Search Results for author: Anqi Liu

Found 39 papers, 12 papers with code

Robust Classification Under Sample Selection Bias

no code implementations NeurIPS 2014 Anqi Liu, Brian Ziebart

In many important machine learning applications, the source distribution used to estimate a probabilistic classifier differs from the target distribution on which the classifier will be used to make predictions.

Binary Classification Classification +3

Etude acoustique et repr\'esentation phonologique sur /ə˞/ suffixe rhotique en mandarin (Acoustic study and phonological representation of the rhotic suffix /ə˞/ in mandarin)

no code implementations JEPTALNRECITAL 2016 Anqi Liu

Historiquement, le suffixe /ə˞/ est un suffixe diminutif correspondant au mot 儿 ({\textless}er{\textgreater} en pinyin) qui signifie {''}petitesse{''}.

Adversarial Multiclass Classification: A Risk Minimization Perspective

no code implementations NeurIPS 2016 Rizal Fathony, Anqi Liu, Kaiser Asif, Brian Ziebart

Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses.

Classification General Classification

Kernel Robust Bias-Aware Prediction under Covariate Shift

no code implementations28 Dec 2017 Anqi Liu, Rizal Fathony, Brian D. Ziebart

Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase logarithmic loss for the target distribution while matching feature expectation constraints from the source distribution.

Robust Covariate Shift Prediction with General Losses and Feature Views

no code implementations28 Dec 2017 Anqi Liu, Brian D. Ziebart

Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions.

Learning Gibbs-regularized GANs with variational discriminator reparameterization

no code implementations27 Sep 2018 Nicholas Rhinehart, Anqi Liu, Kihyuk Sohn, Paul Vernaza

We propose a novel approach to regularizing generative adversarial networks (GANs) leveraging learned {\em structured Gibbs distributions}.

Trajectory Forecasting

Regularized Learning for Domain Adaptation under Label Shifts

2 code implementations ICLR 2019 Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar

We derive a generalization bound for the classifier on the target domain which is independent of the (ambient) data dimensions, and instead only depends on the complexity of the function class.

Domain Adaptation

Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning

no code implementations13 Jun 2019 Quanying Liu, Haiyan Wu, Anqi Liu

Our results demonstrate that IRL is an effective tool to model human decision-making behavior, as well as to help interpret the human psychological process in risk decision-making.

Decision Making reinforcement-learning +1

Robust Regression for Safe Exploration in Control

no code implementations L4DC 2020 Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue

To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration.

Generalization Bounds regression +1

Triply Robust Off-Policy Evaluation

no code implementations13 Nov 2019 Anqi Liu, Hao liu, Anima Anandkumar, Yisong Yue

Ours is a general approach that can be used to augment any existing OPE method that utilizes the direct method.

Multi-Armed Bandits Off-policy evaluation +1

Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

no code implementations9 May 2020 Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung

The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints.

Motion Planning Optimal Motion Planning +1

Active Learning under Label Shift

no code implementations16 Jul 2020 Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue

We address the problem of active learning under label shift: when the class proportions of source and target domains differ.

Active Learning

Distributionally Robust Learning for Unsupervised Domain Adaptation

no code implementations28 Sep 2020 Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, Anima Anandkumar

This formulation motivates the use of two neural networks that are jointly trained --- a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network.

Density Ratio Estimation Unsupervised Domain Adaptation

Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach

no code implementations8 Oct 2020 Haoxuan Wang, Zhiding Yu, Yisong Yue, Anima Anandkumar, Anqi Liu, Junchi Yan

We propose a framework for learning calibrated uncertainties under domain shifts, where the source (training) distribution differs from the target (test) distribution.

Density Ratio Estimation Unsupervised Domain Adaptation

Robust Fairness under Covariate Shift

1 code implementation11 Oct 2020 Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian Ziebart

We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same.

Fairness

Dynamic Social Media Monitoring for Fast-Evolving Online Discussions

no code implementations24 Feb 2021 Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R. Michael Alvarez, Anima Anandkumar

However, collecting social media data using a static set of keywords fails to satisfy the growing need to monitor dynamic conversations and to study fast-changing topics.

Decision Making Time Series +1

Scaling Fair Learning to Hundreds of Intersectional Groups

no code implementations29 Sep 2021 Eric Zhao, De-An Huang, Hao liu, Zhiding Yu, Anqi Liu, Olga Russakovsky, Anima Anandkumar

In real-world applications, however, there are multiple protected attributes yielding a large number of intersectional protected groups.

Attribute Fairness +1

A Simplified System Model for Optical Camera Communication

no code implementations Conference 2021 Anqi Liu, Wenxiao Shi, Wei Liu, Zhuo Wang

Data rate and communication distance are two important criteria for measuring the performance of optical camera communication (OCC) systems.

JAWS: Auditing Predictive Uncertainty Under Covariate Shift

1 code implementation21 Jul 2022 Drew Prinster, Anqi Liu, Suchi Saria

We propose \textbf{JAWS}, a series of wrapper methods for distribution-free uncertainty quantification tasks under covariate shift, centered on the core method \textbf{JAW}, the \textbf{JA}ckknife+ \textbf{W}eighted with data-dependent likelihood-ratio weights.

Uncertainty Quantification

Repeated Environment Inference for Invariant Learning

1 code implementation26 Jul 2022 Aayush Mishra, Anqi Liu

The EI step uses a reference model which focuses on spurious correlations to efficiently reach a good environment partition.

A New Hip Fracture Risk Index Derived from FEA-Computed Proximal Femur Fracture Loads and Energies-to-Failure

no code implementations3 Oct 2022 Xuewei Cao, Joyce H Keyak, Sigurdur Sigurdsson, Chen Zhao, Weihua Zhou, Anqi Liu, Thomas Lang, Hong-Wen Deng, Vilmundur Gudnason, Qiuying Sha

The results showed that the average of the area under the receive operating characteristic curve (AUC) using PC1 was always higher than that using all FE parameters combined in the male subjects.

Ambiguous Images With Human Judgments for Robust Visual Event Classification

no code implementations6 Oct 2022 Kate Sanders, Reno Kriz, Anqi Liu, Benjamin Van Durme

However, humans are frequently presented with visual data that they cannot classify with 100% certainty, and models trained on standard vision benchmarks achieve low performance when evaluated on this data.

Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling

1 code implementation6 Dec 2022 Zhaoning Li, Qiaoli Jiang, Zhengming Wu, Anqi Liu, Haiyan Wu, Miner Huang, Kai Huang, Yixuan Ku

The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario.

Autonomous Driving

Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection

no code implementations23 Dec 2022 Zhitong Yang, Xing Ma, Anqi Liu, Zheyu Zhang

Task-oriented dialog(TOD) aims to assist users in achieving specific goals through multi-turn conversation.

Intent Detection

ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound

no code implementations1 Feb 2023 Anton Orlichenko, Grant Daly, Ziyu Zhou, Anqi Liu, Hui Shen, Hong-Wen Deng, Yu-Ping Wang

The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset.

Data Visualization Navigate

Density-Softmax: Scalable and Calibrated Uncertainty Estimation under Distribution Shifts

1 code implementation13 Feb 2023 Ha Manh Bui, Anqi Liu

In this paper, we propose Density-Softmax, a fast and lightweight deterministic method to improve calibrated uncertainty estimation via a combination of density function with the softmax layer.

Computational Efficiency

Double-Weighting for Covariate Shift Adaptation

1 code implementation15 May 2023 José I. Segovia-Martín, Santiago Mazuelas, Anqi Liu

Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates $x$) of training and testing samples $\mathrm{p}_\text{tr}(x)$ and $\mathrm{p}_\text{te}(x)$ are different but the label conditionals coincide.

Generalization Bounds

MegaWika: Millions of reports and their sources across 50 diverse languages

no code implementations13 Jul 2023 Samuel Barham, Orion Weller, Michelle Yuan, Kenton Murray, Mahsa Yarmohammadi, Zhengping Jiang, Siddharth Vashishtha, Alexander Martin, Anqi Liu, Aaron Steven White, Jordan Boyd-Graber, Benjamin Van Durme

To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials.

Cross-Lingual Question Answering Retrieval +1

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

1 code implementation2 Aug 2023 Anton Orlichenko, Gang Qu, Kuan-Jui Su, Anqi Liu, Hui Shen, Hong-Wen Deng, Yu-Ping Wang

Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10, 000 training subjects without double-dipping.

Evaluating and Enhancing Large Language Models for Conversational Reasoning on Knowledge Graphs

1 code implementation18 Dec 2023 Yuxuan Huang, Lida Shi, Anqi Liu, Hao Xu

We further introduce LLM-ARK, a LLM grounded KG reasoning agent designed to deliver precise and adaptable predictions on KG paths.

Decision Making Knowledge Graphs +1

Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits

no code implementations21 Jan 2024 Yihong Guo, Hao liu, Yisong Yue, Anqi Liu

Central to our methodology is the application of robust regression, a distributionally robust technique tailored here to improve the estimation of conditional reward distribution from logging data.

Multi-Armed Bandits regression

RORA: Robust Free-Text Rationale Evaluation

no code implementations28 Feb 2024 Zhengping Jiang, Yining Lu, Hanjie Chen, Daniel Khashabi, Benjamin Van Durme, Anqi Liu

This is achieved by assessing the conditional V-information \citep{hewitt-etal-2021-conditional} with a predictive family robust against leaky features that can be exploited by a small model.

Decision Making

Density-Regression: Efficient and Distance-Aware Deep Regressor for Uncertainty Estimation under Distribution Shifts

1 code implementation7 Mar 2024 Manh Ha Bui, Anqi Liu

Morden deep ensembles technique achieves strong uncertainty estimation performance by going through multiple forward passes with different models.

Depth Estimation regression +1

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