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Found 16 papers, 7 papers with code
Robust Multi-Task Learning with Excess Risks
To overcome this limitation, we propose Multi-Task Learning with Excess Risks (ExcessMTL), an excess risk-based task balancing method that updates the task weights by their distances to convergence instead.
Threshold-aware Learning to Generate Feasible Solutions for Mixed Integer Programs
Our study investigates that a specific range of variable assignment rates (coverage) yields high-quality feasible solutions, where we suggest optimizing the coverage bridges the gap between the learning and MIP objectives.
MICO: Selective Search with Mutual Information Co-training
In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups.
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone
Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems.
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles
State-less attention-based decoder fails to make such coordination between vehicles.
Tiering as a Stochastic Submodular Optimization Problem
Tiering is an essential technique for building large-scale information retrieval systems.
Robustness to Capitalization Errors in Named Entity Recognition
Robustness to capitalization errors is a highly desirable characteristic of named entity recognizers, yet we find standard models for the task are surprisingly brittle to such noise.
Deep Active Learning for Named Entity Recognition
In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning.
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging.
WordRank: Learning Word Embeddings via Robust Ranking
Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses.
A Scalable Asynchronous Distributed Algorithm for Topic Modeling
Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one needs to deal with a large number of topics (typically in the order of thousands).
Ranking via Robust Binary Classification
We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classification.
Distributed Stochastic Optimization of the Regularized Risk
Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task.
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data
We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classification.
NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion
One of the key features of NOMAD is that the ownership of a variable is asynchronously transferred between processors in a decentralized fashion.
Distributed, Parallel, and Cluster Computing
Using Logistic Regression to Analyze the Balance of a Game: The Case of StarCraft II
In online games, usually a human user competes with others, so the fairness of the game system to all users is of great importance not to lose interests of users on the game.
Applications Multimedia
