Guang Cheng
Professor
Directs the Trustworthy AI Lab on generative data science, including synthetic tabular data for privacy-preserving data sharing.
Guang Cheng's lab at UCLA works on trustworthy & safe ML, learning theory & optimization and data management & big data. 51 papers since 2023, 26 at top venues, mostly ICML and TMLR. 9 PhD students and 5 master's students. 20 alumni, most often to Meta and Amazon. Applications close .
At a glance
- papers, 2023–2026
- 13/ year
- at T1 venues
- 51%
- PhD students
- 9
- MS students
- 5
- alumni listed
- 20
Papers by yearfrom the lab homepage, 2023–2026
- 22 papers2026 · T1 12 · collecting
- 12 papers2025 · T1 5
- 8 papers2024 · T1 4
- 9 papers2023 · T1 5
T1 venuesOtherVenues: ICML, TMLR, ICLR, JMLR, NeurIPS
Research focuspaper titles that use each topic's words
- Data management & big data14 papers
- data14 papers
- Learning theory & optimization9 papers
- Trustworthy & safe ML2 papers
- sharing0 papers
Paper list
From the lab homepage.
202622 papers
- ICLR (T1)``Noisier'’ Noise Contrastive Estimation is (Almost) Maximum Likelihood
- TMLR (T1)Alignment of Diffusion Model and Flow Matching for Text- to-Image Generation
- CVPR APAI Workshop, Best paper award!Authenticated Contradictions from Desynchronized Provenance and Watermarking
- JMLR (T1)Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing
- ICML WorkshopContext-Fractured Decomposition Attacks on Tool-Using LLM Agents: Exploiting Artifact Provenance Gaps.
- ICML (T1)Corrected Samplers for Discrete Flow Models
- ICLR (T1)Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching
- ICML (T1)Error Analysis of Discrete Flow with Generator Matching
- NeurIPS (T1)From Table to Cell: Attention for Better Reasoning with TABALIGN
- ICML (T1)Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System
- NeurIPS (T1)Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
- BiometrikaOptimal Watermark Generation under Type I and Type II Errors
202512 papers
- NeurIPS Lock-LLM WorkshopBreaking Distortion-free Watermarks in Large Language Models,
- ICML WorkshopBridging the Language Gap: Synthetic Voice Diversity via Latent Mixup for Equitable Speech Recognition
- ICLR (T1)CTSyn: A Foundational Model for Cross Tabular Data Generation
- JMLR (T1)Decentralized Sparse Linear Regression via Gradient-Tracking
- JMLR (T1)Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
- AISec'Ensembling Membership Inference Attacks Against Tabular Generative Models
- CIKM (T1)Exploring Reasoning-lnfused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval
- ICDE WorkshopGReaTER: Generate Realistic Tabular data after data Enhancement and Reduction
- JMLR (T1)Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
- JASARate-Optimal Rank Aggregation with Private Pairwise Rankings
- KDD WorkshopRisk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation.
- KDD WorkshopTowards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering
20248 papers
- KDD Workshop(2024) Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data
- Annual Review of Statistics and Its ApplicationA Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models
- AISTATS (T1)Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
- KDD WorkshopData Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models
- ACM ICAIF WorkshopDEREC-SIMPRO: unlock Language Model benefits to advance Synthesis in Data Clean Room
- AISTATS (T1)FairRR: Pre-Processing for Group Fairness through Randomized Response
- NeurIPS (T1)Transfer Learning for Diffusion Models
- ICML (T1)Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints
20239 papers
- NeurIPS WorkshopAutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing
- TMLR (T1)Binary Classification under Local Label Differential Privacy Using Randomized Response Mechanisms
- ACM International Conference on AI in Finance -- WorkshopImprove Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective
- ICML (T1)Improving Adversarial Robustness by Contrastive Guided Diffusion Process
- Journal of the American Statistical Association -- T&MOnline Regularization towards Always-Valid High-Dimensional Dynamic Pricing
- TMLR (T1)Optimal Learning Rates of Deep Convolutional Neural Networks: Additive Ridge Functions
- TMLR (T1)Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization
- Information and Inference: A Journal of the IMASparse Confidence Sets for Normal Mean Models
- ICLR (T1)Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms
Where alumni went20 listed, 69% to industry
Industry 11Academia 5
Most often to
- Meta
- Amazon
- OpenAI
Also to
- Assisant Professor at Chinese University Hong Kong-Shenzhen
- Assistant Prof. of Statistics and Computer Science & Engineering at Michigan State University
- Krannert School of Management
Apply to UCLAFall 2027
Computer Science PhD
- Round
- Single round (Dec 15)
- Opens
- —
- GRE
- optional
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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