
MultipleSource Adaptation with Domain Classifiers
We consider the multiplesource adaptation (MSA) problem and improve a p...
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Beyond Individual and Group Fairness
We present a new datadriven model of fairness that, unlike existing sta...
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Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Federated learning is a challenging optimization problem due to the hete...
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On the Rademacher Complexity of Linear Hypothesis Sets
Linear predictors form a rich class of hypotheses used in a variety of l...
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A Theory of MultipleSource Adaptation with Limited Target Labeled Data
We study multiplesource domain adaptation, when the learner has access ...
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Relative Deviation Margin Bounds
We present a series of new and more favorable marginbased learning guar...
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Corralling Stochastic Bandit Algorithms
We study the problem of corralling stochastic bandit algorithms, that is...
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Reinforcement Learning with Feedback Graphs
We study episodic reinforcement learning in Markov decision processes wh...
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Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Adversarial or test time robustness measures the susceptibility of a cla...
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Three Approaches for Personalization with Applications to Federated Learning
The standard objective in machine learning is to train a single model fo...
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Adaptive RegionBased Active Learning
We present a new active learning algorithm that adaptively partitions th...
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Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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Learning GANs and Ensembles Using Discrepancy
Generative adversarial networks (GANs) generate data based on minimizing...
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SCAFFOLD: Stochastic Controlled Averaging for OnDevice Federated Learning
Federated learning is a key scenario in modern largescale machine learn...
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Bandits with Feedback Graphs and Switching Costs
We study the adversarial multiarmed bandit problem where partial observ...
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AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
AdaNet is a lightweight TensorFlowbased (Abadi et al., 2015) framework ...
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Hypothesis Set Stability and Generalization
We present an extensive study of generalization for datadependent hypot...
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Agnostic Federated Learning
A key learning scenario in largescale applications is that of federated...
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Policy Regret in Repeated Games
The notion of policy regret in online learning is a well defined? perfor...
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Algorithms and Theory for MultipleSource Adaptation
This work includes a number of novel contributions for the multiplesour...
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Online NonAdditive Path Learning under Full and Partial Information
We consider the online path learning problem in a graph with nonadditiv...
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Logistic Regression: The Importance of Being Improper
Learning linear predictors with the logistic lossboth in stochastic a...
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Theory and Algorithms for Forecasting Time Series
We present datadependent learning bounds for the general scenario of no...
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Parameterfree online learning via model selection
We introduce an efficient algorithmic framework for model selection in o...
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Structured Prediction Theory Based on Factor Graph Complexity
We present a general theoretical analysis of structured prediction with ...
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Foundations of Coupled Nonlinear Dimensionality Reduction
In this paper we introduce and analyze the learning scenario of coupled ...
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Accelerating Optimization via Adaptive Prediction
We present a powerful general framework for designing datadependent opt...
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Tight Lower Bound on the Probability of a Binomial Exceeding its Expectation
We give the proof of a tight lower bound on the probability that a binom...
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New Analysis and Algorithm for Learning with Drifting Distributions
We present a new analysis of the problem of learning with drifting distr...
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Multiple Source Adaptation and the Renyi Divergence
This paper presents a novel theoretical study of the general problem of ...
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L2 Regularization for Learning Kernels
The choice of the kernel is critical to the success of many learning alg...
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Algorithms for Learning Kernels Based on Centered Alignment
This paper presents new and effective algorithms for learning kernels. I...
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Ensembles of Kernel Predictors
This paper examines the problem of learning with a finite and possibly l...
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On the Estimation of Coherence
Lowrank matrix approximations are often used to help scale standard mac...
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New Generalization Bounds for Learning Kernels
This paper presents several novel generalization bounds for the problem ...
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