
Tiering as a Stochastic Submodular Optimization Problem
Tiering is an essential technique for building largescale information r...
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Recognizing Variables from their Data via Deep Embeddings of Distributions
A key obstacle in automated analytics and metalearning is the inability...
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Deep Factors for Forecasting
Producing probabilistic forecasts for large collections of similar and/o...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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Deep Factors with Gaussian Processes for Forecasting
A large collection of time series poses significant challenges for class...
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Deep Graphs
We propose an algorithm for deep learning on networks and graphs. It rel...
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Detecting and Correcting for Label Shift with Black Box Predictors
Faced with distribution shift between training and test set, we wish to ...
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Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Knowledge bases (KB), both automatically and manually constructed, are o...
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Efficient Multitask Feature and Relationship Learning
In this paper we propose a multiconvex framework for multitask learnin...
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Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
We propose a method to optimize the representation and distinguishabilit...
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AIDE: Fast and Communication Efficient Distributed Optimization
In this paper, we present two new communicationefficient methods for di...
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Stochastic FrankWolfe Methods for Nonconvex Optimization
We study FrankWolfe methods for nonconvex stochastic and finitesum opt...
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Neural Machine Translation with Recurrent Attention Modeling
Knowing which words have been attended to in previous time steps while g...
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Fast Stochastic Methods for Nonsmooth Nonconvex Optimization
We analyze stochastic algorithms for optimizing nonconvex, nonsmooth fin...
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Stochastic Variance Reduction for Nonconvex Optimization
We study nonconvex finitesum problems and analyze stochastic variance r...
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Fast Incremental Method for Nonconvex Optimization
We analyze a fast incremental aggregated gradient method for optimizing ...
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Data Driven Resource Allocation for Distributed Learning
In distributed machine learning, data is dispatched to multiple machines...
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Stacked Attention Networks for Image Question Answering
This paper presents stacked attention networks (SANs) that learn to answ...
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On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
We study optimization algorithms based on variance reduction for stochas...
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Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
We consider the problem of Bayesian learning on sensitive datasets and p...
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Deep Fried Convnets
The fully connected layers of a deep convolutional neural network typica...
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Trend Filtering on Graphs
We introduce a family of adaptive estimators on graphs, based on penaliz...
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The Falling Factorial Basis and Its Statistical Applications
We study a novel splinelike basis, which we name the "falling factorial...
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Randomized Nonlinear Component Analysis
Classical methods such as Principal Component Analysis (PCA) and Canonic...
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Exponential Families for Conditional Random Fields
In this paper we de ne conditional random elds in reproducing kernel Hil...
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Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
This paper analyzes the problem of Gaussian process (GP) bandits with de...
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SuperSamples from Kernel Herding
We extend the herding algorithm to continuous spaces by using the kernel...
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Regret Bounds for Deterministic Gaussian Process Bandits
This paper analyses the problem of Gaussian process (GP) bandits with de...
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Alex Smola
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Director, Machine Learning at Amazon, Professor at Carnegie Mellon University