b'Cho-Jui Hsieh'

research

∙ 05/31/2023

Representer Point Selection for Explaining Regularized High-dimensional Models

We introduce a novel class of sample-based explanations we term high-dim...

0 Che-Ping Tsai, et al. ∙

research

∙ 05/31/2023

Red Teaming Language Model Detectors with Language Models

The prevalence and high capacity of large language models (LLMs) present...

0 Zhouxing Shi, et al. ∙

research

∙ 05/30/2023

Universality and Limitations of Prompt Tuning

Despite the demonstrated empirical efficacy of prompt tuning to adapt a ...

0 Yihan Wang, et al. ∙

research

∙ 05/29/2023

Robust Lipschitz Bandits to Adversarial Corruptions

Lipschitz bandit is a variant of stochastic bandits that deals with a co...

0 Yue Kang, et al. ∙

research

∙ 05/21/2023

PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation

The eXtreme Multi-label Classification (XMC) problem seeks to find relev...

0 Eli Chien, et al. ∙

research

∙ 04/26/2023

Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories

In this paper, we define, evaluate, and improve the “relay-generalizatio...

0 Li-Cheng Lan, et al. ∙

research

∙ 02/18/2023

Online Continuous Hyperparameter Optimization for Contextual Bandits

In stochastic contextual bandit problems, an agent sequentially makes ac...

0 Yue Kang, et al. ∙

research

∙ 02/13/2023

Symbolic Discovery of Optimization Algorithms

We present a method to formulate algorithm discovery as program search, ...

0 Xiangning Chen, et al. ∙

research

∙ 02/02/2023

Effective Robustness against Natural Distribution Shifts for Models with Different Training Data

“Effective robustness” measures the extra out-of-distribution (OOD) robu...

0 Zhouxing Shi, et al. ∙

research

∙ 11/19/2022

Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory

Dataset distillation methods aim to compress a large dataset into a smal...

0 Justin Cui, et al. ∙

research

∙ 11/07/2022

Are AlphaZero-like Agents Robust to Adversarial Perturbations?

The success of AlphaZero (AZ) has demonstrated that neural-network-based...

1 Li-Cheng Lan, et al. ∙

research

∙ 11/04/2022

Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning

Intentionally crafted adversarial samples have effectively exploited wea...

0 Anaelia Ovalle, et al. ∙

research

∙ 11/01/2022

Preserving In-Context Learning ability in Large Language Model Fine-tuning

Pretrained large language models (LLMs) are strong in-context learners t...

5 Yihan Wang, et al. ∙

research

∙ 10/22/2022

ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation

Adversarial Examples Detection (AED) is a crucial defense technique agai...

0 Fan Yin, et al. ∙

research

∙ 10/21/2022

Reducing Training Sample Memorization in GANs by Training with Memorization Rejection

Generative adversarial network (GAN) continues to be a popular research ...

13 Andrew Bai, et al. ∙

research

∙ 10/18/2022

Uncertainty in Extreme Multi-label Classification

Uncertainty quantification is one of the most crucial tasks to obtain tr...

0 Jyun-Yu Jiang, et al. ∙

research

∙ 10/16/2022

End-to-End Learning to Index and Search in Large Output Spaces

Extreme multi-label classification (XMC) is a popular framework for solv...

22 Nilesh Gupta, et al. ∙

research

∙ 10/14/2022

Watermarking Pre-trained Language Models with Backdooring

Large pre-trained language models (PLMs) have proven to be a crucial com...

0 Chenxi Gu, et al. ∙

research

∙ 10/13/2022

Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation

Lipschitz constants are connected to many properties of neural networks,...

0 Zhouxing Shi, et al. ∙

research

∙ 09/27/2022

Efficient Non-Parametric Optimizer Search for Diverse Tasks

Efficient and automated design of optimizers plays a crucial role in ful...

0 Ruochen Wang, et al. ∙

research

∙ 08/31/2022

Concept Gradient: Concept-based Interpretation Without Linear Assumption

Concept-based interpretations of black-box models are often more intuiti...

0 Andrew Bai, et al. ∙

research

∙ 08/11/2022

General Cutting Planes for Bound-Propagation-Based Neural Network Verification

Bound propagation methods, when combined with branch and bound, are amon...

7 Huan Zhang, et al. ∙

research

∙ 07/20/2022

FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning

Federated learning (FL) has recently attracted increasing attention from...

0 Yuanhao Xiong, et al. ∙

research

∙ 07/20/2022

DC-BENCH: Dataset Condensation Benchmark

Dataset Condensation is a newly emerging technique aiming at learning a ...

0 Justin Cui, et al. ∙

research

∙ 06/11/2022

Improving the Adversarial Robustness of NLP Models by Information Bottleneck

Existing studies have demonstrated that adversarial examples can be dire...

0 Cenyuan Zhang, et al. ∙

research

∙ 03/29/2022

Generalizing Few-Shot NAS with Gradient Matching

Efficient performance estimation of architectures drawn from large searc...

0 Shoukang Hu, et al. ∙

research

∙ 03/16/2022

On the Convergence of Certified Robust Training with Interval Bound Propagation

Interval Bound Propagation (IBP) is so far the base of state-of-the-art ...

7 Yihan Wang, et al. ∙

research

∙ 03/05/2022

Towards Efficient and Scalable Sharpness-Aware Minimization

Recently, Sharpness-Aware Minimization (SAM), which connects the geometr...

5 Yong Liu, et al. ∙

research

∙ 12/16/2021

Extreme Zero-Shot Learning for Extreme Text Classification

The eXtreme Multi-label text Classification (XMC) problem concerns findi...

23 Yuanhao Xiong, et al. ∙

research

∙ 12/15/2021

Temporal Shuffling for Defending Deep Action Recognition Models against Adversarial Attacks

Recently, video-based action recognition methods using convolutional neu...

0 Jaehui Hwang, et al. ∙

research

∙ 11/18/2021

A Review of Adversarial Attack and Defense for Classification Methods

Despite the efficiency and scalability of machine learning systems, rece...

0 Yao Li, et al. ∙

research

∙ 11/02/2021

Can Vision Transformers Perform Convolution?

Several recent studies have demonstrated that attention-based networks, ...

0 Shanda Li, et al. ∙

research

∙ 10/29/2021

Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction

Learning on graphs has attracted significant attention in the learning c...

19 Eli Chien, et al. ∙

research

∙ 10/22/2021

How and When Adversarial Robustness Transfers in Knowledge Distillation?

Knowledge distillation (KD) has been widely used in teacher-student trai...

0 Rulin Shao, et al. ∙

research

∙ 10/13/2021

Adversarial Attack across Datasets

It has been observed that Deep Neural Networks (DNNs) are vulnerable to ...

0 Yunxiao Qin, et al. ∙

research

∙ 09/05/2021

Training Meta-Surrogate Model for Transferable Adversarial Attack

We consider adversarial attacks to a black-box model when no queries are...

0 Yunxiao Qin, et al. ∙

research

∙ 08/29/2021

Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution

Recent studies have shown that deep neural networks are vulnerable to in...

0 Zongyi Li, et al. ∙

research

∙ 08/18/2021

RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving

Predictor-based algorithms have achieved remarkable performance in the N...

0 Ruochen Wang, et al. ∙

research

∙ 08/17/2021

RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection

3D point cloud understanding has made great progress in recent years. Ho...

12 Yongming Rao, et al. ∙

research

∙ 08/10/2021

Rethinking Architecture Selection in Differentiable NAS

Differentiable Neural Architecture Search is one of the most popular Neu...

0 Ruochen Wang, et al. ∙

research

∙ 06/24/2021

Label Disentanglement in Partition-based Extreme Multilabel Classification

Partition-based methods are increasingly-used in extreme multi-label cla...

0 Xuanqing Liu, et al. ∙

research

∙ 06/05/2021

Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms

The stochastic contextual bandit problem, which models the trade-off bet...

0 Qin Ding, et al. ∙

research

∙ 06/05/2021

Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks

Stochastic linear contextual bandit algorithms have substantial applicat...

0 Qin Ding, et al. ∙

research

∙ 06/03/2021

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

Attention is sparse in vision transformers. We observe the final predict...

12 Yongming Rao, et al. ∙

research

∙ 06/03/2021

When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations

Vision Transformers (ViTs) and MLPs signal further efforts on replacing ...

18 Xiangning Chen, et al. ∙

research

∙ 06/01/2021

Concurrent Adversarial Learning for Large-Batch Training

Large-batch training has become a commonly used technique when training ...

0 Yong Liu, et al. ∙

research

∙ 05/19/2021

Balancing Robustness and Sensitivity using Feature Contrastive Learning

It is generally believed that robust training of extremely large network...

11 Seungyeon Kim, et al. ∙

research

∙ 05/18/2021

Detecting Adversarial Examples with Bayesian Neural Network

In this paper, we propose a new framework to detect adversarial examples...

0 Yao Li, et al. ∙

research

∙ 04/30/2021

Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks

Recently, the vulnerability of deep image classification models to adver...

0 Jun-Ho Choi, et al. ∙

research

∙ 04/18/2021

On the Faithfulness Measurements for Model Interpretations

Recent years have witnessed the emergence of a variety of post-hoc inter...

0 Fan Yin, et al. ∙

Cho-Jui Hsieh

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