Search Results for author: Izzeddin Gur

Found 14 papers, 3 papers with code

Environment Generation for Zero-Shot Compositional Reinforcement Learning

1 code implementation NeurIPS 2021 Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust

We learn to generate environments composed of multiple pages or rooms, and train RL agents capable of completing wide-range of complex tasks in those environments.

reinforcement-learning

Targeted Environment Design from Offline Data

no code implementations29 Sep 2021 Izzeddin Gur, Ofir Nachum, Aleksandra Faust

We formalize our approach as offline targeted environment design(OTED), which automatically learns a distribution over simulator parameters to match a provided offline dataset, and then uses the learned simulator to train an RL agent in standard online fashion.

Offline RL

SparseDice: Imitation Learning for Temporally Sparse Data via Regularization

no code implementations ICML Workshop URL 2021 Alberto Camacho, Izzeddin Gur, Marcin Lukasz Moczulski, Ofir Nachum, Aleksandra Faust

We are concerned with a setting where the demonstrations comprise only a subset of state-action pairs (as opposed to the whole trajectories).

Imitation Learning

Adversarial Environment Generation for Learning to Navigate the Web

1 code implementation2 Mar 2021 Izzeddin Gur, Natasha Jaques, Kevin Malta, Manoj Tiwari, Honglak Lee, Aleksandra Faust

The regret objective trains the adversary to design a curriculum of environments that are "just-the-right-challenge" for the navigator agents; our results show that over time, the adversary learns to generate increasingly complex web navigation tasks.

Decision Making

Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques

no code implementations24 Nov 2020 Jihyeon Lee, Joseph Z. Xu, Kihyuk Sohn, Wenhan Lu, David Berthelot, Izzeddin Gur, Pranav Khaitan, Ke-Wei, Huang, Kyriacos Koupparis, Bernhard Kowatsch

To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected.

Disaster Response Humanitarian

Learning to Navigate the Web

no code implementations ICLR 2019 Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust, Dilek Hakkani-Tur

Even though recent approaches improve the success rate on relatively simple environments with the help of human demonstrations to guide the exploration, they still fail in environments where the set of possible instructions can reach millions.

Meta-Learning reinforcement-learning

User Modeling for Task Oriented Dialogues

no code implementations11 Nov 2018 Izzeddin Gur, Dilek Hakkani-Tur, Gokhan Tur, Pararth Shah

We further develop several variants by utilizing a latent variable model to inject random variations into user responses to promote diversity in simulated user responses and a novel goal regularization mechanism to penalize divergence of user responses from the initial user goal.

Dialogue State Tracking Task-Oriented Dialogue Systems

What It Takes to Achieve 100\% Condition Accuracy on WikiSQL

no code implementations EMNLP 2018 Semih Yavuz, Izzeddin Gur, Yu Su, Xifeng Yan

The SQL queries in WikiSQL are simple: Each involves one relation and does not have any join operation.

Translation

DialSQL: Dialogue Based Structured Query Generation

no code implementations ACL 2018 Izzeddin Gur, Semih Yavuz, Yu Su, Xifeng Yan

The recent advance in deep learning and semantic parsing has significantly improved the translation accuracy of natural language questions to structured queries.

Semantic Parsing Translation

Recovering Question Answering Errors via Query Revision

no code implementations EMNLP 2017 Semih Yavuz, Izzeddin Gur, Yu Su, Xifeng Yan

The existing factoid QA systems often lack a post-inspection component that can help models recover from their own mistakes.

Question Answering Semantic Parsing

Accurate Supervised and Semi-Supervised Machine Reading for Long Documents

no code implementations EMNLP 2017 Daniel Hewlett, Llion Jones, Alex Lacoste, re, Izzeddin Gur

We also evaluate the model in a semi-supervised setting by downsampling the WikiReading training set to create increasingly smaller amounts of supervision, while leaving the full unlabeled document corpus to train a sequence autoencoder on document windows.

Question Answering Reading Comprehension

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