Cho-Jui Hsieh
Associate Professor
Studies scalable optimization, adversarial robustness and verification of neural networks, and efficient training of large models.
Cho-Jui Hsieh's lab at UCLA works on trustworthy & safe ML, learning theory & optimization and efficient training & inference. 61 papers since 2023, 50 at top venues, mostly ICLR and NeurIPS. 13 PhD students. 18 alumni. Applications close .
At a glance
- papers, 2023–2026
- 16/ year
- at T1 venues
- 82%
- PhD students
- 13
- MS students
- 0
- alumni listed
- 18
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 16 papers2025 · T1 13
- 23 papers2024 · T1 18
- 22 papers2023 · T1 19
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, WWW, EMNLP
Research focuspaper titles that use each topic's words
- Learning theory & optimization11 papers
- Efficient training & inference10 papers
- Trustworthy & safe ML2 papers
- efficient training0 papers
- adversarial robustness0 papers
Paper list
From the lab homepage.
202516 papers
- CoNLLAccelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review
- Findings of NAACLAn Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning
- NeurIPS (T1)Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models
- ICLR (T1)Is Your Multimodal Language Model Oversensitive to Safe Queries?
- ICLR (T1)Large Language Models are Interpretable Learners
- ICLR (T1)LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
- KDD (T1)Matryoshka Model Learning for Improved Elastic Student Models
- TACASNeural Network Verification with Branch-and-Bound for General Nonlinearities Code Benchmark
- NeurIPS (T1)On the Loss of Context-awareness in General Instruction Fine-tuning
- ICML (T1)OR-Bench: An Over-Refusal Benchmark for Large Language Models
- EMNLP (T1)QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models
- ICML (T1)SeedLoRA: A Fusion Approach to Efficient LLM Fine-Tuning
202423 papers
- ICML (T1)Ameliorate Spurious Correlations in Dataset Condensation
- Findings of ACLAutomatic Engineering of Long Prompts
- ICLR (T1)Combining Axes Preconditioners through Kronecker Approximation for Deep Learning
- TMLR (T1)Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation
- Findings of ACLDefending LLMs against Jailbreaking Attacks via Backtranslation Code
- ICML (T1)dPOD: On Discrete Prompt Optimization for Diffusion Models
- Findings of EMNLPDrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers
- WWW (T1)Entity Disambiguation with Extreme Multi-label Ranking
- ICML (T1)Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning
- KDD (T1)Gandalf: Learning label-label correlations in extreme multi-label classification via label features
- UAI (T1)Low-rank Matrix Bandits with Heavy-tailed Rewards
- ICML (T1)Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation for Efficient Synthesis and Verification
202322 papers
- NeurIPS (T1)A Computationally Efficient Sparsified Online Newton Method
- NeurIPS (T1)Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization
- CIKM (T1)Build faster with less: A journey to accelerate sparse model building for semantic matching in product search
- ICLR (T1)Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories
- ICLR (T1)Concept Gradient: Concept-based Interpretation Without Linear Assumption
- NeurIPS (T1)Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
- In ACL-FindingsEnhancing Unsupervised Semantic Parsing with Distributed Contextual Representations
- CVPR (T1)FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
- WWW (T1)FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search
- In HotStorageNeSSA: Near-Storage Data Selection for Accelerated Machine Learning Training
- ICML (T1)PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
- ICML (T1)Representer Point Selection for Explaining Regularized High-dimensional Models
Where alumni went18 listed
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.
Similar labsnearest by research summary
- GBOxford
Giuseppe De Giacomo
- Reasoning & neuro-symbolic AI
- USUT Austin
Ray Mooney
- Multilingual, translation & linguistics
- USCMU
Yonatan Bisk
- Multimodal & grounded language
- CNTsinghua
Yuantao Gu
- Learning theory & optimization
- GBOxford
Ian Horrocks
- Reasoning & neuro-symbolic AI
- USGeorgia Tech
Jacob Abernethy
- Learning theory & optimization
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
UCLAcloses