Search Results for author: John Miller

Found 25 papers, 9 papers with code

Test-Time Training for Generalization under Distribution Shifts

no code implementations ICML 2020 Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, University of California Moritz Hardt

We introduce a general approach, called test-time training, for improving the performance of predictive models when training and test data come from different distributions.

Image Classification Self-Supervised Learning

Neural Borrowing Detection with Monolingual Lexical Models

no code implementations RANLP 2021 John Miller, Emanuel Pariasca, Cesar Beltran Castañon

From there we experiment with several different models and approaches including a lexical donor model with augmented wordlist.

Language Modelling

Multi-User Augmented Reality with Infrastructure-free Collaborative Localization

no code implementations30 Oct 2021 John Miller, Elahe Soltanaghai, Raewyn Duvall, Jeff Chen, Vikram Bhat, Nuno Pereira, Anthony Rowe

In this paper, we present LocAR, an infrastructure-free 6-degrees-of-freedom (6DoF) localization system for AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system.

Retiring Adult: New Datasets for Fair Machine Learning

1 code implementation NeurIPS 2021 Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt

Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning.

Fairness

Improving COVID-19 Forecasting using eXogenous Variables

1 code implementation20 Jul 2021 Mohammadhossein Toutiaee, Xiaochuan Li, Yogesh Chaudhari, Shophine Sivaraja, Aishwarya Venkataraj, Indrajeet Javeri, Yuan Ke, Ismailcem Arpinar, Nicole Lazar, John Miller

We demonstrate significant enhancement in the forecasting accuracy for a COVID-19 dataset, with a maximum improvement in forecasting accuracy by 64. 58% and 59. 18% (on average) over the GCN-LSTM model in the national level data, and 58. 79% and 52. 40% (on average) over the GCN-LSTM model in the state level data.

Mortality Prediction Time Series +1

Outside the Echo Chamber: Optimizing the Performative Risk

no code implementations17 Feb 2021 John Miller, Juan C. Perdomo, Tijana Zrnic

In performative prediction, predictions guide decision-making and hence can influence the distribution of future data.

Decision Making

Gaussian Function On Response Surface Estimation

no code implementations4 Jan 2021 Mohammadhossein Toutiaee, John Miller

We utilize a Gaussian process as a surrogate to capture the response surface of a complex model, in which we incorporate two parts in the process: interpolated values that are modeled by a stationary Gaussian process Z governed by a prior covariance function, and a mean function mu that captures the known trends in the underlying model.

The Effect of Natural Distribution Shift on Question Answering Models

no code implementations ICML 2020 John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt

We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data.

Question Answering

A Meta-Analysis of Overfitting in Machine Learning

no code implementations NeurIPS 2019 Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt

By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition.

Strategic Classification is Causal Modeling in Disguise

no code implementations ICML 2020 John Miller, Smitha Milli, Moritz Hardt

Moreover, we show a similar result holds for designing cost functions that satisfy the requirements of previous work.

Causal Inference Classification +2

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

3 code implementations29 Sep 2019 Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt

In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions.

CARLA MAP Leaderboard Image Classification +3

Test-Time Training for Out-of-Distribution Generalization

no code implementations25 Sep 2019 Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt

We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training data come from different distributions.

Image Classification Out-of-Distribution Generalization +1

Model Similarity Mitigates Test Set Overuse

no code implementations NeurIPS 2019 Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht

Excessive reuse of test data has become commonplace in today's machine learning workflows.

Toward Universal Dependencies for Shipibo-Konibo

no code implementations WS 2018 Alonso Vasquez, Renzo Ego Aguirre, C Angulo, y, John Miller, Claudia Villanueva, {\v{Z}}eljko Agi{\'c}, Roberto Zariquiey, Arturo Oncevay

We present an initial version of the Universal Dependencies (UD) treebank for Shipibo-Konibo, the first South American, Amazonian, Panoan and Peruvian language with a resource built under UD.

Dependency Parsing Machine Translation

The Social Cost of Strategic Classification

no code implementations25 Aug 2018 Smitha Milli, John Miller, Anca D. Dragan, Moritz Hardt

Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule.

Classification Decision Making +2

Stable Recurrent Models

no code implementations ICLR 2019 John Miller, Moritz Hardt

Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks.

Globally Normalized Reader

1 code implementation EMNLP 2017 Jonathan Raiman, John Miller

Rapid progress has been made towards question answering (QA) systems that can extract answers from text.

Data Augmentation Question Answering

Topic Model Stability for Hierarchical Summarization

no code implementations WS 2017 John Miller, Kathleen Mccoy

We envisioned responsive generic hierarchical text summarization with summaries organized by section and paragraph based on hierarchical structure topic models.

Text Summarization Topic Models

Deep Voice 2: Multi-Speaker Neural Text-to-Speech

1 code implementation NeurIPS 2017 Sercan Arik, Gregory Diamos, Andrew Gibiansky, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou

We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1.

Speech Synthesis

Traversing Knowledge Graphs in Vector Space

1 code implementation EMNLP 2015 Kelvin Guu, John Miller, Percy Liang

Path queries on a knowledge graph can be used to answer compositional questions such as "What languages are spoken by people living in Lisbon?".

Knowledge Base Completion Knowledge Graphs

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