Bridging Gaps Between Metrics and Social Outcomes in Multi-Stakeholder Machine Learning
Serena Lutong Wang [2024]

Reliable Representation Learning: Theory and Practice
Yaodong Yu [2024]

Learning and Decision-Making in Complex Environments
Alexander Wei [2023]

Learning to Design Protein and DNA Libraries
Akosua Busia [2023]

Prediction and Statistical Inference in Feedback Loops
Tijana Zrnic [2023]

Structure-Driven Algorithm Design in Optimization and Machine Learning
Tianyi Lin [2023]

Toward Trustworthy Scientific Inquiry and Design with Machine Learning
Clara Wong-Fannjiang [2023]

Cognitive analyses of machine learning systems
Erin Grant [2022]

Incorporating Intent, Impact, and Context for Beneficial Machine Learning
Esther Rolf [2022]

Learning Beyond the Standard Model (of Data)
Nilesh Tripuraneni [2022]

NumS: Scalable Array Programming for the Cloud
Huseyin Elibol [2022]

Social Dynamics of Machine Learning for Decision Making
Lydia Liu [2022]

The Dynamics of Recommender Systems
Karl Krauth [2022]

Towards Socially and Economically Beneficial Machine Learning
Wenshuo Guo [2022]

Charting Cellular States, One Cell at a Time: Computational, Inferential and Modeling Perspectives
Romain Lopez [2021]

Designing Algorithms for Learning and Decision-Making in Societal Systems
Eric Mazumdar [2021]

Model Selection for Contextual Bandits and Reinforcement Learning
Aldo Pacchiano [2021]

Statistical Complexity and Regret in Linear Control
Max Simchowitz [2021]

Structured Neural Models and Structured Decoding for Natural Language Processing
Mitchell Stern [2020]

The Interplay between Sampling and Optimization
Cheng Xiang [2020]

The sample complexity of simple reinforcement learning
Horia Mania [2020]

Machine Learning: Why Do Simple Algorithms Work So Well?
Chi Jin [2019]

On Systems and Algorithms for Distributed Machine Learning
Robert Nishihara [2019]

Ray: A Distributed Execution Engine for the Machine Learning Ecosystem
Philipp Moritz [2019]

Sharp and Practical Performance Bounds for MCMC and Multiple Testing
Maxim Rabinovich [2019]

Detection limits and fluctuation results in some spiked random matrix models and pooling of discrete data
Ahmed El Alaoui [2018]

Parallel Machine Learning Using Concurrency Control
Xinghao Pan [2017]

System-Aware Optimization for Machine Learning at Scale
Virginia Smith [2017]

Distributed machine learning with communication constraints
Yuchen Zhang [2016]

Variational and Dynamical Perspectives On Learning and Optimization
Andre Wibisono [2016]

Multiple Optimality Guarantees in Statistical Learning
John Duchi [2014]

Safety, Risk Awareness and Exploration in Reinforcement Learning
Teodor Moldovan [2014]

Learning from Subsampled Data: Active and Randomized Strategies
Fabian Wauthier [2013]

Matrix Factorization and Matrix Concentration
Lester Mackey [2012]

Randomized Algorithms for Scalable Machine Learning
Ariel Jacob Kleiner [2012]

Bayesian Nonparametric Latent Feature Models
Kurt Miller [2011]

Incorporating Supervision for Visual Recognition and Segmentation
Alex Yu Jen Shyr [2011]

Learning Dependency-Based Compositional Semantics
Percy Shuo Liang [2011]

Automating Datacenter Operations Using Machine Learning
Peter Bodik [2010]

Computational Methods for Meiotic Recombination Inference
Junming Yin [2010]

Modeling Events in Time Using Cascades Of Poisson Processes
Aleksandr Simma [2010]

Probabilistic Models of Evolution and Language Change
Alexandre Bouchard-Cote [2010]

Statistical models for analyzing human genetic variation
Sriram Sankararaman [2010]

Discriminative Machine Learning with Structure
Simon Lacoste-Julien [2009]

Nonparametric Bayesian Models for Machine Learning
Romain Jean Thibaux [2008]

Resampling Methods for Protein Structure Prediction
Benjamin Norman Blum [2008]

Learning in decentralized systems: A nonparametric approach
Xuanlong Nguyen [2007]

Predicting Protein Molecular Function
Barbara Elizabeth Engelhardt [2007]

A Kinetic Model for G protein-coupled Signal Transduction in Macrophage Cells
Patrick Joseph Flaherty [2006]

Automated Music Analysis Using Dynamic Graphical Models
Brian K. Vogel [2005]

Learning Blind Source Separation
Francis R. Bach [2005]

Statistical Software Debugging
Alice X. Zheng [2005]

Probabilistic Graphical Models and Algorithms for Genomic Analysis
Eric Poe Xing [2004]

Probabilistic Models for Text and Images
David M. Blei [2004]

Shaping and Policy Search in Reinforcement Learning
Andrew Y. Ng [2003]