Search Results for author: Zifeng Wang

Found 75 papers, 34 papers with code

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation

1 code implementation28 May 2025 Zhenglun Kong, Zheng Zhan, Shiyue Hou, Yifan Gong, Xin Meng, Pengwei Sui, Peiyan Dong, Xuan Shen, Zifeng Wang, Pu Zhao, Hao Tang, Stratis Ioannidis, Yanzhi Wang

To address these issues, we propose a framework that adaptively selects and aggregates knowledge from diverse LLMs to build a single, stronger model, avoiding the high memory overhead of ensemble and inflexible weight merging.

BioDSA-1K: Benchmarking Data Science Agents for Biomedical Research

no code implementations22 May 2025 Zifeng Wang, Benjamin Danek, Jimeng Sun

We propose BioDSA-1K as a foundation for building and evaluating generalizable, trustworthy AI agents for biomedical discovery.

Benchmarking

TrialPanorama: Database and Benchmark for Systematic Review and Design of Clinical Trials

no code implementations22 May 2025 Zifeng Wang, Qiao Jin, Jiacheng Lin, Junyi Gao, Jathurshan Pradeepkumar, Pengcheng Jiang, Benjamin Danek, Zhiyong Lu, Jimeng Sun

This structured and ontology-grounded design enables TrialPanorama to serve as a unified, extensible resource for a wide range of clinical trial tasks, including trial planning, design, and summarization.

s3: You Don't Need That Much Data to Train a Search Agent via RL

1 code implementation20 May 2025 Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, Jiawei Han

Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference.

RAG Reinforcement Learning (RL) +2

High-Precision Overlay Registration via Spatial-Terminal Iterative Learning in Roll-to-Roll Manufacturing

no code implementations11 Mar 2025 Zifeng Wang, Xiaoning Jin

In this paper, we propose a Spatial-Terminal Iterative Learning Control (STILC) method integrated with PID control to iteratively learn and reduce registration error cycle-by-cycle, converging it to zero.

In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents

no code implementations11 Mar 2025 Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long T. Le, Yiwen Song, Yanfei Chen, Hamid Palangi, George Lee, Anand Iyer, Tianlong Chen, Huan Liu, Chen-Yu Lee, Tomas Pfister

Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information from long-term interactions limits their effectiveness in applications requiring sustained personalization.

Management Reinforcement Learning (RL) +1

Magnet: Multi-turn Tool-use Data Synthesis and Distillation via Graph Translation

no code implementations10 Mar 2025 Fan Yin, Zifeng Wang, I-Hung Hsu, Jun Yan, Ke Jiang, Yanfei Chen, Jindong Gu, Long T. Le, Kai-Wei Chang, Chen-Yu Lee, Hamid Palangi, Tomas Pfister

To address this, we propose Magnet, a principled framework for synthesizing high-quality training trajectories to enhance the function calling capability of large language model agents in multi-turn conversations with humans.

Large Language Model

DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

1 code implementation28 Feb 2025 Pengcheng Jiang, Jiacheng Lin, Lang Cao, Runchu Tian, SeongKu Kang, Zifeng Wang, Jimeng Sun, Jiawei Han

We introduce DeepRetrieval, a reinforcement learning (RL) approach that trains LLMs for query generation through trial and error without supervised data (reference query).

Information Retrieval reinforcement-learning +3

PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving

no code implementations22 Feb 2025 Mihir Parmar, Xin Liu, Palash Goyal, Yanfei Chen, Long Le, Swaroop Mishra, Hossein Mobahi, Jindong Gu, Zifeng Wang, Hootan Nakhost, Chitta Baral, Chen-Yu Lee, Tomas Pfister, Hamid Palangi

Recent agent frameworks and inference-time algorithms often struggle with complex planning problems due to limitations in verifying generated plans or reasoning and varying complexity of instances within a single task.

When One LLM Drools, Multi-LLM Collaboration Rules

no code implementations6 Feb 2025 Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi, Yulia Tsvetkov

This position paper argues that in many realistic (i. e., complex, contextualized, subjective) scenarios, one LLM is not enough to produce a reliable output.

Diversity

SynRL: Aligning Synthetic Clinical Trial Data with Human-preferred Clinical Endpoints Using Reinforcement Learning

no code implementations11 Nov 2024 Trisha Das, Zifeng Wang, Afrah Shafquat, Mandis Beigi, Jason Mezey, Jimeng Sun

In this paper, we propose SynRL which leverages reinforcement learning to improve the performance of patient data generators by customizing the generated data to meet the user-specified requirements for synthetic data outcomes and endpoints.

Can Large Language Models Replace Data Scientists in Biomedical Research?

no code implementations28 Oct 2024 Zifeng Wang, Benjamin Danek, Ziwei Yang, Zheng Chen, Jimeng Sun

To address this gap, we developed a benchmark of data science coding tasks derived from the analyses of 39 published studies.

A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges

no code implementations28 Oct 2024 Zifeng Wang, Hanyin Wang, Benjamin Danek, Ying Li, Christina Mack, Hoifung Poon, Yajuan Wang, Pranav Rajpurkar, Jimeng Sun

The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to clinical decision support, assisting telemedicine, medical devices, and healthcare insurance applications.

Drug Discovery Hallucination

OmnixR: Evaluating Omni-modality Language Models on Reasoning across Modalities

no code implementations16 Oct 2024 Lichang Chen, Hexiang Hu, Mingda Zhang, YiWen Chen, Zifeng Wang, Yandong Li, Pranav Shyam, Tianyi Zhou, Heng Huang, Ming-Hsuan Yang, Boqing Gong

To address this, OmnixR offers two evaluation variants: (1)synthetic subset: a synthetic dataset generated automatically by translating text into multiple modalities--audio, images, video, and hybrids (Omnify).

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence

no code implementations15 Oct 2024 Shangbin Feng, Zifeng Wang, Yike Wang, Sayna Ebrahimi, Hamid Palangi, Lesly Miculicich, Achin Kulshrestha, Nathalie Rauschmayr, Yejin Choi, Yulia Tsvetkov, Chen-Yu Lee, Tomas Pfister

Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single task, multi-task domains, reward models, as well as diverse human interests, improving over 12 model composition baselines by up to 21. 0% across tasks and contexts.

Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling

no code implementations15 Oct 2024 Wenda Xu, Rujun Han, Zifeng Wang, Long T. Le, Dhruv Madeka, Lei LI, William Yang Wang, Rishabh Agarwal, Chen-Yu Lee, Tomas Pfister

To address these limitations, we introduce Speculative Knowledge Distillation (SKD), a novel approach that leverages cooperation between student and teacher models to generate high-quality training data on-the-fly while aligning with the student's inference-time distribution.

Instruction Following Knowledge Distillation +2

TableRAG: Million-Token Table Understanding with Language Models

1 code implementation7 Oct 2024 Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen, Yasuhisa Fujii, Hsuan-Tien Lin, Chen-Yu Lee, Tomas Pfister

Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables.

RAG Retrieval +1

Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting

no code implementations11 Jul 2024 Zilong Wang, Zifeng Wang, Long Le, Huaixiu Steven Zheng, Swaroop Mishra, Vincent Perot, Yuwei Zhang, Anush Mattapalli, Ankur Taly, Jingbo Shang, Chen-Yu Lee, Tomas Pfister

Retrieval augmented generation (RAG) combines the generative abilities of large language models (LLMs) with external knowledge sources to provide more accurate and up-to-date responses.

ARC RAG +3

Accelerating Clinical Evidence Synthesis with Large Language Models

no code implementations25 Jun 2024 Zifeng Wang, Lang Cao, Benjamin Danek, Qiao Jin, Zhiyong Lu, Jimeng Sun

Here, we introduce TrialMind, a generative artificial intelligence (AI) pipeline for facilitating human-AI collaboration in three crucial tasks for evidence synthesis: study search, screening, and data extraction.

Language Modelling

Panacea: A foundation model for clinical trial search, summarization, design, and recruitment

1 code implementation25 Jun 2024 Jiacheng Lin, Hanwen Xu, Zifeng Wang, Sheng Wang, Jimeng Sun

To address this challenge, we propose a clinical trial foundation model named Panacea, designed to handle multiple tasks, including trial search, trial summarization, trial design, and patient-trial matching.

Clinical Knowledge

Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization

no code implementations23 Jun 2024 Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister

Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input.

RAG Retrieval-augmented Generation

CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation

no code implementations8 Jun 2024 I-Hung Hsu, Zifeng Wang, Long T. Le, Lesly Miculicich, Nanyun Peng, Chen-Yu Lee, Tomas Pfister

Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses by accurately citing verifiable sources.

Open-Domain Question Answering

Continual Learning of Large Language Models: A Comprehensive Survey

2 code implementations25 Apr 2024 Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, Zifeng Wang, Sayna Ebrahimi, Hao Wang

In this survey, we provide a comprehensive overview of the current research progress on LLMs within the context of CL.

Continual Learning Survey

CodecLM: Aligning Language Models with Tailored Synthetic Data

no code implementations8 Apr 2024 Zifeng Wang, Chun-Liang Li, Vincent Perot, Long T. Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister

To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs.

Instruction Following

ADAPT to Robustify Prompt Tuning Vision Transformers

1 code implementation19 Mar 2024 Masih Eskandar, Tooba Imtiaz, Zifeng Wang, Jennifer Dy

The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks.

Adversarial Defense

TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale

no code implementations15 Mar 2024 Pengcheng Jiang, Cao Xiao, Zifeng Wang, Parminder Bhatia, Jimeng Sun, Jiawei Han

To overcome this, we introduce TriSum, a framework for distilling LLMs' text summarization abilities into a compact, local model.

Text Summarization

GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models

1 code implementation16 Feb 2024 Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun, Jiawei Han

The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs).

Relation Relation Extraction +1

PILOT: Legal Case Outcome Prediction with Case Law

no code implementations28 Jan 2024 Lang Cao, Zifeng Wang, Cao Xiao, Jimeng Sun

We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.

Decision Making Prediction +1

BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs

1 code implementation5 Oct 2023 Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Huzefa Rangwala, Rishita Anubhai

Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.

Cross-Modal Retrieval Domain Generalization +3

UniPredict: Large Language Models are Universal Tabular Classifiers

no code implementations5 Oct 2023 Ruiyu Wang, Zifeng Wang, Jimeng Sun

Specifically, we train a single LLM on an aggregation of 169 tabular datasets with diverse targets and compare its performance against baselines that are trained on each dataset separately.

Few-Shot Learning

CITING: Large Language Models Create Curriculum for Instruction Tuning

no code implementations4 Oct 2023 Tao Feng, Zifeng Wang, Jimeng Sun

Specifically, we employ a teacher LLM to create a curriculum for instruction tuning of the student LLM, namely Curriculum Instruction TunING (CITING).

LMDX: Language Model-based Document Information Extraction and Localization

no code implementations19 Sep 2023 Vincent Perot, Kai Kang, Florian Luisier, Guolong Su, Xiaoyu Sun, Ramya Sree Boppana, Zilong Wang, Zifeng Wang, Jiaqi Mu, Hao Zhang, Chen-Yu Lee, Nan Hua

The main obstacles to adopting LLMs for this task include the absence of layout encoding within LLMs, which is critical for high quality extraction, and the lack of a grounding mechanism to localize the predicted entities within the document.

Language Modeling Language Modelling

MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models

1 code implementation17 Aug 2023 Yilin Wen, Zifeng Wang, Jimeng Sun

Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks.

Decision Making Hallucination +5

Matching Patients to Clinical Trials with Large Language Models

1 code implementation27 Jul 2023 Qiao Jin, Zifeng Wang, Charalampos S. Floudas, Fangyuan Chen, Changlin Gong, Dara Bracken-Clarke, Elisabetta Xue, Yifan Yang, Jimeng Sun, Zhiyong Lu

The TrialGPT-Ranking scores are highly correlated with human judgments and outperform the best-competing models by 43. 8% in ranking and excluding trials.

Language Modelling Large Language Model +1

SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)

no code implementations26 May 2023 Ruoxi Sun, Sercan Ö. Arik, Alex Muzio, Lesly Miculicich, Satya Gundabathula, Pengcheng Yin, Hanjun Dai, Hootan Nakhost, Rajarishi Sinha, Zifeng Wang, Tomas Pfister

Text-to-SQL, the process of translating natural language into Structured Query Language (SQL), represents a transformative application of large language models (LLMs), potentially revolutionizing how humans interact with data.

Data Augmentation In-Context Learning +5

MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement

1 code implementation20 May 2023 Zifeng Wang, Chufan Gao, Cao Xiao, Jimeng Sun

Tabular data prediction has been employed in medical applications such as patient health risk prediction.

Prediction

SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning

no code implementations7 Apr 2023 Zifeng Wang, Cao Xiao, Jimeng Sun

Accurate trial outcome prediction based on historical trial data promises better trial investment decisions and more trial success.

Meta-Learning

Robust Meta Learning for Image based tasks

no code implementations30 Jan 2023 Penghao Jiang, Xin Ke, Zifeng Wang, Chunxi Li

However, learning such a model is not possible in standard machine learning frameworks as the distribution of the test data is unknown.

Meta-Learning

Invariant Meta Learning for Out-of-Distribution Generalization

no code implementations26 Jan 2023 Penghao Jiang, Ke Xin, Zifeng Wang, Chunxi Li

Modern deep learning techniques have illustrated their excellent capabilities in many areas, but relies on large training data.

Meta-Learning Out-of-Distribution Generalization

QueryForm: A Simple Zero-shot Form Entity Query Framework

no code implementations14 Nov 2022 Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.

document understanding Form +1

MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

1 code implementation18 Oct 2022 Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, Jimeng Sun

Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.

Contrastive Learning Image-text Retrieval +1

PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning

1 code implementation11 Oct 2022 Zifeng Wang, Jimeng Sun

Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is challenging due to privacy concerns, which hinders the use of ML for healthcare applications.

Attribute Imputation +2

SparCL: Sparse Continual Learning on the Edge

1 code implementation20 Sep 2022 Zifeng Wang, Zheng Zhan, Yifan Gong, Geng Yuan, Wei Niu, Tong Jian, Bin Ren, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy

SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity.

Continual Learning

Artificial Intelligence for In Silico Clinical Trials: A Review

no code implementations16 Sep 2022 Zifeng Wang, Chufan Gao, Lucas M. Glass, Jimeng Sun

In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials.

Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision

1 code implementation29 Jun 2022 Zifeng Wang, Jimeng Sun

We propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns through self-supervision without annotating similar clinical trials.

Clinical Knowledge Retrieval

Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network

1 code implementation12 Jan 2022 Batool Salehi, Guillem Reus-Muns, Debashri Roy, Zifeng Wang, Tong Jian, Jennifer Dy, Stratis Ioannidis, Kaushik Chowdhury

Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times.

Edge-computing

Learning to Prompt for Continual Learning

5 code implementations CVPR 2022 Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister

The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.

class-incremental learning Class Incremental Learning +2

SurvTRACE: Transformers for Survival Analysis with Competing Events

1 code implementation2 Oct 2021 Zifeng Wang, Jimeng Sun

In medicine, survival analysis studies the time duration to events of interest such as mortality.

Multi-Task Learning Selection bias +1

PAC-Bayes Information Bottleneck

1 code implementation ICLR 2022 Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu, Jimeng Sun, Xi Chen, Yefeng Zheng

Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.

Deep Bayesian Unsupervised Lifelong Learning

1 code implementation13 Jun 2021 Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer Dy

We develop a fully Bayesian inference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, which can progressively discover new clusters without forgetting the past with unlabelled data while learning latent representations.

Bayesian Inference Lifelong learning

Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness

1 code implementation NeurIPS 2021 Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer Dy

We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier.

Adversarial Robustness

Lifelong Learning based Disease Diagnosis on Clinical Notes

1 code implementation27 Feb 2021 Zifeng Wang, Yifan Yang, Rui Wen, Xi Chen, Shao-Lun Huang, Yefeng Zheng

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i. e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks.

Lifelong learning

Open-World Class Discovery with Kernel Networks

1 code implementation13 Dec 2020 Zifeng Wang, Batool Salehi, Andrey Gritsenko, Kaushik Chowdhury, Stratis Ioannidis, Jennifer Dy

We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples.

Learn-Prune-Share for Lifelong Learning

1 code implementation13 Dec 2020 Zifeng Wang, Tong Jian, Kaushik Chowdhury, Yanzhi Wang, Jennifer Dy, Stratis Ioannidis

In lifelong learning, we wish to maintain and update a model (e. g., a neural network classifier) in the presence of new classification tasks that arrive sequentially.

Lifelong learning

Instance-wise Feature Grouping

no code implementations NeurIPS 2020 Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy

Moreover, the features that belong to each group, and the important feature groups may vary per sample.

General Classification

Finding Influential Instances for Distantly Supervised Relation Extraction

no code implementations COLING 2022 Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng

Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise.

Relation Relation Extraction

Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

no code implementations6 Sep 2020 Zifeng Wang, Rui Wen, Xi Chen, Shilei Cao, Shao-Lun Huang, Buyue Qian, Yefeng Zheng

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs).

Graph Representation Learning Inductive Learning +1

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

1 code implementation NeurIPS 2020 Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng

Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.

counterfactual Recommendation Systems

On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond

no code implementations25 Jan 2020 Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang

Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk.

counterfactual Fairness

Less Is Better: Unweighted Data Subsampling via Influence Function

1 code implementation3 Dec 2019 Zifeng Wang, Hong Zhu, Zhenhua Dong, Xiuqiang He, Shao-Lun Huang

In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.

General Classification image-classification +3

Streaming Adaptive Nonparametric Variational Autoencoder

no code implementations7 Jun 2019 Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer G. Dy

We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data.

Clustering Feature Engineering +1

Collaborative Deep Reinforcement Learning for Multi-Object Tracking

no code implementations ECCV 2018 Liangliang Ren, Jiwen Lu, Zifeng Wang, Qi Tian, Jie zhou

To address this, we develop a deep prediction-decision network in our C-DRL, which simultaneously detects and predicts objects under a unified network via deep reinforcement learning.

Deep Reinforcement Learning Multi-Object Tracking +3

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