Dean P. Foster

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Spectral dimensionality reduction for HMMs pdf

Dean P. Foster, Jordan Rodu, Lyle H. Ungar, 2012, ArXiv.

Cost and Trust Issues in On-Line Auctions

David C. Parkes, Dean P. Foster, Lyle H. Ungar, 1998 .

Using Regression for Spectral Estimation of HMMs

Dean P. Foster, Jordan Rodu, Lyle H. Ungar, 2013, SLSP.

On Optimal Retirement

Dean P. Foster, Larry A. Shepp, Philip A. Ernst, 2014, J. Appl. Probab..

Competitive algorithms for layered graph traversal

Yuval Rabani, Amos Fiat, Dean P. Foster, 1991, [1991] Proceedings 32nd Annual Symposium of Foundations of Computer Science.

Faster Ridge Regression via the Subsampled Randomized Hadamard Transform

Dean P. Foster, Lyle H. Ungar, Yichao Lu, 2013, NIPS.

Basic Business Statistics - A Casebook

Dean P. Foster, Robert A. Stine, Richard P. Waterman, 1997 .

New Subsampling Algorithms for Fast Least Squares Regression

Dean P. Foster, Lyle H. Ungar, Yichao Lu, 2013, NIPS.

What are the Statistical Limits of Offline RL with Linear Function Approximation? pdf

Ruosong Wang, Sham M. Kakade, Dean P. Foster, 2020, ICLR.

New Insights from Coarse Word Sense Disambiguation in the Crowd

Dean P. Foster, Lyle H. Ungar, Hansen Andrew Schwartz, 2012, COLING.

Deterministic calibration and Nash equilibrium

Sham M. Kakade, Dean P. Foster, S. Kakade, 2008, J. Comput. Syst. Sci..

Feature Selection using Multiple Streams

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2010, AISTATS.

Competitive Algorithms for Layered Graph Traversal

Sundar Vishwanathan, Yuval Rabani, Amos Fiat, 1998, SIAM J. Comput..

Experiments with Spectral Learning of Latent-Variable PCFGs

Karl Stratos, Shay B. Cohen, Michael Collins, 2013, HLT-NAACL.

Brain Imaging and Brain Privacy: A Realistic Concern?

Dean P. Foster, Martha J. Farah, M. Elizabeth Smith, 2009, Journal of Cognitive Neuroscience.

Probabilistic Analysis of a Heuristics for the Dual Bin Packing Problem

Dean P. Foster, Rakesh V. Vohra, 1989, Inf. Process. Lett..

Invariances and Data Augmentation for Supervised Music Transcription

Zaïd Harchaoui, Sham M. Kakade, Dean P. Foster, 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Dynamic Local Regret for Non-convex Online Forecasting

Dean P. Foster, Sergül Aydöre, Tianhao Zhu, 2019, NeurIPS.

Reply to Professor Clemen

Dean P. Foster, Rakesh V. Vohra, 1993, Oper. Res..

Adaptive Monotone Shrinkage for Regression

Dean P. Foster, Zhuang Ma, Robert A. Stine, 2014, UAI.

Business Analysis Using Regression - A Casebook

Dean P. Foster, Robert A. Stine, Richard P. Waterman, 1997 .

Designing Tests for Better Comparisons

Dean P. Foster, Robert A. Stine, Richard P. Waterman, 1997 .

An Axiomatic Characterization of a Class of Locations in Tree Networks

Dean P. Foster, Rakesh V. Vohra, 1998, Oper. Res..

Impartial Predictive Modeling: Ensuring Fairness in Arbitrary Models

Dean P. Foster, Robert A. Stine, Kory D. Johnson, 2016 .

Statistical Summaries of Data

Dean P. Foster, Robert A. Stine, Richard P. Waterman, 1997 .

Clustering Methods for Collaborative Filtering

Dean P. Foster, Lyle H. Ungar, L. Ungar, 1998, AAAI 1998.

A Randomization Rule for Selecting Forecasts

Dean P. Foster, Rakesh V. Vohra, R. Vohra, 1993, Oper. Res..

Introduction to Learning in Games: A Symposium in Honor of David Blackwell

David K. Levine, Dean P. Foster, Rakesh V. Vohra, 1999 .

Learning, hypothesis testing, and Nash equilibrium

H. Peyton Young, Dean P. Foster, H. Young, 2003, Games Econ. Behav..

A proposal for learning by ontological leaps

Dean P. Foster, Lyle H. Ungar, Lyle H. Ungar, 2002 .

Two Step CCA: A new spectral method for estimating vector models of words

Dean P. Foster, Jordan Rodu, Lyle H. Ungar, 2012, ICML 2012.

Eigenwords: spectral word embeddings

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2015, J. Mach. Learn. Res..

Efficient Feature Selection in the Presence of Multiple Feature Classes

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Optimal Weighting of Multi-View Data with Low Dimensional Hidden States pdf

Dean P. Foster, Yichao Lu, 2012, ArXiv.

Characterizing the generalization performance of model selection strategies

Dale Schuurmans, Dean P. Foster, Lyle H. Ungar, 1997, ICML.

Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis

Dean P. Foster, Yichao Lu, Zhuang Ma, 2015, ICML.

Information Consistency of Nonparametric Gaussian Process Methods

Sham M. Kakade, Dean P. Foster, Matthias W. Seeger, 2008, IEEE Transactions on Information Theory.

No Internal Regret via Neighborhood Watch

Dean P. Foster, Alexander Rakhlin, 2012, AISTATS.

Spectral learning of latent-variable PCFGs: algorithms and sample complexity

Karl Stratos, Shay B. Cohen, Michael Collins, 2014, J. Mach. Learn. Res..

Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent

Dean P. Foster, Yichao Lu, 2014, UAI.

Domain Adaptation: A Small Sample Statistical Approach

Ruslan Salakhutdinov, Sham M. Kakade, Dean P. Foster, 2012, AISTATS.

Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2009, ECML/PKDD.

Fitting High-Dimensional Interaction Models with Error Control

Dean P. Foster, Robert A. Stine, Kory D. Johnson, 2015 .

Multi-view Regression Via Canonical Correlation Analysis

Sham M. Kakade, Dean P. Foster, S. Kakade, 2007, COLT.

Online Sparse Linear Regression

Dean P. Foster, Howard J. Karloff, Satyen Kale, 2016, COLT.

Neural Tree Transducers for Tree to Tree Learning

Dean P. Foster, Lyle H. Ungar, João Sedoc, 2018 .

Semantic Word Clusters Using Signed Spectral Clustering

Dean P. Foster, Lyle H. Ungar, João Sedoc, 2017, ACL.

A Formal Statistical Approach to Collaborative Filtering

Dean P. Foster, Lyle H. Ungar, 1998 .

Spectral Learning of Latent-Variable PCFGs

Karl Stratos, Shay B. Cohen, Michael Collins, 2012, ACL.

Deterministic Calibration and Nash Equilibrium

Sham M. Kakade, Dean P. Foster, S. Kakade, 2004, COLT.

Multi-View Learning of Word Embeddings via CCA

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2011, NIPS.

Calibration via Regression

Sham M. Kakade, Dean P. Foster, 2006, 2006 IEEE Information Theory Workshop - ITW '06 Punta del Este.

A risk comparison of ordinary least squares vs ridge regression

Sham M. Kakade, Dean P. Foster, Lyle H. Ungar, 2011, J. Mach. Learn. Res..

Variable Selection is Hard pdf

Dean P. Foster, Howard J. Karloff, Justin Thaler, 2014, COLT.

Multiscale Hidden Markov Models For Covariance Prediction

Dean P. Foster, Jordan Rodu, Lyle H. Ungar, 2018 .

One-shot learning and big data with n=2

Dean P. Foster, Lee H. Dicker, 2013, NIPS.

Local Asymptotic Coding and the Minimum Description Length

Dean P. Foster, Robert A. Stine, 1999, IEEE Trans. Inf. Theory.

Domain Adaptation: Overfitting and Small Sample Statistics pdf

Ruslan Salakhutdinov, Sham M. Kakade, Dean P. Foster, 2011, ArXiv.

Smooth calibration, leaky forecasts, finite recall, and Nash dynamics

Sergiu Hart, Dean P. Foster, 2018, Games Econ. Behav..

Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation pdf

Anima Anandkumar, Sham M. Kakade, Dean P. Foster, 2012, NIPS 2012.

PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing pdf

Eric Horvitz, Stefano Tessaro, Tadayoshi Kohno, 2020, IEEE Data Eng. Bull..

A Local Regret in Nonconvex Online Learning pdf

Sergül Aydöre, Dean P. Foster, Lee H. Dicker, 2018, ArXiv.

Accounting for Cognitive Costs in On-Line Auction Design

David C. Parkes, Dean P. Foster, Lyle H. Ungar, 1998, AMET.

Domain Adaptation with Coupled Subspaces

John Blitzer, Sham M. Kakade, Dean P. Foster, 2011, AISTATS.

Streaming Feature Selection using IIC

Jing Zhou, Dean P. Foster, Lyle H. Ungar, 2005, AISTATS.

Universal codes for finite sequences of integers drawn from a monotone distribution

Dean P. Foster, Robert A. Stine, Abraham J. Wyner, 2002, IEEE Trans. Inf. Theory.

Spectral Dependency Parsing with Latent Variables

Michael Collins, Dean P. Foster, Jordan Rodu, 2012, EMNLP-CoNLL.

Complexity-Based Approach to Calibration with Checking Rules

Ambuj Tewari, Dean P. Foster, Karthik Sridharan, 2011, COLT.

Statistical Language and Speech Processing

Yoshua Bengio, Horia Cucu, Corneliu Burileanu, 2013, Lecture Notes in Computer Science.

Minimum Description Length Penalization for Group and Multi-Task Sparse Learning

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2011, J. Mach. Learn. Res..

Transfer Learning Using Feature Selection pdf

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2009, ArXiv.

Large Scale Canonical Correlation Analysis with Iterative Least Squares

Dean P. Foster, Yichao Lu, 2014, NIPS.

Worst-Case Bounds for Gaussian Process Models

Sham M. Kakade, Dean P. Foster, Matthias W. Seeger, 2005, NIPS.

Spectral Learning Algorithms for Natural Language Processing

Karl Stratos, Shay B. Cohen, Michael Collins, 2013, HLT-NAACL.

A Spectral Algorithm for Latent Dirichlet Allocation

Anima Anandkumar, Sham M. Kakade, Dean P. Foster, 2012, Algorithmica.

A New Approach to Lexical Disambiguation of Arabic Text

Mark Liberman, Dean P. Foster, Lyle H. Ungar, 2010, EMNLP.

Partial Monitoring - Classification, Regret Bounds, and Algorithms

Csaba Szepesvári, Dean P. Foster, Alexander Rakhlin, 2014, Math. Oper. Res..

Overview and Foundations

Dean P. Foster, Robert A. Stine, Richard P. Waterman, 1997 .

Stochastic Convex Optimization with Bandit Feedback

Sham M. Kakade, Dean P. Foster, Alekh Agarwal, 2011, SIAM J. Optim..

Stochastic Evolutionary Game Dynamics

H. Peyton Young, Dean P. Foster, 1990 .

Streamwise Feature Selection

Jing Zhou, Dean P. Foster, Lyle H. Ungar, 2006, J. Mach. Learn. Res..

A Level-set Hit-and-run Sampler for Quasi-Concave Distributions

Dean P. Foster, Shane T. Jensen, 2014, AISTATS.

Streaming feature selection using alpha-investing

Jing Zhou, Dean P. Foster, Lyle H. Ungar, 2005, KDD '05.

VIF Regression: A Fast Regression Algorithm for Large Data

Dean P. Foster, Dongyu Lin, Dongyu Lin, 2009, 2009 Ninth IEEE International Conference on Data Mining.

Using CCA to improve CCA: A new spectral method for estimating vector models of words

Dean P. Foster, Jordan Rodu, Lyle H. Ungar, 2012, ICML.

Unsupervised Distance Metric Learning Using Predictability

Dean P. Foster, Lyle H. Ungar, Abhishek A. Gupta, 2008 .

Semantic Word Clusters Using Signed Normalized Graph Cuts pdf

Dean P. Foster, Lyle H. Ungar, João Sedoc, 2016, ArXiv.

Coupled Recurrent Models for Polyphonic Music Composition

Zaïd Harchaoui, Sham M. Kakade, Dean P. Foster, 2019, ISMIR.

Threshold Martingales and the Evolution of Forecasts pdf

Dean P. Foster, Robert A. Stine, 2021, ArXiv.

On Submodular Contextual Bandits pdf

Dean P. Foster, Alexander Rakhlin, 2021, ArXiv.

Forecast Hedging and Calibration

Sergiu Hart, Dean P. Foster, 2021, Journal of Political Economy.

The Benefits of Implicit Regularization from SGD in Least Squares Problems pdf

Vladimir Braverman, Sham M. Kakade, Dean P. Foster, 2021, ArXiv.

Single-Index Models in the High Signal Regime

Dean P. Foster, Ashwin Pananjady, 2021, IEEE Transactions on Information Theory.

α‐investing: a procedure for sequential control of expected false discoveries

Dean P. Foster, R. Stine, 2008 .

The risk inflation criterion for multiple regression

Dean P. Foster, E. George, 1994 .

Multi-View Dimensionality Reduction via Canonical Correlation Multi-View Dimensionality Reduction via Canonical Correlation Analysis Analysis Multi-View Dimensionality Reduction via Canonical Correlation Analysis Multi-View Dimensionality Reduction via Canonical Correlation Analysis Multi-View Dimen

Dean P. Foster, 2008 .

Calibration and empirical Bayes variable selection

Dean P. Foster, E. George, 2000 .

An Economic Argument for Affirmative Action

Dean P. Foster, R. Vohra, 1992 .

Variable Selection in Data Mining

Dean P. Foster, R. Stine, 2004 .

Fitting Equations to Data

Dean P. Foster, R. Stine, R. Waterman, 1998 .

On the Nonconvergence of Fictitious Play in Coordination Games

Dean P. Foster, H. Young, 1996 .

The Competitive Complexity Ratio

Dean P. Foster, R. Stine, 2000 .

The Contribution of Parameters to Stochastic Complexity

Dean P. Foster, R. Stine, .

VIF Regression: A Fast Regression Algorithm for Large Data

Dean P. Foster, L. Ungar, Dongyu Lin, 2011 .

Asypmtotic Filtering Theory for Univariate Arch Models

Dean P. Foster, Daniel B. Nelson, 1994 .

Filtering and Forecasting with Misspecified Arch Models Ii: Making the Right Forecast with the Wrong Model

Dean P. Foster, Daniel B. Nelson, 1992 .

TTI-TR-2009-1 Zero-Shot Domain Adaptation : A MultiView Approach

Dean P. Foster, John Blitzer, 2009 .

A strategy-proof test of portfolio returns

Dean P. Foster, H. Young, 2012 .

Filtering and forecasting with misspecified ARCH models II

Dean P. Foster, Daniel B. Nelson, 1992 .

Asypmtotic Filtering Theory for Univariate Arch Models

Dean P. Foster, Daniel B. Nelson, 1994 .

A Sparse linear regression

Dean P. Foster, 2016 .

A Probabilistic Analysis of the K-Location Problem

Dean P. Foster, R. Vohra, 1992 .

Graininess of judgment under uncertainty: An accuracy-informativeness trade-off

Dean P. Foster, Ilan Yaniv, Ilan Yaniv, 1995 .

Precision and Accuracy of Judgmental Estimation

Dean P. Foster, Ilan Yaniv, Ilan Yaniv, 1997 .

An Operational Measure of Riskiness

Sergiu Hart, Dean P. Foster, 2009, Journal of Political Economy.

Continuous Record Asymptotics for Rolling Sample Variance Estimators

Dean P. Foster, Daniel B. Nelson, Dean P. Foster, 1994 .

Submodularity in Statistics: Comparing the Success of Model Selection Methods

Dean P. Foster, Robert A. Stine, Kory D. Johnson, 2015 .

A Risk Ratio Comparison of $l_0$ and $l_1$ Penalized Regression

Dean P. Foster, Robert A. Stine, Lyle H. Ungar, 2015 .

Statistics for Business: Decision Making and Analysis

Dean P. Foster, Robert A. Stine, 2010 .

Honest Confidence Intervals for the Error Variance in Stepwise Regression

Dean P. Foster, Robert A. Stine, 2006 .

Multi-View Dimensionality Reduction via Canonical Correlation Analysis

Sham M. Kakade, Dean P. Foster, 2008 .

Cooperation in the long-run

H. Peyton Young, Dean P. Foster, 1991 .

Polyshrink: An Adaptive Variable Selection Procedure That Is Competitive with Bayes Experts

Dean P. Foster, R. Stine, 2005 .

Estimation up to a Change-Point

Dean P. Foster, Edward I. George, E. George, 1993 .

Combining multiple probability predictions using a simple logit model

Jonathan Baron, Dean P. Foster, Philip E. Tetlock, 2014 .

Cooperation in the Short and in the Long Run

Dean P. Foster, H. P. Young, 2010 .

Calibrated Learning and Correlated Equilibrium

Dean P. Foster, Rakesh Vohra, R. Vohra, 1996 .

Featurizing Text : Converting Text into Predictors for Regression Analysis

Mark Liberman, Dean P. Foster, Robert A. Stine, 2013 .

Gaming Performance Fees By Portfolio Managers

H. Peyton Young, Dean P. Foster, 2010 .

Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy

Dean P. Foster, R. Stine, 2001 .

Calibration, Expected Utility and Local Optimality

Dean P. Foster, R. Vohra, 1999 .

The Power of a Few Large Blocks : A credible assumption with incredible efficiency

Dean P. Foster, Dongyu Lin, 2010 .

An Operational Measure of Riskiness 1

Dean P. Foster, S. Hart, 2008 .

A Proof of Calibration Via Blackwell's Approachability Theorem

Dean P. Foster, 1999 .

Regret in the On-Line Decision Problem

Dean P. Foster, R. Vohra, 1999 .

Cost and Trust Issues in On-Line

Dean P. Foster, D. Parkes, AuctionsLyle H. Ungar, 1998 .

PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing

Dean P. Foster, S. Kakade, J. Langford, 2020, IEEE Data Eng. Bull..

Multiple hypothesis testing using the excess discovery count and alpha-investing rules

Dean P. Foster, R. Stine, 2005 .

Adaptive Variable Selection with Bayesian Oracles

Dean P. Foster, R. Stine, 2002 .

The Benefits of Implicit Regularization from SGD in Least Squares Problems

Dean P. Foster, S. Kakade, Quanquan Gu, 2021, NeurIPS.