Cho‐jui Hsieh

Cho‐jui Hsieh's AcademicInfluence.com Rankings

Cho‐jui Hsieh

Computer Science

#4790

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#5055

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Data Mining

#77

World Rank

#77

Historical Rank

Machine Learning

#1060

World Rank

#1076

Historical Rank

Database

#1984

World Rank

#2082

Historical Rank

computer-science Degrees

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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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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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