
InformationTheoretic Bounds for Integral Estimation
In this paper, we consider a zeroorder stochastic oracle model of estim...
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Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem
In this paper, we study the problem of fair sparse regression on a biase...
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A Simple Unified Framework for High Dimensional Bandit Problems
Stochastic high dimensional bandit problems with low dimensional structu...
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On the Fundamental Limits of Exact Inference in Structured Prediction
Inference is a main task in structured prediction and it is naturally mo...
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A Thorough View of Exact Inference in Graphs from the Degree4 SumofSquares Hierarchy
Performing inference in graphs is a common task within several machine l...
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Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees
Inverse Reinforcement Learning (IRL) is the problem of finding a reward ...
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Information Theoretic Limits of Exact Recovery in Subhypergraph Models for Community Detection
In this paper, we study the information theoretic bounds for exact recov...
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Randomized Deep Structured Prediction for DiscourseLevel Processing
Expressive text encoders such as RNNs and Transformer Networks have been...
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PrivSyn: Differentially Private Data Synthesis
In differential privacy (DP), a challenging problem is to generate synth...
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Information Theoretic Sample Complexity Lower Bound for FeedForward FullyConnected Deep Networks
In this paper, we study the sample complexity lower bound of a dlayer f...
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Fundamental Limits of Adversarial Learning
Robustness of machine learning methods is essential for modern practical...
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Fairness constraints can help exact inference in structured prediction
Many inference problems in structured prediction can be modeled as maxim...
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Support Union Recovery in Meta Learning of Gaussian Graphical Models
In this paper we study Meta learning of Gaussian graphical models. In ou...
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Exact Support Recovery in Federated Regression with Oneshot Communication
Federated learning provides a framework to address the challenges of dis...
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Exact Partitioning of Highorder Planted Models with a Tensor Nuclear Norm Constraint
We study the problem of efficient exact partitioning of the hypergraphs ...
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Provable Sample Complexity Guarantees for Learning of ContinuousAction Graphical Games with Nonparametric Utilities
In this paper, we study the problem of learning the exact structure of c...
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InformationTheoretic Lower Bounds for ZeroOrder Stochastic Gradient Estimation
In this paper we analyze the necessary number of samples to estimate the...
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First Order Methods take Exponential Time to Converge to Global Minimizers of NonConvex Functions
Machine learning algorithms typically perform optimization over a class ...
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Novel Change of Measure Inequalities and PACBayesian Bounds
PACBayesian theory has received a growing attention in the machine lear...
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The Sample Complexity of Meta Sparse Regression
This paper addresses the metalearning problem in sparse linear regressi...
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Provable Computational and Statistical Guarantees for Efficient Learning of ContinuousAction Graphical Games
In this paper, we study the problem of learning the set of pure strategy...
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Exact Partitioning of Highorder Models with a Novel Convex Tensor Cone Relaxation
In this paper we propose the first correct polytime algorithm for exact...
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Direct Estimation of Difference Between Structural Equation Models in High Dimensions
Discovering causeeffect relationships between variables from observatio...
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Exact inference in structured prediction
Structured prediction can be thought of as a simultaneous prediction of ...
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Minimax bounds for structured prediction
Structured prediction can be considered as a generalization of many stan...
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On the Correctness and Sample Complexity of Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) is the problem of finding a reward ...
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Learning Bayesian Networks with Low Rank Conditional Probability Tables
In this paper, we provide a method to learn the directed structure of a ...
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On the Statistical Efficiency of Optimal Kernel Sum Classifiers
We propose a novel combination of optimization tools with learning theor...
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Information Theoretic Limits for Standard and OneBit Compressed Sensing with GraphStructured Sparsity
In this paper, we analyze the information theoretic lower bound on the n...
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Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression
In this paper, we propose a random projection approach to estimate varia...
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Learning latent variable structured prediction models with Gaussian perturbations
The standard marginbased structured prediction commonly uses a maximum ...
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Learning MaximumAPosteriori Perturbation Models for Structured Prediction in Polynomial Time
MAP perturbation models have emerged as a powerful framework for inferen...
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Regularized Loss Minimizers with Local Data Obfuscation
While data privacy has been studied for more than a decade, it is still ...
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Learning Binary Bayesian Networks in Polynomial Time and Sample Complexity
We consider the problem of structure learning for binary Bayesian networ...
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Informationtheoretic Limits for Community Detection in Network Models
We analyze the informationtheoretic limits for the recovery of node lab...
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On the Sample Complexity of Learning from a Sequence of Experiments
We analyze the sample complexity of a new problem: learning from a seque...
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The Error Probability of Random Fourier Features is Dimensionality Independent
We show that the error probability of reconstructing kernel matrices fro...
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Learning linear structural equation models in polynomial time and sample complexity
The problem of learning structural equation models (SEMs) from data is a...
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Learning causal Bayes networks using interventional path queries in polynomial time and sample complexity
Causal discovery from empirical data is a fundamental problem in many sc...
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On the Statistical Efficiency of Compositional Nonparametric Prediction
In this paper, we propose a compositional nonparametric method in which ...
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Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Learning the directed acyclic graph (DAG) structure of a Bayesian networ...
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Information Theoretic Limits for Linear Prediction with GraphStructured Sparsity
We analyze the necessary number of samples for sparse vector recovery in...
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From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria
In this paper we study the problem of exact recovery of the purestrateg...
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InformationTheoretic Lower Bounds for Recovery of Diffusion Network Structures
We study the informationtheoretic lower bound of the sample complexity ...
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Informationtheoretic limits of Bayesian network structure learning
In this paper, we study the informationtheoretic limits of learning the...
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On the Sample Complexity of Learning Graphical Games
We analyze the sample complexity of learning graphical games from purely...
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Structured Prediction: From Gaussian Perturbations to LinearTime Principled Algorithms
Marginbased structured prediction commonly uses a maximum loss over all...
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Inverse Covariance Estimation for HighDimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models
We propose maximum likelihood estimation for learning Gaussian graphical...
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On the Statistical Efficiency of ℓ_1,p MultiTask Learning of Gaussian Graphical Models
In this paper, we present ℓ_1,p multitask structure learning for Gaussi...
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Lipschitz Parametrization of Probabilistic Graphical Models
We show that the loglikelihood of several probabilistic graphical model...
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