
Multilabel Contrastive Predictive Coding
Variational mutual information (MI) estimators are widely used in unsupe...
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Belief Propagation Neural Networks
Learned neural solvers have successfully been used to solve combinatoria...
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Unsupervised Calibration under Covariate Shift
A probabilistic model is said to be calibrated if its predicted probabil...
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Experience Replay with Likelihoodfree Importance Weights
The use of past experiences to accelerate temporal difference (TD) learn...
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Individual Calibration with Randomized Forecasting
Machine learning applications often require calibrated predictions, e.g....
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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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Predicting Livelihood Indicators from Crowdsourced Street Level Images
Major decisions from governments and other large organizations rely on m...
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Efficient Poverty Mapping using Deep Reinforcement Learning
The combination of highresolution satellite imagery and machine learnin...
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Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
Learning disentangled representations is regarded as a fundamental task ...
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MOPO: Modelbased Offline Policy Optimization
Offline reinforcement learning (RL) refers to the problem of learning po...
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Farmland Parcel Delineation Using Spatiotemporal Convolutional Networks
Farm parcel delineation provides cadastral data that is important in dev...
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Output Diversified Initialization for Adversarial Attacks
Adversarial examples are often constructed by iteratively refining a ran...
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Training Deep EnergyBased Models with fDivergence Minimization
Deep energybased models (EBMs) are very flexible in distribution parame...
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Gaussianization Flows
Iterative Gaussianization is a fixedpoint iteration procedure that can ...
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Predictive Coding for LocallyLinear Control
Highdimensional observations and unknown dynamics are major challenges ...
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Permutation Invariant Graph Generation via ScoreBased Generative Modeling
Learning generative models for graphstructured data is challenging beca...
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Learning When and Where to Zoom with Deep Reinforcement Learning
While high resolution images contain semantically more useful informatio...
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A Theory of Usable Information Under Computational Constraints
We propose a new framework for reasoning about information in complex sy...
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Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation
Feedforward computations, such as evaluating a neural network or samplin...
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Generating Interpretable Poverty Maps using Object Detection in Satellite Images
Accurate locallevel poverty measurement is an essential task for govern...
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Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
Satellite images hold great promise for continuous environmental monitor...
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Efficient Object Detection in Large Images using Deep Reinforcement Learning
Traditionally, an object detector is applied to every part of the scene ...
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Approximating the Permanent by Sampling from Adaptive Partitions
Computing the permanent of a nonnegative matrix is a core problem with ...
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Fair Generative Modeling via Weak Supervision
Realworld datasets are often biased with respect to key demographic fac...
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Bridging the Gap Between fGANs and Wasserstein GANs
Generative adversarial networks (GANs) have enjoyed much success in lear...
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Weakly Supervised Disentanglement with Guarantees
Learning disentangled representations that correspond to factors of vari...
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Unsupervised OutofDistribution Detection with Batch Normalization
Likelihood from a generative model is a natural statistic for detecting ...
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Understanding the Limitations of Variational Mutual Information Estimators
Variational approaches based on neural networks are showing promise for ...
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Cross Domain Imitation Learning
We study the question of how to imitate tasks across domains with discre...
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MetaInverse Reinforcement Learning with Probabilistic Context Variables
Providing a suitable reward function to reinforcement learning can be di...
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Temporal FiLM: Capturing LongRange Sequence Dependencies with FeatureWise Modulations
Learning representations that accurately capture longrange dependencies...
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MultiAgent Adversarial Inverse Reinforcement Learning
Reinforcement learning agents are prone to undesired behaviors due to re...
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MintNet: Building Invertible Neural Networks with Masked Convolutions
We propose a new way of constructing invertible neural networks by combi...
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Generative Modeling by Estimating Gradients of the Data Distribution
We introduce a new generative model where samples are produced via Lange...
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Bias Correction of Learned Generative Models using LikelihoodFree Importance Weighting
A learned generative model often produces biased statistics relative to ...
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Calibrated ModelBased Deep Reinforcement Learning
Estimates of predictive uncertainty are important for accurate modelbas...
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Learning Neural PDE Solvers with Convergence Guarantees
Partial differential equations (PDEs) are widely used across the physica...
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AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given unpaired data from multiple domains, a key challenge is to efficie...
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Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Score matching is a popular method for estimating unnormalized statistic...
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Learning to Interpret Satellite Images in Global Scale Using Wikipedia
Despite recent progress in computer vision, finegrained interpretation o...
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Predicting Economic Development using Geolocated Wikipedia Articles
Progress on the UN Sustainable Development Goals (SDGs) is hampered by a...
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Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
Millions of people worldwide are absent from their country's census. Acc...
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Distributed generation of privacy preserving data with user customization
Distributed devices such as mobile phones can produce and store large am...
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Stochastic Optimization of Sorting Networks via Continuous Relaxations
Sorting input objects is an important step in many machine learning pipe...
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Training Variational Autoencoders with Buffered Stochastic Variational Inference
The recognition network in deep latent variable models such as variation...
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MetaAmortized Variational Inference and Learning
How can we learn to do probabilistic inference in a way that generalizes...
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Differentiable Subset Sampling
Many machine learning tasks require sampling a subset of items from a co...
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Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
Rapid identification of bacteria is essential to prevent the spread of i...
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Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
The goal of statistical compressive sensing is to efficiently acquire an...
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Learning Controllable Fair Representations
Learning data representations that are transferable and fair with respec...
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Stefano Ermon
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Assistant Professor, Department of Computer Science Fellow, Woods Institute for the Environment at Stanford University