Cho‐jui Hsieh
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Computer Science
Cho‐jui Hsieh's Degrees
- PhD Computer Science National Taiwan University
- Masters Computer Science National Taiwan University
- Bachelors Computer Science National Taiwan University
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(Suggest an Edit or Addition)Cho‐jui Hsieh's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- LIBLINEAR: A Library for Large Linear Classification (2008) (8130)
- ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models (2017) (1249)
- VisualBERT: A Simple and Performant Baseline for Vision and Language (2019) (1038)
- A dual coordinate descent method for large-scale linear SVM (2008) (987)
- Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent (2017) (773)
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (2019) (663)
- Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (2019) (577)
- Towards Fast Computation of Certified Robustness for ReLU Networks (2018) (577)
- EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples (2017) (498)
- Training and Testing Low-degree Polynomial Data Mappings via Linear SVM (2010) (491)
- Efficient Neural Network Robustness Certification with General Activation Functions (2018) (479)
- ImageNet Training in Minutes (2017) (365)
- Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach (2018) (348)
- Sparse inverse covariance matrix estimation using quadratic approximation (2011) (348)
- Towards Robust Neural Networks via Random Self-ensemble (2017) (329)
- Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems (2012) (290)
- AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks (2018) (287)
- Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach (2018) (277)
- A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification (2010) (257)
- Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines (2008) (238)
- Towards Stable and Efficient Training of Verifiably Robust Neural Networks (2019) (236)
- Fast coordinate descent methods with variable selection for non-negative matrix factorization (2011) (229)
- A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks (2019) (203)
- DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification (2021) (194)
- BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables (2013) (191)
- QUIC: quadratic approximation for sparse inverse covariance estimation (2014) (180)
- Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples (2018) (179)
- NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion (2013) (165)
- A sequential dual method for large scale multi-class linear svms (2008) (161)
- When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations (2021) (158)
- A Divide-and-Conquer Solver for Kernel Support Vector Machines (2013) (152)
- Large Linear Classification When Data Cannot Fit in Memory (2011) (149)
- Prediction and clustering in signed networks: a local to global perspective (2013) (148)
- Memory Efficient Kernel Approximation (2014) (146)
- Parallel matrix factorization for recommender systems (2014) (140)
- Low rank modeling of signed networks (2012) (139)
- Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond (2020) (132)
- PU Learning for Matrix Completion (2014) (131)
- Nuclear Norm Minimization via Active Subspace Selection (2014) (129)
- Stabilizing Differentiable Architecture Search via Perturbation-based Regularization (2020) (128)
- Sign-OPT: A Query-Efficient Hard-label Adversarial Attack (2019) (127)
- Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network (2018) (120)
- Matrix Completion with Noisy Side Information (2015) (115)
- MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius (2020) (110)
- Reducing BERT Pre-Training Time from 3 Days to 76 Minutes (2019) (109)
- PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent (2015) (107)
- The Limitations of Adversarial Training and the Blind-Spot Attack (2019) (104)
- SSE-PT: Sequential Recommendation Via Personalized Transformer (2020) (94)
- Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning (2017) (94)
- Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations (2020) (93)
- On the Adversarial Robustness of Vision Transformers (2021) (88)
- Gradient Boosted Decision Trees for High Dimensional Sparse Output (2017) (88)
- A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order (2016) (83)
- Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise (2019) (83)
- What Does BERT with Vision Look At? (2020) (81)
- Large-batch training for LSTM and beyond (2019) (79)
- Robust Decision Trees Against Adversarial Examples (2019) (78)
- Rethinking Architecture Selection in Differentiable NAS (2021) (78)
- Convergence of Adversarial Training in Overparametrized Neural Networks (2019) (77)
- Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data (2018) (77)
- Robustness Verification for Transformers (2020) (70)
- CAT: Customized Adversarial Training for Improved Robustness (2020) (68)
- Rob-GAN: Generator, Discriminator, and Adversarial Attacker (2018) (67)
- On the Robustness of Self-Attentive Models (2019) (66)
- A Scalable Asynchronous Distributed Algorithm for Topic Modeling (2014) (64)
- Robust Reinforcement Learning on State Observations with Learned Optimal Adversary (2021) (62)
- Fast Prediction for Large-Scale Kernel Machines (2014) (61)
- Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers (2020) (60)
- Attack Graph Convolutional Networks by Adding Fake Nodes (2018) (60)
- Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification (2021) (58)
- ML-LOO: Detecting Adversarial Examples with Feature Attribution (2019) (57)
- Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification (2021) (55)
- Learning to Encode Position for Transformer with Continuous Dynamical Model (2020) (55)
- Coordinate Descent Method for Large-scale L 2-loss Linear SVM (2008) (53)
- GPU-acceleration for Large-scale Tree Boosting (2017) (52)
- DrNAS: Dirichlet Neural Architecture Search (2020) (52)
- 100-epoch ImageNet Training with AlexNet in 24 Minutes (2017) (51)
- RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications (2018) (49)
- A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning (2019) (48)
- A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation (2012) (48)
- GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking (2018) (47)
- Goal-Directed Inductive Matrix Completion (2016) (46)
- Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning (2017) (46)
- Robustness Verification of Tree-based Models (2019) (44)
- Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks (2019) (42)
- HogWild++: A New Mechanism for Decentralized Asynchronous Stochastic Gradient Descent (2016) (42)
- Iterative Scaling and Coordinate Descent Methods for Maximum Entropy (2009) (40)
- Large Scale Distributed Sparse Precision Estimation (2013) (37)
- Asynchronous Parallel Greedy Coordinate Descent (2016) (36)
- A Greedy Approach for Budgeted Maximum Inner Product Search (2016) (36)
- Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble (2020) (36)
- Emotional EEG classification using connectivity features and convolutional neural networks (2020) (35)
- Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent (2019) (34)
- Robust and Accurate Object Detection via Adversarial Learning (2021) (34)
- Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations (2020) (32)
- SQL-Rank: A Listwise Approach to Collaborative Ranking (2018) (32)
- Efficient Neural Interaction Function Search for Collaborative Filtering (2019) (32)
- Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples (2020) (31)
- Evaluations and Methods for Explanation through Robustness Analysis (2019) (31)
- MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation (2020) (30)
- Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble (2021) (30)
- Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution (2021) (29)
- Large linear classification when data cannot fit in memory (2010) (29)
- Fast Deep Neural Network Training on Distributed Systems and Cloud TPUs (2019) (28)
- Towards Efficient and Scalable Sharpness-Aware Minimization (2022) (26)
- Improved Adversarial Training via Learned Optimizer (2020) (26)
- Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems (2018) (25)
- Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent (2015) (25)
- Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings (2014) (24)
- GraphDefense: Towards Robust Graph Convolutional Networks (2019) (24)
- Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding (2021) (23)
- Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations (2018) (23)
- An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling (2020) (22)
- A Fast Sampling Algorithm for Maximum Inner Product Search (2019) (20)
- Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction (2021) (20)
- Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers (2019) (20)
- Organizational overlap on social networks and its applications (2013) (19)
- Fast Certified Robust Training with Short Warmup (2021) (18)
- Large-scale Collaborative Ranking in Near-Linear Time (2017) (18)
- Convergence of Adversarial Training in Overparametrized Networks (2019) (18)
- Learning Word Embeddings for Low-Resource Languages by PU Learning (2018) (18)
- ImageNet Training in 24 Minutes (2017) (17)
- An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes (2009) (17)
- Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks (2018) (16)
- Symbolic Discovery of Optimization Algorithms (2023) (16)
- History PCA: A New Algorithm for Streaming PCA (2018) (16)
- Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems (2020) (15)
- RedSync : Reducing Synchronization Traffic for Distributed Deep Learning (2018) (15)
- Stochastic Zeroth-order Optimization via Variance Reduction method (2018) (15)
- General Cutting Planes for Bound-Propagation-Based Neural Network Verification (2022) (13)
- Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective (2019) (13)
- RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection (2021) (13)
- DRONE: Data-aware Low-rank Compression for Large NLP Models (2021) (13)
- Scalable Demand-Aware Recommendation (2017) (13)
- Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient (2018) (12)
- A divide-and-conquer procedure for sparse inverse covariance estimation (2012) (12)
- An inexact subsampled proximal Newton-type method for large-scale machine learning (2017) (12)
- Improved Bounded Matrix Completion for Large-Scale Recommender Systems (2017) (12)
- Improving the Speed and Quality of GAN by Adversarial Training (2020) (11)
- Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks (2021) (11)
- Stochastically Controlled Stochastic Gradient for the Convex and Non-convex Composition problem (2018) (11)
- Robust Principal Component Analysis with Side Information (2016) (11)
- RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving (2021) (11)
- DC-BENCH: Dataset Condensation Benchmark (2022) (11)
- Multi-Stage Influence Function (2020) (11)
- A Review of Adversarial Attack and Defense for Classification Methods (2021) (10)
- Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data (2020) (10)
- QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models (2014) (10)
- On the Transferability of Adversarial Attacks against Neural Text Classifier (2020) (10)
- Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering (2019) (10)
- Enhancing Certifiable Robustness via a Deep Model Ensemble (2019) (10)
- Can Vision Transformers Perform Convolution? (2021) (10)
- Multiple Accounts Detection on Facebook Using Semi-Supervised Learning on Graphs (2018) (9)
- Learning to Learn by Zeroth-Order Oracle (2019) (9)
- Fixing the Convergence Problems in Parallel Asynchronous Dual Coordinate Descent (2016) (9)
- FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning (2022) (9)
- Automatic Perturbation Analysis on General Computational Graphs (2020) (9)
- Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization (2019) (9)
- On Lp-norm Robustness of Ensemble Decision Stumps and Trees (2020) (9)
- Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging (2021) (9)
- Computationally Efficient Nyström Approximation using Fast Transforms (2016) (9)
- On the Faithfulness Measurements for Model Interpretations (2021) (9)
- Local Critic Training for Model-Parallel Learning of Deep Neural Networks (2018) (8)
- ZOO (2017) (8)
- Concurrent Adversarial Learning for Large-Batch Training (2021) (8)
- On the Sensitivity and Stability of Model Interpretations in NLP (2021) (8)
- Rank Aggregation and Prediction with Item Features (2017) (7)
- Spanning attack: reinforce black-box attacks with unlabeled data (2020) (7)
- Extreme Zero-Shot Learning for Extreme Text Classification (2021) (7)
- The Limit of the Batch Size (2020) (7)
- Generalizing Few-Shot NAS with Gradient Matching (2022) (7)
- Label Disentanglement in Partition-based Extreme Multilabel Classification (2021) (7)
- Self-Progressing Robust Training (2020) (7)
- Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation (2021) (7)
- Robust Text CAPTCHAs Using Adversarial Examples (2021) (6)
- Towards Robustness of Deep Neural Networks via Regularization (2021) (6)
- Fast Certified Robust Training via Better Initialization and Shorter Warmup (2021) (6)
- Overcoming Catastrophic Forgetting by Bayesian Generative Regularization (2021) (6)
- ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM (2018) (6)
- Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding (2018) (6)
- Improving the Adversarial Robustness of NLP Models by Information Bottleneck (2022) (6)
- An Efficient Adversarial Attack for Tree Ensembles (2020) (6)
- Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines (2017) (5)
- Detecting Adversarial Examples with Bayesian Neural Network (2021) (5)
- From Adversarial Training to Generative Adversarial Networks (2018) (5)
- Fast Variance Reduction Method with Stochastic Batch Size (2018) (5)
- How and When Adversarial Robustness Transfers in Knowledge Distillation? (2021) (5)
- Sparse Learning with Semi-Proximal-Based Strictly Contractive Peaceman-Rachford Splitting Method (2016) (5)
- Voting based ensemble improves robustness of defensive models (2020) (4)
- On the Convergence of Certified Robust Training with Interval Bound Propagation (2022) (4)
- Extreme Learning to Rank via Low Rank Assumption (2018) (4)
- Multi-Proxy Wasserstein Classifier for Image Classification (2021) (4)
- Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory (2022) (4)
- Efficient Contextual Representation Learning With Continuous Outputs (2019) (4)
- Speeding up ImageNet Training on Supercomputers (2018) (4)
- Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks (2021) (4)
- Evolved Optimizer for Vision (2022) (4)
- Random Sharpness-Aware Minimization (2022) (3)
- A Hyperplane-Based Algorithm for Semi-Supervised Dimension Reduction (2017) (3)
- Fast LSTM by dynamic decomposition on cloud and distributed systems (2020) (3)
- Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation (2022) (3)
- Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search (2020) (3)
- Temporal Collaborative Ranking Via Personalized Transformer (2019) (3)
- Training Meta-Surrogate Model for Transferable Adversarial Attack (2021) (3)
- Provable, Scalable and Automatic Perturbation Analysis on General Computational Graphs (2020) (3)
- A comparison of second-order methods for deep convolutional neural networks (2018) (3)
- Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Imbalanced Data (2019) (3)
- Adversarially Robust Deep Image Super-Resolution Using Entropy Regularization (2020) (3)
- Learning to Schedule Learning rate with Graph Neural Networks (2022) (3)
- Watermarking Pre-trained Language Models with Backdooring (2022) (3)
- ImageNet Training by CPU: AlexNet in 11 Minutes and ResNet-50 in 48 Minutes (2017) (3)
- Provably Robust Metric Learning (2020) (3)
- Efficient Contextual Representation Learning Without Softmax Layer (2019) (3)
- Communication-Efficient Parallel Block Minimization for Kernel Machines (2016) (3)
- Preserving In-Context Learning ability in Large Language Model Fine-tuning (2022) (3)
- Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples (2022) (2)
- Learning from Group Comparisons: Exploiting Higher Order Interactions (2018) (2)
- Overcoming Catastrophic Forgetting by Generative Regularization (2019) (2)
- Are AlphaZero-like Agents Robust to Adversarial Perturbations? (2022) (2)
- Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification (2021) (2)
- Block-wise Partitioning for Extreme Multi-label Classification (2018) (2)
- 2.5D Visual Relationship Detection (2021) (2)
- Regularized sparse inverse covariance matrix estimation (2012) (2)
- Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems (2022) (2)
- Nomadic Computing for Big Data Analytics (2016) (2)
- Efficient Non-Parametric Optimizer Search for Diverse Tasks (2022) (2)
- Communication-avoiding kernel ridge regression on parallel and distributed systems (2021) (1)
- Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks (2021) (1)
- A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks (2022) (1)
- Positive-Unlabeled Demand-Aware Recommendation (2017) (1)
- Fast LSTM Inference by Dynamic Decomposition on Cloud Systems (2019) (1)
- How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers (2020) (1)
- Concept Gradient: Concept-based Interpretation Without Linear Assumption (2022) (1)
- On 𝓁p-norm Robustness of Ensemble Stumps and Trees (2020) (1)
- Stabilizing Neural ODE Networks with Stochasticity (2019) (1)
- MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model (2019) (1)
- Supracellular Measurement of Spatially Varying Mechanical Heterogeneities in Live Monolayers. (2022) (1)
- VERIFIABLY ROBUST NEURAL NETWORKS (2019) (1)
- Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification (2022) (1)
- Adversarial Attack across Datasets (2021) (1)
- Stochastically Controlled Compositional Gradient for Composition Problems (2021) (1)
- Natural Adversarial Sentence Generation with Gradient-based Perturbation (2019) (1)
- Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021) (2021) (1)
- Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables (2019) (1)
- L ARGE B ATCH O PTIMIZATION FOR D EEP L EARNING : T RAINING BERT IN 76 MINUTES (2020) (1)
- Graphic displays of MLB pitching mechanics and its evolutions in PITCHf/x data (2018) (0)
- Machine Learning Meliorates Computing and Robustness in Discrete Combinatorial Optimization Problems (2016) (0)
- Exploiting structure in large-scale optimization for machine learning (2015) (0)
- Reducing Training Sample Memorization in GANs by Training with Memorization Rejection (2022) (0)
- BOSH: An Efficient Meta Algorithm for Decision-based Attacks (2019) (0)
- Uncertainty in Extreme Multi-label Classification (2022) (0)
- SERVATIONS WITH LEARNED OPTIMAL ADVERSARY (2021) (0)
- WORKS: AN EXTREME VALUE THEORY APPROACH (2018) (0)
- O N THE C ONVERGENCE OF C ERTIFIED R OBUST T RAINING WITH I NTERVAL B OUND P ROPAGATION (2022) (0)
- FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search (2022) (0)
- L G ] 1 9 Ju n 20 19 Convergence of Adversarial Training in Overparametrized Networks (2019) (0)
- Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (2022) (0)
- Supplementary Material : Scalable Demand-Aware Recommendation (2018) (0)
- T HE L IMITATIONS OF A DVERSARIAL T RAINING AND THE B LIND-S POT A TTACK (2019) (0)
- ENCE ON LARGE VOCABULARY NEURAL NETWORKS (2018) (0)
- Measures and Best Practices for Responsible AI (2021) (0)
- Online Continuous Hyperparameter Optimization for Contextual Bandits (2023) (0)
- NLRR++: Scalable Subspace Clustering via Non-Convex Block Coordinate Descent (2018) (0)
- Multiple hyperplanes Support Vector Machine for Ranking R 96922050 (2008) (0)
- Distributed Primal-Dual Optimization for Non-uniformly Distributed Data (2018) (0)
- Stochastically Controlled Compositional Gradient for the Composition problem (2019) (0)
- Computable Expert Knowledge in Computer Games (2017) (0)
- R ETHINKING A RCHITECTURE S ELECTION IN D IFFER ENTIABLE NAS (2021) (0)
- Toward Finding The Global Optimal of Adversarial Examples (2019) (0)
- Rethinking the Role of Hyperparameter Tuning in Optimizer Benchmarking (2021) (0)
- Weight Perturbation as Defense against Adversarial Word Substitutions (2022) (0)
- Better Generalization by Efficient Trust Region Method (2018) (0)
- Phenotyping Senescent Mesenchymal Stromal Cells using AI Image Translation (2023) (0)
- The Fourth Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2022) (2022) (0)
- EAD : Elastic-Net Attacks to DNNs Preliminaries on Elastic-Net Regularization (2018) (0)
- Effective Robustness against Natural Distribution Shifts for Models with Different Training Data (2023) (0)
- End-to-End Learning to Index and Search in Large Output Spaces (2022) (0)
- Spatiotemporally Discriminative Video-Language Pre-Training with Text Grounding (2023) (0)
- MASSIVELY PARALLEL INCOMPLETE VERIFIERS (2021) (0)
- Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories (2023) (0)
- AutoZOOM : Background and Methods 3 . 1 Black-box Attack Formulation and Zeroth Order Optimization (2019) (0)
- FEATURE BIG DATA (2016) (0)
- Balancing Robustness and Sensitivity using Feature Contrastive Learning (2021) (0)
- Gradient‐Based Optimizers for Statistics and Machine Learning (2022) (0)
- SPROUT: Self-Progressing Robust Training (2019) (0)
- PECOS (2022) (0)
- On $\ell_p$-norm Robustness of Ensemble Stumps and Trees (2020) (0)
- Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms (2021) (0)
- Temporal Shuffling for Defending Deep Action Recognition Models against Adversarial Attacks (2021) (0)
- ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation (2022) (0)
- C ONCURRENT A DVERSARIAL L EARNING FOR L ARGE B ATCH T RAINING (2022) (0)
- Generating universal language adversarial examples by understanding and enhancing the transferability across neural models (2020) (0)
- Parallel matrix factorization for recommender systems (2013) (0)
- Multiscale Non-stationary Stochastic Bandits (2020) (0)
- Defending Against Adversarial Examples by Regularized Deep Embedding (2019) (0)
- Title: Contextual MDPs: A New Model and PAC Guarantees for Reinforcement Learning with Rich Observations (2016) (0)
- Learning to Learn with Smooth Regularization (2022) (0)
- 1 Exactly Verifying a Single Tree in Linear Time Although computing (2019) (0)
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