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06/07/2023
Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems
Large-scale conversational systems typically rely on a skill-routing com...
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05/17/2023
Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems
Off-Policy reinforcement learning has been a driving force for the state...
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09/17/2022
Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems
Recently, self-learning methods based on user satisfaction metrics and c...
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04/14/2022
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems
Skill routing is an important component in large-scale conversational sy...
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04/05/2022
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data
In many real-world machine learning applications, samples belong to a se...
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11/11/2020
Real-Time Decentralized knowledge Transfer at the Edge
Proliferation of edge networks creates islands of learning agents workin...
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10/21/2020
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents
Turn-level user satisfaction is one of the most important performance me...
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12/20/2019
Group-Connected Multilayer Perceptron Networks
Despite the success of deep learning in domains such as image, voice, an...
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12/17/2019
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance
In many real-world machine learning problems, feature values are not rea...
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10/04/2019
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector
Effective modeling of electronic health records presents many challenges...
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09/15/2019
Target-Focused Feature Selection Using a Bayesian Approach
In many real-world scenarios where data is high dimensional, test time a...
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08/11/2019
TAPER: Time-Aware Patient EHR Representation
Effective representation learning of electronic health records is a chal...
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05/22/2019
Generative Imputation and Stochastic Prediction
In many machine learning applications, we are faced with incomplete data...
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02/19/2019
Nutrition and Health Data for Cost-Sensitive Learning
Traditionally, machine learning algorithms have been focused on modeling...
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01/02/2019
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
In many real-world learning scenarios, features are only acquirable at a...
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11/03/2018
Dynamic Feature Acquisition Using Denoising Autoencoders
In real-world scenarios, different features have different acquisition c...
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04/19/2018
