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Hsiang-Fu Yu

25 papers · 2014–2024 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (10) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (11) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) 🌍 Conference Polyglot (7) 🀝 Dynamic Duo (16) πŸ† Keyword Champion (2) πŸ’Ž Century Club (25) ⚑ Prolific Year (5) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (132) πŸ”₯ Unstoppable (11)

Conferences

NIPS (10) ICML (6) AISTATS (4) NAACL (2) ACL (1) ICLR (1) JMLR (1)

Top co-authors

Cho-jui Hsieh (16) Inderjit S Dhillon (11) Jiong Zhang (7) Wei-Cheng Chang (7) Inderjit Dhillon (6) Inderjit S. Dhillon (6) Eli Chien (3) Nikhil Rao (3) Olgica Milenkovic (2) Jyun-Yu Jiang (2)

Keywords

matrix factorization (6) multi-label classification (4) extreme multi-label classification (4) linear convergence (3) collaborative filtering (3) coordinate descent (3) data augmentation (2) model compression (2) stochastic optimization (2) high-dimensional time series (2) parallel optimization (2) empirical risk minimization (2) demand forecasting (2) maximum inner product search (2) self-supervised learning (1) few-shot learning (1) feature selection (1) logistic regression (1) zero-shot learning (1) text classification (1)

Papers

MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering ACL 2024
Representer Point Selection for Explaining Regularized High-dimensional Models ICML 2023
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation ICML 2023
Extreme Zero-Shot Learning for Extreme Text Classification NAACL 2022
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction ICLR 2022
PECOS: Prediction for Enormous and Correlated Output Spaces JMLR 2022
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces NIPS 2022
Label Disentanglement in Partition-based Extreme Multilabel Classification NIPS 2021
DRONE: Data-aware Low-rank Compression for Large NLP Models NIPS 2021
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification NIPS 2021
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering AISTATS 2020
Learning to Encode Position for Transformer with Continuous Dynamical Model ICML 2020
Extreme Multi-label Classification from Aggregated Labels ICML 2020
Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables AISTATS 2019
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting NIPS 2019
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks NIPS 2019
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models AISTATS 2019
A Fast Sampling Algorithm for Maximum Inner Product Search AISTATS 2019
Learning Word Embeddings for Low-Resource Languages by PU Learning NAACL 2018
A Greedy Approach for Budgeted Maximum Inner Product Search NIPS 2017
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction NIPS 2016
Asynchronous Parallel Greedy Coordinate Descent NIPS 2016
Collaborative Filtering with Graph Information: Consistency and Scalable Methods NIPS 2015
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent ICML 2015
Large-scale Multi-label Learning with Missing Labels ICML 2014