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05/31/2023
Representer Point Selection for Explaining Regularized High-dimensional Models
We introduce a novel class of sample-based explanations we term high-dim...
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05/21/2023
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
The eXtreme Multi-label Classification (XMC) problem seeks to find relev...
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10/18/2022
Uncertainty in Extreme Multi-label Classification
Uncertainty quantification is one of the most crucial tasks to obtain tr...
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10/16/2022
End-to-End Learning to Index and Search in Large Output Spaces
Extreme multi-label classification (XMC) is a popular framework for solv...
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02/21/2022
Accelerating Primal-dual Methods for Regularized Markov Decision Processes
Entropy regularized Markov decision processes have been widely used in r...
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12/16/2021
Extreme Zero-Shot Learning for Extreme Text Classification
The eXtreme Multi-label text Classification (XMC) problem concerns findi...
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10/29/2021
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
Learning on graphs has attracted significant attention in the learning c...
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10/01/2021
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMC) seeks to find relevant lab...
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06/24/2021
Label Disentanglement in Partition-based Extreme Multilabel Classification
Partition-based methods are increasingly-used in extreme multi-label cla...
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06/23/2021
Extreme Multi-label Learning for Semantic Matching in Product Search
We consider the problem of semantic matching in product search: given a ...
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06/04/2021
Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees
Tree-based models underpin many modern semantic search engines and recom...
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10/12/2020
PECOS: Prediction for Enormous and Correlated Output Spaces
Many challenging problems in modern applications amount to finding relev...
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04/01/2020
Extreme Multi-label Classification from Aggregated Labels
Extreme multi-label classification (XMC) is the problem of finding the r...
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03/13/2020
Learning to Encode Position for Transformer with Continuous Dynamical Model
We introduce a new way of learning to encode position information for no...
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08/27/2019
Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure
The architecture of Transformer is based entirely on self-attention, and...
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05/29/2019
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
In this paper, we consider recommender systems with side information in ...
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05/09/2019
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Forecasting high-dimensional time series plays a crucial role in many ap...
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05/08/2019
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
Deep neural networks have yielded superior performance in many applicati...
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05/07/2019
A Modular Deep Learning Approach for Extreme Multi-label Text Classification
Extreme multi-label classification (XMC) aims to assign to an instance t...
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05/09/2018
LearningWord Embeddings for Low-resource Languages by PU Learning
Word embedding is a key component in many downstream applications in pro...
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05/31/2016
Extreme Stochastic Variational Inference: Distributed and Asynchronous
We propose extreme stochastic variational inference (ESVI), an asynchron...
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09/28/2015