Search Results for author: Judy Hanwen Shen

Found 11 papers, 7 papers with code

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting

1 code implementation17 Jun 2021 Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni

We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while protecting privacy.

Fast and Memory Efficient Differentially Private-SGD via JL Projections

no code implementations NeurIPS 2021 Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat

Unlike previous attempts to make DP-SGD faster which work only on a subset of network architectures or use compiler techniques, we propose an algorithmic solution which works for any network in a black-box manner which is the main contribution of this paper.

Human-centric Dialog Training via Offline Reinforcement Learning

1 code implementation EMNLP 2020 Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu, Rosalind Picard

We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL).

Language Modelling Offline RL +1

Differentially Private Set Union

1 code implementation ICML 2020 Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin

Known algorithms for this problem proceed by collecting a subset of items from each user, taking the union of such subsets, and disclosing the items whose noisy counts fall above a certain threshold.

Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog

no code implementations ICLR 2020 Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard

This is a critical shortcoming for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e. g. systems that learn from human interaction.

OpenAI Gym Open-Domain Dialog +2

Hierarchical Reinforcement Learning for Open-Domain Dialog

1 code implementation17 Sep 2019 Abdelrhman Saleh, Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Rosalind Picard

Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text.

Hierarchical Reinforcement Learning Open-Domain Dialog +1

Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

1 code implementation30 Jun 2019 Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard

Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment.

Open-Domain Dialog Q-Learning +1

Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems

2 code implementations NeurIPS 2019 Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques, Craig Ferguson, Noah Jones, Agata Lapedriza, Rosalind Picard

To investigate the strengths of this novel metric and interactive evaluation in comparison to state-of-the-art metrics and human evaluation of static conversations, we perform extended experiments with a set of models, including several that make novel improvements to recent hierarchical dialog generation architectures through sentiment and semantic knowledge distillation on the utterance level.

Dialogue Evaluation Knowledge Distillation

Comparing Models of Associative Meaning: An Empirical Investigation of Reference in Simple Language Games

1 code implementation CONLL 2018 Judy Hanwen Shen, Matthias Hofer, Bjarke Felbo, Roger Levy

These results shed light on the nature of the lexical resources that speakers and listeners can bring to bear in achieving reference through associative meaning alone.

Detecting Anxiety through Reddit

no code implementations WS 2017 Judy Hanwen Shen, Frank Rudzicz

Previous investigations into detecting mental illnesses through social media have predominately focused on detecting depression through Twitter corpora.

Language Modelling Word Embeddings

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