1.8k citations · 9.5k across the 166 of their papers we have counts for
99 papers · 1 filter
Representer Point Selection for Explaining Regularized High-dimensional Models
Che-Ping Tsai, Jiong Zhang, Eli Chien +3
We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model i…
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Eli Chien, Jiong Zhang, Cho-Jui Hsieh +4
The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extractio…
Universality and Limitations of Prompt Tuning
Yihan Wang, Jatin Chauhan, Wei Wang +1
Despite the demonstrated empirical efficacy of prompt tuning to adapt a pretrained language model for a new task, the theoretical underpinnings of the difference between "tuning pa…
Robust Lipschitz Bandits to Adversarial Corruptions
Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee
Lipschitz bandit is a variant of stochastic bandits that deals with a continuous arm set defined on a metric space, where the reward function is subject to a Lipschitz constraint.…
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories
Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh
In this paper, we define, evaluate, and improve the ``relay-generalization'' performance of reinforcement learning (RL) agents on the out-of-distribution ``controllable'' states. I…
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits
Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee
In stochastic contextual bandits, an agent sequentially makes actions from a time-dependent action set based on past experience to minimize the cumulative regret. Like many other m…
Symbolic Discovery of Optimization Algorithms
Xiangning Chen, Chen Liang, Da Huang +9
We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient sea…
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
Zhouxing Shi, Nicholas Carlini, Ananth Balashankar +4
"Effective robustness" measures the extra out-of-distribution (OOD) robustness beyond what can be predicted from the in-distribution (ID) performance. Existing effective robustness…
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning
Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh
Intentionally crafted adversarial samples have effectively exploited weaknesses in deep neural networks. A standard method in adversarial robustness assumes a framework to defend a…
Reducing Training Sample Memorization in GANs by Training with Memorization Rejection
Andrew Bai, Cho-Jui Hsieh, Wendy Kan +1
Generative adversarial network (GAN) continues to be a popular research direction due to its high generation quality. It is observed that many state-of-the-art GANs generate sample…
Uncertainty in Extreme Multi-label Classification
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhong +2
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain ha…
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation
Zhouxing Shi, Yihan Wang, Huan Zhang +2
Lipschitz constants are connected to many properties of neural networks, such as robustness, fairness, and generalization. Existing methods for computing Lipschitz constants either…
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu +2
Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output c…
Efficient Non-Parametric Optimizer Search for Diverse Tasks
Ruochen Wang, Yuanhao Xiong, Minhao Cheng +1
Efficient and automated design of optimizers plays a crucial role in full-stack AutoML systems. However, prior methods in optimizer search are often limited by their scalability, g…
Concept Gradient: Concept-based Interpretation Without Linear Assumption
Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar +2
Concept-based interpretations of black-box models are often more intuitive for humans to understand. The most widely adopted approach for concept-based interpretation is Concept Ac…
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Huan Zhang, Shiqi Wang, Kaidi Xu +5
Bound propagation methods, when combined with branch and bound, are among the most effective methods to formally verify properties of deep neural networks such as correctness, robu…
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng +2
Federated learning~(FL) has recently attracted increasing attention from academia and industry, with the ultimate goal of achieving collaborative training under privacy and communi…
DC-BENCH: Dataset Condensation Benchmark
Justin Cui, Ruochen Wang, Si Si +1
Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset. As the size of datasets con…
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search
Patrick H. Chen, Chang Wei-cheng, Yu Hsiang-fu +2
Approximate K-Nearest Neighbor Search (AKNNS) has now become ubiquitous in modern applications, for example, as a fast search procedure with two tower deep learning models. Graph-b…
On the Convergence of Certified Robust Training with Interval Bound Propagation
Yihan Wang, Zhouxing Shi, Quanquan Gu +1
Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial pertu…
Towards Efficient and Scalable Sharpness-Aware Minimization
Yong Liu, Siqi Mai, Xiangning Chen +2
Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of the loss landscape and generalization, has demonstrated significant performance boosts on training larg…
Extreme Zero-Shot Learning for Extreme Text Classification
Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh +2
The eXtreme Multi-label text Classification (XMC) problem concerns finding most relevant labels for an input text instance from a large label set. However, the XMC setup faces two…
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh +4
Learning on graphs has attracted significant attention in the learning community due to numerous real-world applications. In particular, graph neural networks (GNNs), which take nu…
How and When Adversarial Robustness Transfers in Knowledge Distillation?
Rulin Shao, Jinfeng Yi, Pin-Yu Chen +1
Knowledge distillation (KD) has been widely used in teacher-student training, with applications to model compression in resource-constrained deep learning. Current works mainly foc…