Jiancong Xiao
Assistant Professor
Works on statistical and learning-theoretic foundations of LLMs, including preference alignment, calibration and adversarial robustness.
Jiancong Xiao's lab at NUS works on trustworthy & safe ML, learning theory & optimization and interpretability, evaluation & safety of LMs. 15 papers since 2023, 12 at top venues, mostly ICML and ICLR. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 80%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 2 · collecting
- 5 papers2025 · T1 4
- 3 papers2024 · T1 3
- 3 papers2023 · T1 3
T1 venuesOtherVenues: ICML, ICLR, NeurIPS, COLT, JMLR
Research focuspaper titles that use each topic's words
- calibration3 papers
- Learning theory & optimization2 papers
- preference2 papers
- Trustworthy & safe ML0 papers
- Interpretability, evaluation & safety of LMs0 papers
Paper list
From the lab homepage.
20264 papers
- NeurIPS (T1)A Semantic-Sampling Framework for Evaluating Calibration in Open-Ended Question Answering
- JMLR (T1)Adversrial Rademacher Complexity of Deep Neural Networks
- Conference on Language ModelingBeyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration
- AoSStatistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium
20255 papers
- ICLR (T1)Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic
- ICLR (T1)Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Models Alignment
- JASAOn the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization
- ICLR (T1)Preserving Diversity in Supervised Fine-Tuning of Large Language Models
- ICML (T1)Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
20243 papers
- COLT (T1)Bridging the Gap: Rademacher Complexity in Robust and Standard Generalization
- NeurIPS (T1)Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity
- ICML (T1)Uniformly Stable Algorithms for Adversarial Training and Beyond
20233 papers
- ICML (T1)Improving Adversarial Training for Multiple Perturbations through the Lens of Uniform Stability
- ICML (T1)PAC-Bayesian Adversarially Robust Generalization Bounds for Deep Neural Networks
- NeurIPS (T1)PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NUSFall 2027
School of Computing PhD in Computer Science
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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Your notes
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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.
- Collected
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- The lab homepage
- Members
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- Alumni
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