
Optimal Mean Estimation without a Variance
We study the problem of heavytailed mean estimation in settings where t...
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PostSelection Inference via Algorithmic Stability
Modern approaches to data analysis make extensive use of datadriven mod...
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Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations
Reinforcement learning (RL) algorithms combined with modern function app...
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Do Offline Metrics Predict Online Performance in Recommender Systems?
Recommender systems operate in an inherently dynamical setting. Past rec...
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Efficient Methods for Structured NonconvexNonconcave MinMax Optimization
The use of minmax optimization in adversarial training of deep neural n...
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Resource Allocation in Multiarmed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism
We study exploration in stochastic multiarmed bandits when we have acce...
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Learning Strategies in Decentralized Matching Markets under Uncertain Preferences
We study twosided decentralized matching markets in which participants ...
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Uncertainty Sets for Image Classifiers using Conformal Prediction
Convolutional image classifiers can achieve high predictive accuracy, bu...
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Learning from eXtreme Bandit Feedback
We study the problem of batch learning from bandit feedback in the setti...
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Exploration in twostage recommender systems
Twostage recommender systems are widely adopted in industry due to thei...
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ROOTSGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm
The theory and practice of stochastic optimization has focused on stocha...
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On Localized Discrepancy for Domain Adaptation
We propose the discrepancybased generalization theories for unsupervise...
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Covariance estimation with nonnegative partial correlations
We study the problem of highdimensional covariance estimation under the...
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Transferable Calibration with Lower Bias and Variance in Domain Adaptation
Domain Adaptation (DA) enables transferring a learning machine from a la...
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Optimal Robust Linear Regression in Nearly Linear Time
We study the problem of highdimensional robust linear regression where ...
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Finding Equilibrium in MultiAgent Games with Payoff Uncertainty
We study the problem of finding equilibrium strategies in multiagent ga...
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Manifold Learning via Manifold Deflation
Nonlinear dimensionality reduction methods provide a valuable means to v...
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Accelerated Message Passing for EntropyRegularized MAP Inference
Maximum a posteriori (MAP) inference in discretevalued Markov random fi...
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On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
Optimal transport (OT) distances are increasingly used as loss functions...
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On the Theory of Transfer Learning: The Importance of Task Diversity
We provide new statistical guarantees for transfer learning via represen...
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Active Learning for Nonlinear System Identification with Guarantees
While the identification of nonlinear dynamical systems is a fundamental...
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Projection Robust Wasserstein Distance and Riemannian Optimization
Projection robust Wasserstein (PRW) distance, or Wasserstein projection ...
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Instability, Computational Efficiency and Statistical Accuracy
Many statistical estimators are defined as the fixed point of a datadep...
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Lower bounds in multiple testing: A framework based on derandomized proxies
The large bulk of work in multiple testing has focused on specifying pro...
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Mechanism Design with Bandit Feedback
We study a multiround welfaremaximising mechanism design problem, wher...
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On Learning Rates and Schrödinger Operators
The learning rate is perhaps the single most important parameter in the ...
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On Dissipative Symplectic Integration with Applications to GradientBased Optimization
Continuoustime dynamical systems have proved useful in providing concep...
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On Linear Stochastic Approximation: Finegrained PolyakRuppert and NonAsymptotic Concentration
We undertake a precise study of the asymptotic and nonasymptotic proper...
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Is Temporal Difference Learning Optimal? An InstanceDependent Analysis
We address the problem of policy evaluation in discounted Markov decisio...
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PostEstimation Smoothing: A Simple Baseline for Learning with Side Information
Observational data are often accompanied by natural structural indices, ...
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Robustness Guarantees for Mode Estimation with an Application to Bandits
Mode estimation is a classical problem in statistics with a wide range o...
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Optimization with Momentum: Dynamical, ControlTheoretic, and Symplectic Perspectives
We analyze the convergence rate of various momentumbased optimization a...
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Provable MetaLearning of Linear Representations
Metalearning, or learningtolearn, seeks to design algorithms that can...
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On Thompson Sampling with Langevin Algorithms
Thompson sampling is a methodology for multiarmed bandit problems that ...
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FiniteTime LastIterate Convergence for MultiAgent Learning in Games
We consider multiagent learning via online gradient descent (OGD) in a ...
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Robust Optimization for Fairness with Noisy Protected Groups
Many existing fairness criteria for machine learning involve equalizing ...
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DecisionMaking with AutoEncoding Variational Bayes
To make decisions based on a model fit by AutoEncoding Variational Baye...
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Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Adaptivity is an important yet understudied property in modern optimiza...
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Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms
We study the fixedsupport Wasserstein barycenter problem (FSWBP), whic...
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FixedSupport Wasserstein Barycenters: Computational Hardness and Fast Algorithm
We study the fixedsupport Wasserstein barycenter problem (FSWBP), whic...
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Computational Hardness and Fast Algorithm for FixedSupport Wasserstein Barycenter
We study in this paper the fixedsupport Wasserstein barycenter problem ...
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NearOptimal Algorithms for Minimax Optimization
This paper resolves a longstanding open question pertaining to the desig...
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Variance Reduction with Sparse Gradients
Variance reduction methods such as SVRG and SpiderBoost use a mixture of...
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A ControlTheoretic Perspective on Optimal HighOrder Optimization
In this paper, we provide a controltheoretic perspective on optimal ten...
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Sampling for Bayesian Mixture Models: MCMC with PolynomialTime Mixing
We study the problem of sampling from the power posterior distribution i...
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The Power of Batching in Multiple Hypothesis Testing
One important partition of algorithms for controlling the false discover...
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On the Complexity of Approximating Multimarginal Optimal Transport
We study the complexity of approximating the multimarginal optimal trans...
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Towards Understanding the Transferability of Deep Representations
Deep neural networks trained on a wide range of datasets demonstrate imp...
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A Diffusion Process Perspective on Posterior Contraction Rates for Parameters
We show that diffusion processes can be exploited to study the posterior...
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HighOrder Langevin Diffusion Yields an Accelerated MCMC Algorithm
We propose a Markov chain Monte Carlo (MCMC) algorithm based on thirdor...
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Michael I. Jordan
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Michael Irwin Jordan is an american scientist, professor in machine learning, statistical science and artificial intelligence at the University of California, and researcher in Berkeley. He is one of the leading figures in machine learning, and Science has reported him as the most important computer scientist in the world in 2016.
In 1978, Jordan received his BS magna cum laude degree in Psychology from Louisiana State University, his MS degree in Mathematics from Arizona State University in 1980 and his PhD in cognitive science from the University of California in San Diego in 1985. Jordan was a student of David Rumelhart and a member of the PDP Group in the 1980s at the University of California, San Diego.
Jordan currently is a full professor, working in the Department of Statistics and the Department of EECS at the University of California, Berkeley. From 1988 to 1998 he was professor in the Brain and Cognitive Sciences Department at MIT.
Jordan began to develop recurrent neural networks as a cognitive model in the 1980s. In recent years, his work has been less driven by a cognitive point of view and more by traditional statistics.
In the machinelearning community, Jordan popularized Bayesian networks and is known for pointing out links between machine learning and statistics. He was also prominent in formalizing variation methods for approximate inference and popularizing the machine learning expectative maximization algorithm.
In 2001, Jordan and others resigned from the Machine Learning editorial board. They advocated less restrictive access in a public letter and committed support to a new open access newspaper, The Journal of Machine Learning Research, created by Leslie Kaelbling to support the development of machine learning.
Jordan has earned numerous awards, including the ACM  AAAI Allen Newell Award, the IEEE Pioneer Award for Neural Networks, and the NSF Young Investigator Award. This is a prize for the best paper award at the International Conference on Machine Learn. In 2010 he was appointed a Fellow for “contributions to the theory and application of machine training” in the Association for Machinery for Computing Machinery. Jordan belongs to the National Academy of Science, to the National Academy of Engineering and to the Academy of Arts and Sciences in the US.
He was named a Neyman lecturer and an Institute of Mathematical Statistics medallion lecturer. In 2015 he was awarded the David E. Rumelhart Prize and in 2009 received the ACM/AAAI Allen Newell Award.
In 2016 Jordan was identified by an analysis of published literature by the Semantic Scholar Project as the “most influential computer scientist.”