64 citations · 253 across the 34 of their papers we have counts for
37 papers
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints
Chaoqi Wang, Yibo Jiang, Chenghao Yang +2
The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misu…
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Fengxue Zhang, Jialin Song, James Bowden +4
We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, whic…
Active Policy Improvement from Multiple Black-box Oracles
Xuefeng Liu, Takuma Yoneda, Chaoqi Wang +2
Reinforcement learning (RL) has made significant strides in various complex domains. However, identifying an effective policy via RL often necessitates extensive exploration. Imita…
Efficient Online Decision Tree Learning with Active Feature Acquisition
Arman Rahbar, Ziyu Ye, Yuxin Chen +1
Constructing decision trees online is a classical machine learning problem. Existing works often assume that features are readily available for each incoming data point. However, i…
Learning Human-Compatible Representations for Case-Based Decision Support
Han Liu, Yizhou Tian, Chacha Chen +3
Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks. Despite the promising performance of…
Contextual Active Model Selection
Xuefeng Liu, Fangfang Xia, Rick L. Stevens +1
While training models and labeling data are resource-intensive, a wealth of pre-trained models and unlabeled data exists. To effectively utilize these resources, we present an appr…
Learning Representation for Bayesian Optimization with Collision-free Regularization
Fengxue Zhang, Brian Nord, Yuxin Chen
Bayesian optimization has been challenged by datasets with large-scale, high-dimensional, and non-stationary characteristics, which are common in real-world scenarios. Recent works…
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen +2
As practitioners increasingly deploy machine learning models in critical domains such as health care, finance, and policy, it becomes vital to ensure that domain experts function e…
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
Renyu Zhang, Aly A. Khan, Robert L. Grossman +1
Active learning has demonstrated data efficiency in many fields. Existing active learning algorithms, especially in the context of batch-mode deep Bayesian active models, rely heav…
Online Learning of Energy Consumption for Navigation of Electric Vehicles
Niklas Åkerblom, Yuxin Chen, Morteza Haghir Chehreghani
Energy efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consum…
Teaching an Active Learner with Contrastive Examples
Chaoqi Wang, Adish Singla, Yuxin Chen
We study the problem of active learning with the added twist that the learner is assisted by a helpful teacher. We consider the following natural interaction protocol: At each roun…
Understanding the Effect of Bias in Deep Anomaly Detection
Ziyu Ye, Yuxin Chen, Haitao Zheng
Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting trainin…
Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities
Chinmaya Mahesh, Kristin Dona, David W. Miller +1
Data-intensive science is increasingly reliant on real-time processing capabilities and machine learning workflows, in order to filter and analyze the extreme volumes of data being…
Preference-Based Batch and Sequential Teaching
Farnam Mansouri, Yuxin Chen, Ara Vartanian +2
Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest t…
The Teaching Dimension of Kernel Perceptron
Akash Kumar, Hanqi Zhang, Adish Singla +1
Algorithmic machine teaching has been studied under the linear setting where exact teaching is possible. However, little is known for teaching nonlinear learners. Here, we establis…
Using Ensemble Classifiers to Detect Incipient Anomalies
Baihong Jin, Yingshui Tan, Albert Liu +3
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions. Th…
Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
Yingshui Tan, Baihong Jin, Xiangyu Yue +2
Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks. In this approach, predictions from a diverse set of learne…
Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?
Baihong Jin, Yingshui Tan, Yuxin Chen +2
Intermediate-Severity (IS) faults present milder symptoms compared to severe faults, and are more difficult to detect and diagnose due to their close resemblance to normal operatin…
Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries
Akash Kumar, Adish Singla, Yisong Yue +1
We examine the task of locating a target region among those induced by intersections of halfspaces in . This generic task connects to fundamental machine learning…
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
Niklas Åkerblom, Yuxin Chen, Morteza Haghir Chehreghani
Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consum…
Adaptive Teaching of Temporal Logic Formulas to Learners with Preferences
Zhe Xu, Yuxin Chen, Ufuk Topcu
Machine teaching is an algorithmic framework for teaching a target hypothesis via a sequence of examples or demonstrations. We investigate machine teaching for temporal logic formu…
Landmark Ordinal Embedding
Nikhil Ghosh, Yuxin Chen, Yisong Yue
In this paper, we aim to learn a low-dimensional Euclidean representation from a set of constraints of the form "item j is closer to item i than item k". Existing approaches for th…
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
Farnam Mansouri, Yuxin Chen, Ara Vartanian +2
Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest t…
Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults
Baihong Jin, Yingshui Tan, Yuxin Chen +1
The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions. This approach was recently ap…