1 3 · collecting
- 5 papers2025 · T1 5
- 8 papers2024 · T1 8
- 4 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, ICML, ICLR, COLT, TPAMI
Research focuspaper titles that use each topic's words
- Learning theory & optimization11 papers
- optimization6 papers
- intelligence science0 papers
- researches machine0 papers
- learning theory0 papers
Paper list
From DBLP.
20263 papers
- TAC (T1)CORE: Common Random Reconstruction for Distributed Optimization With Provable Lower Total Communication Costs (opens in a new tab)
- ICLR (T1)MVR: Multi-view Video Reward Shaping for Reinforcement Learning (opens in a new tab)
- ICLR (T1)Strong Correlations Induce Cause Only Predictions in Transformer Training (opens in a new tab)
20255 papers
- TPAMI (T1)Hessian-Aware Zeroth-Order Optimization (opens in a new tab)
- NeurIPS (T1)Improving Model Representation and Reducing KV Cache via Skip Connections with First Value Heads (opens in a new tab)
- ICML (T1)Learning Curves of Stochastic Gradient Descent in Kernel Regression (opens in a new tab)
- NeurIPS (T1)PaZO: Preconditioned Accelerated Zeroth-Order Optimization for Fine-Tuning LLMs (opens in a new tab)
- ICLR (T1)SEPARATE: A Simple Low-rank Projection for Gradient Compression in Modern Large-scale Model Training Process (opens in a new tab)
20248 papers
- TPAMI (T1)Designing Universally-Approximating Deep Neural Networks: A First-Order Optimization Approach (opens in a new tab)
- ICML (T1)End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations (opens in a new tab)
- NeurIPS (T1)Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition (opens in a new tab)
- JMLR (T1)PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium (opens in a new tab)
- ICML (T1)Quantum Algorithms and Lower Bounds for Finite-Sum Optimization (opens in a new tab)
- ICML (T1)Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective (opens in a new tab)
- NeurIPS (T1)Separation and Bias of Deep Equilibrium Models on Expressivity and Learning Dynamics (opens in a new tab)
- NeurIPS (T1)The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing (opens in a new tab)
20234 papers
- NeurIPS (T1)Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee (opens in a new tab)
- COLT (T1)On the Lower Bound of Minimizing Polyak-Łojasiewicz functions (opens in a new tab)
- NeurIPS (T1)Task-Robust Pre-Training for Worst-Case Downstream Adaptation (opens in a new tab)
- COLT (T1)Zeroth-order Optimization with Weak Dimension Dependency (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to PekingFall 2027
Computer Science PhD (international applicants)
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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- Members
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- Alumni
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