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11/17/2022
Online Distribution Shift Detection via Recency Prediction
When deploying modern machine learning-enabled robotic systems in high-s...
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03/02/2022
Low-Degree Multicalibration
Introduced as a notion of algorithmic fairness, multicalibration has pro...
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09/28/2021
Sample-Efficient Safety Assurances using Conformal Prediction
When deploying machine learning models in high-stakes robotics applicati...
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07/12/2021
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
When facing uncertainty, decision-makers want predictions they can trust...
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02/22/2021
Localized Calibration: Metrics and Recalibration
Probabilistic classifiers output confidence scores along with their pred...
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11/15/2020
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
Decision makers often need to rely on imperfect probabilistic forecasts....
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08/21/2020
Privacy Preserving Recalibration under Domain Shift
Classifiers deployed in high-stakes real-world applications must output ...
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06/18/2020
Individual Calibration with Randomized Forecasting
Machine learning applications often require calibrated predictions, e.g....
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06/18/2020
A Framework for Sample Efficient Interval Estimation with Control Variates
We consider the problem of estimating confidence intervals for the mean ...
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03/02/2020
Permutation Invariant Graph Generation via Score-Based Generative Modeling
Learning generative models for graph-structured data is challenging beca...
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02/25/2020
A Theory of Usable Information Under Computational Constraints
We propose a new framework for reasoning about information in complex sy...
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09/30/2019
Cross Domain Imitation Learning
We study the question of how to imitate tasks across domains with discre...
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06/04/2019
Learning Neural PDE Solvers with Convergence Guarantees
Partial differential equations (PDEs) are widely used across the physica...
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12/11/2018
Learning Controllable Fair Representations
Learning data representations that are transferable and fair with respec...
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11/08/2018
Bias and Generalization in Deep Generative Models: An Empirical Study
In high dimensional settings, density estimation algorithms rely crucial...
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06/18/2018
The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models
A variety of learning objectives have been proposed for training latent ...
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05/23/2018
Amortized Inference Regularization
The variational autoencoder (VAE) is a popular model for density estimat...
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06/23/2017
A-NICE-MC: Adversarial Training for MCMC
Existing Markov Chain Monte Carlo (MCMC) methods are either based on gen...
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06/07/2017
InfoVAE: Information Maximizing Variational Autoencoders
It has been previously observed that variational autoencoders tend to ig...
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03/07/2017
On the Limits of Learning Representations with Label-Based Supervision
Advances in neural network based classifiers have transformed automatic ...
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02/28/2017
Towards Deeper Understanding of Variational Autoencoding Models
We propose a new family of optimization criteria for variational auto-en...
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02/27/2017