← Authors

Cho-jui Hsieh

186 papers · 2008–2025 · 16 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer πŸƒ Academic Marathon (17) 🌍 Conference Polyglot (16) πŸ—ΊοΈ Taxonomy Completionist (25) 🐝 Cross-Pollinator (14) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🏠 Conference Loyalist (50) 🌟 Keyword Trendsetter Combo (4) 🀝 Dynamic Duo (33) πŸ‘‘ Triple Crown 🌱 Topic Pioneer πŸ† Keyword Champion (2) πŸ† Grand Slam πŸ”¬ Deep Specialist (51) πŸ—ƒοΈ Keyword Collector (101) πŸ”₯ Unstoppable (18) πŸš€ Conference Pioneer ⚑ Prolific Year (18) ❓ The Questioner (9) πŸ“ˆ Trend Setter πŸ’Ž Century Club (186)

Conferences

NIPS (50) ICLR (33) ICML (28) ACL (14) JMLR (10) AAAI (9) EMNLP (9) AISTATS (6) CVPR (5) ECCV (5) NAACL (5) ICCV (4) IJCAI (3) IJCNLP (3) CONLL (1) UAI (1)

Top co-authors

Huan Zhang (33) Minhao Cheng (25) Kai-Wei Chang (22) Inderjit S Dhillon (21) Si Si (20) Ruochen Wang (19) Hsiang-Fu Yu (16) Inderjit S. Dhillon (14) Inderjit Dhillon (12) Xiangning Chen (12)

Research topics

Differential Privacy (1)

Keywords

adversarial robustness (20) adversarial attack (17) adversarial example (13) coordinate descent (9) matrix factorization (9) adversarial training (8) neural network (8) neural network verification (8) low-rank approximation (8) convex optimization (7) text classification (7) large language model (6) model compression (6) neural network optimization (5) recommender system (5) adversarial defense (5) adversarial perturbation (5) linear convergence (5) support vector machine (5) sparse inverse covariance (4)

Papers

The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise ICLR 2025
Large Language Models are Interpretable Learners ICLR 2025
SeedLoRA: A Fusion Approach to Efficient LLM Fine-Tuning ICML 2025
An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning NAACL 2025
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review ACL 2025
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review CONLL 2025
OR-Bench: An Over-Refusal Benchmark for Large Language Models ICML 2025
QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models EMNLP 2025
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization ICLR 2025
Is Your Multimodal Language Model Oversensitive to Safe Queries? ICLR 2025
Automatic Engineering of Long Prompts ACL 2024
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering ACL 2024
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding ICLR 2024
Combining Axes Preconditioners through Kronecker Approximation for Deep Learning ICLR 2024
Two-stage LLM Fine-tuning with Less Specialization and More Generalization ICLR 2024
Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns? EMNLP 2024
DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLMs Jailbreakers EMNLP 2024
UNICORN: A Unified Causal Video-Oriented Language-Modeling Framework for Temporal Video-Language Tasks EMNLP 2024
When and How do negative prompts take effect? ECCV 2024
Defending LLMs against Jailbreaking Attacks via Backtranslation ACL 2024
Low-rank Matrix Bandits with Heavy-tailed Rewards UAI 2024
Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation ICML 2024
On Discrete Prompt Optimization for Diffusion Models ICML 2024
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts ICML 2024
Ameliorate Spurious Correlations in Dataset Condensation ICML 2024
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning ICML 2024
A Computationally Efficient Sparsified Online Newton Method NIPS 2023
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? NIPS 2023
Symbolic Discovery of Optimization Algorithms NIPS 2023
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning CVPR 2023
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation ICML 2023
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory ICML 2023
Representer Point Selection for Explaining Regularized High-dimensional Models ICML 2023
Training Meta-Surrogate Model for Transferable Adversarial Attack AAAI 2023
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract) AAAI 2023
Universality and Limitations of Prompt Tuning NIPS 2023
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data NIPS 2023
Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization NIPS 2023
Serving Graph Compression for Graph Neural Networks ICLR 2023
Towards Robustness Certification Against Universal Perturbations ICLR 2023
Concept Gradient: Concept-based Interpretation Without Linear Assumption ICLR 2023
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories ICLR 2023
Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations ACL 2023
Robust Lipschitz Bandits to Adversarial Corruptions NIPS 2023
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation EMNLP 2022
DC-BENCH: Dataset Condensation Benchmark NIPS 2022
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms NIPS 2022
General Cutting Planes for Bound-Propagation-Based Neural Network Verification NIPS 2022
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation NIPS 2022
Are AlphaZero-like Agents Robust to Adversarial Perturbations? NIPS 2022
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces NIPS 2022
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems NIPS 2022
Random Sharpness-Aware Minimization NIPS 2022
Efficient Non-Parametric Optimizer Search for Diverse Tasks NIPS 2022
On the Sensitivity and Stability of Model Interpretations in NLP ACL 2022
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples ACL 2022
Improving the Adversarial Robustness of NLP Models by Information Bottleneck ACL 2022
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks AISTATS 2022
Towards Efficient and Scalable Sharpness-Aware Minimization CVPR 2022
Learning to Learn with Smooth Regularization ECCV 2022
Weight Perturbation as Defense against Adversarial Word Substitutions EMNLP 2022
On the Convergence of Certified Robust Training with Interval Bound Propagation ICLR 2022
Generalizing Few-Shot NAS with Gradient Matching ICLR 2022
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction ICLR 2022
Concurrent Adversarial Learning for Large-Batch Training ICLR 2022
Learning to Schedule Learning rate with Graph Neural Networks ICLR 2022
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations ICLR 2022
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks ICML 2022
CAT: Customized Adversarial Training for Improved Robustness IJCAI 2022
Extreme Zero-Shot Learning for Extreme Text Classification NAACL 2022
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble ACL 2021
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble IJCNLP 2021
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization ICML 2021
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification NIPS 2021
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation NAACL 2021
Robust and Accurate Object Detection via Adversarial Learning CVPR 2021
Label Disentanglement in Partition-based Extreme Multilabel Classification NIPS 2021
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification NIPS 2021
DRONE: Data-aware Low-rank Compression for Large NLP Models NIPS 2021
On the Transferability of Adversarial Attacks against Neural Text Classifier EMNLP 2021
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution EMNLP 2021
Multi-Proxy Wasserstein Classifier for Image Classification AAAI 2021
Fast Certified Robust Training with Short Warmup NIPS 2021
RandomRooms: Unsupervised Pre-Training From Synthetic Shapes and Randomized Layouts for 3D Object Detection ICCV 2021
Towards Robustness of Deep Neural Networks via Regularization ICCV 2021
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving ICCV 2021
Self-Progressing Robust Training AAAI 2021
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling AISTATS 2021
Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search AAAI 2021
Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding NIPS 2021
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary ICLR 2021
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers ICLR 2021
DrNAS: Dirichlet Neural Architecture Search ICLR 2021
Evaluations and Methods for Explanation through Robustness Analysis ICLR 2021
Rethinking Architecture Selection in Differentiable NAS ICLR 2021
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations NIPS 2020
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data JMLR 2020
Provably Robust Metric Learning NIPS 2020
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data NIPS 2020
An Efficient Adversarial Attack for Tree Ensembles NIPS 2020
Multi-Stage Influence Function NIPS 2020
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond NIPS 2020
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework CVPR 2020
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius ICLR 2020
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples ACL 2020
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes ICLR 2020
What Does BERT with Vision Look At? ACL 2020
On Lp-norm Robustness of Ensemble Decision Stumps and Trees ICML 2020
Learning to Encode Position for Transformer with Continuous Dynamical Model ICML 2020
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization ICML 2020
Improved Adversarial Training via Learned Optimizer ECCV 2020
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation ECCV 2020
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples AAAI 2020
Robustness Verification for Transformers ICLR 2020
Towards Stable and Efficient Training of Verifiably Robust Neural Networks ICLR 2020
Learning to Learn by Zeroth-Order Oracle ICLR 2020
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering AISTATS 2020
ML-LOO: Detecting Adversarial Examples with Feature Attribution AAAI 2020
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack ICLR 2020
AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks AAAI 2019
Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables AISTATS 2019
A Fast Sampling Algorithm for Maximum Inner Product Search AISTATS 2019
Rob-GAN: Generator, Discriminator, and Adversarial Attacker CVPR 2019
MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model EMNLP 2019
Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks ICCV 2019
The Limitations of Adversarial Training and the Blind-Spot Attack ICLR 2019
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks ICLR 2019
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach ICLR 2019
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network ICLR 2019
Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent NAACL 2019
Robustness Verification of Tree-based Models NIPS 2019
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning NIPS 2019
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers NIPS 2019
Convergence of Adversarial Training in Overparametrized Neural Networks NIPS 2019
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks NIPS 2019
Robust Decision Trees Against Adversarial Examples ICML 2019
MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model IJCNLP 2019
On the Robustness of Self-Attentive Models ACL 2019
RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications AAAI 2019
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning ACL 2018
Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations JMLR 2018
Extreme Learning to Rank via Low Rank Assumption ICML 2018
Fast Variance Reduction Method with Stochastic Batch Size ICML 2018
Towards Fast Computation of Certified Robustness for ReLU Networks ICML 2018
SQL-Rank: A Listwise Approach to Collaborative Ranking ICML 2018
Learning from Group Comparisons: Exploiting Higher Order Interactions NIPS 2018
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking NIPS 2018
Efficient Neural Network Robustness Certification with General Activation Functions NIPS 2018
Learning Word Embeddings for Low-Resource Languages by PU Learning NAACL 2018
Towards Robust Neural Networks via Random Self-ensemble ECCV 2018
Distributed Primal-Dual Optimization for Non-uniformly Distributed Data IJCAI 2018
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach ICLR 2018
Rank Aggregation and Prediction with Item Features AISTATS 2017
Scalable Demand-Aware Recommendation NIPS 2017
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent NIPS 2017
Memory Efficient Kernel Approximation JMLR 2017
A Greedy Approach for Budgeted Maximum Inner Product Search NIPS 2017
Improved Bounded Matrix Completion for Large-Scale Recommender Systems IJCAI 2017
Gradient Boosted Decision Trees for High Dimensional Sparse Output ICML 2017
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order NIPS 2016
Computationally Efficient NystrΓΆm Approximation using Fast Transforms ICML 2016
Asynchronous Parallel Greedy Coordinate Descent NIPS 2016
Robust Principal Component Analysis with Side Information ICML 2016
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent NIPS 2015
PU Learning for Matrix Completion ICML 2015
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent ICML 2015
Matrix Completion with Noisy Side Information NIPS 2015
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models NIPS 2014
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings NIPS 2014
Fast Prediction for Large-Scale Kernel Machines NIPS 2014
QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation JMLR 2014
Prediction and Clustering in Signed Networks: A Local to Global Perspective JMLR 2014
A Divide-and-Conquer Solver for Kernel Support Vector Machines ICML 2014
Nuclear Norm Minimization via Active Subspace Selection ICML 2014
Memory Efficient Kernel Approximation ICML 2014
Large Scale Distributed Sparse Precision Estimation NIPS 2013
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables NIPS 2013
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation NIPS 2012
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation NIPS 2011
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification JMLR 2010
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM JMLR 2010
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models JMLR 2010
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy ACL 2009
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy IJCNLP 2009
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines JMLR 2008
LIBLINEAR: A Library for Large Linear Classification JMLR 2008