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ORCIDDean P. Foster
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- affiliation: Department of Statistics, University of Pennsylvania
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2020 – today
- 2024
[c54]Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade:
A Study on the Calibration of In-context Learning. NAACL-HLT 2024: 6118-6136- [i35]Carson Eisenach, Udaya Ghai, Dhruv Madeka, Kari Torkkola, Dean P. Foster, Sham M. Kakade:
Neural Coordination and Capacity Control for Inventory Management. CoRR abs/2410.02817 (2024) - 2023
- [c53]Zeyu Jia, Randy Jia, Dhruv Madeka, Dean P. Foster:
Linear Reinforcement Learning with Ball Structure Action Space. ALT 2023: 755-775 - [c52]Dean P. Foster, Dylan J. Foster, Noah Golowich, Alexander Rakhlin:
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. COLT 2023: 2678-2792 - [i34]Dylan J. Foster, Dean P. Foster, Noah Golowich, Alexander Rakhlin:
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. CoRR abs/2305.00684 (2023) - [i33]Jens Tuyls, Dhruv Madeka, Kari Torkkola, Dean P. Foster, Karthik Narasimhan, Sham M. Kakade:
Scaling Laws for Imitation Learning in NetHack. CoRR abs/2307.09423 (2023) - [i32]Dean P. Foster, Randy Jia, Dhruv Madeka:
Contextual Bandits for Evaluating and Improving Inventory Control Policies. CoRR abs/2310.16096 (2023) - [i31]Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean P. Foster, Sham M. Kakade:
Learning an Inventory Control Policy with General Inventory Arrival Dynamics. CoRR abs/2310.17168 (2023) - [i30]Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade:
A Study on the Calibration of In-context Learning. CoRR abs/2312.04021 (2023) - [i29]Xinyi Chen, Angelica Chen, Dean P. Foster, Elad Hazan:
AI safety by debate via regret minimization. CoRR abs/2312.04792 (2023) - 2022
- [c51]Kory D. Johnson
, Dean P. Foster, Robert A. Stine:
Impartial Predictive Modeling and the Use of Proxy Variables. iConference (1) 2022: 292-308 - [c50]Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster:
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. NeurIPS 2022 - [i28]Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster:
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. CoRR abs/2207.08342 (2022) - [i27]Dean P. Foster, Sergiu Hart:
"Calibeating": Beating Forecasters at Their Own Game. CoRR abs/2209.04892 (2022) - [i26]Dhruv Madeka, Kari Torkkola, Carson Eisenach, Dean P. Foster, Anna Luo:
Deep Inventory Management. CoRR abs/2210.03137 (2022) - [i25]Dean P. Foster, Sergiu Hart:
Smooth Calibration, Leaky Forecasts, Finite Recall, and Nash Dynamics. CoRR abs/2210.07152 (2022) - [i24]Dean P. Foster, Sergiu Hart:
Forecast Hedging and Calibration. CoRR abs/2210.07169 (2022) - [i23]Zeyu Jia, Randy Jia, Dhruv Madeka, Dean P. Foster:
Linear Reinforcement Learning with Ball Structure Action Space. CoRR abs/2211.07419 (2022) - 2021
- [j23]Ashwin Pananjady, Dean P. Foster:
Single-Index Models in the High Signal Regime. IEEE Trans. Inf. Theory 67(6): 4092-4124 (2021) - [c49]Ruosong Wang, Dean P. Foster, Sham M. Kakade:
What are the Statistical Limits of Offline RL with Linear Function Approximation? ICLR 2021 - [c48]Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster:
Variance Reduced Training with Stratified Sampling for Forecasting Models. ICML 2021: 7145-7155 - [c47]Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean P. Foster, Daniel N. Hill, Inderjit S. Dhillon:
Top-k eXtreme Contextual Bandits with Arm Hierarchy. ICML 2021: 9422-9433 - [c46]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade:
The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS 2021: 5456-5468 - [i22]Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean P. Foster, Daniel N. Hill, Inderjit S. Dhillon:
Top-k eXtreme Contextual Bandits with Arm Hierarchy. CoRR abs/2102.07800 (2021) - [i21]Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster:
Variance Reduction in Training Forecasting Models with Subgroup Sampling. CoRR abs/2103.02062 (2021) - [i20]Dean P. Foster, Robert A. Stine:
Threshold Martingales and the Evolution of Forecasts. CoRR abs/2105.06834 (2021) - [i19]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade:
The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR abs/2108.04552 (2021) - [i18]Dean P. Foster, Alexander Rakhlin:
On Submodular Contextual Bandits. CoRR abs/2112.02165 (2021) - 2020
- [j22]Justin Chan, Landon P. Cox, Dean P. Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham M. Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet Sharma, Sudheesh Singanamalla, Jacob E. Sunshine, Stefano Tessaro:
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing. IEEE Data Eng. Bull. 43(2): 15-35 (2020) - [i17]Justin Chan, Dean P. Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham M. Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob E. Sunshine, Stefano Tessaro:
PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing. CoRR abs/2004.03544 (2020) - [i16]Ruosong Wang, Dean P. Foster, Sham M. Kakade:
What are the Statistical Limits of Offline RL with Linear Function Approximation? CoRR abs/2010.11895 (2020)
2010 – 2019
- 2019
- [c45]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. ICML 2019: 6607-6617 - [c44]John Thickstun, Zaïd Harchaoui, Dean P. Foster, Sham M. Kakade:
Coupled Recurrent Models for Polyphonic Music Composition. ISMIR 2019: 311-318 - [c43]Sergül Aydöre, Tianhao Zhu, Dean P. Foster:
Dynamic Local Regret for Non-convex Online Forecasting. NeurIPS 2019: 7980-7989 - [c42]Kyungjin Yoo, Dean P. Foster:
Interactive Visualization of Painting Data with Augmented Reality. VRST 2019: 76:1 - [c41]Kyungjin Yoo, Dean P. Foster:
Interactive Visualization of Painting Data with Augmented Reality. VRST 2019: 110:1-110:2 - [i15]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. CoRR abs/1905.12417 (2019) - [i14]Sergül Aydöre, Tianhao Zhu, Dean P. Foster:
Dynamic Local Regret for Non-convex Online Forecasting. CoRR abs/1910.07927 (2019) - 2018
- [j21]Dean P. Foster, Sergiu Hart:
Smooth calibration, leaky forecasts, finite recall, and Nash dynamics. Games Econ. Behav. 109: 271-293 (2018) - [c40]John Thickstun, Zaïd Harchaoui, Dean P. Foster, Sham M. Kakade:
Invariances and Data Augmentation for Supervised Music Transcription. ICASSP 2018: 2241-2245 - [i13]Sergül Aydöre, Lee H. Dicker, Dean P. Foster:
A Local Regret in Nonconvex Online Learning. CoRR abs/1811.05095 (2018) - [i12]John Thickstun, Zaïd Harchaoui, Dean P. Foster, Sham M. Kakade:
Coupled Recurrent Models for Polyphonic Music Composition. CoRR abs/1811.08045 (2018) - 2017
- [c39]João Sedoc, Jean Gallier, Dean P. Foster, Lyle H. Ungar:
Semantic Word Clusters Using Signed Spectral Clustering. ACL (1) 2017: 939-949 - [i11]John Thickstun, Zaïd Harchaoui, Dean P. Foster, Sham M. Kakade:
Invariances and Data Augmentation for Supervised Music Transcription. CoRR abs/1711.04845 (2017) - 2016
- [c38]Dean P. Foster, Satyen Kale, Howard J. Karloff:
Online Sparse Linear Regression. COLT 2016: 960-970 - [i10]João Sedoc, Jean Gallier, Lyle H. Ungar, Dean P. Foster:
Semantic Word Clusters Using Signed Normalized Graph Cuts. CoRR abs/1601.05403 (2016) - [i9]Dean P. Foster, Satyen Kale, Howard J. Karloff:
Online Sparse Linear Regression. CoRR abs/1603.02250 (2016) - 2015
- [j20]Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu:
A Spectral Algorithm for Latent Dirichlet Allocation. Algorithmica 72(1): 193-214 (2015) - [j19]Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
Eigenwords: spectral word embeddings. J. Mach. Learn. Res. 16: 3035-3078 (2015) - [c37]Dean P. Foster, Howard J. Karloff, Justin Thaler:
Variable Selection is Hard. COLT 2015: 696-709 - [c36]Zhuang Ma, Yichao Lu, Dean P. Foster:
Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis. ICML 2015: 169-178 - 2014
- [j18]Philip A. Ernst, Dean P. Foster, Larry A. Shepp:
On Optimal Retirement. J. Appl. Probab. 51(2): 333-345 (2014) - [j17]Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster, Lyle H. Ungar:
Spectral learning of latent-variable PCFGs: algorithms and sample complexity. J. Mach. Learn. Res. 15(1): 2399-2449 (2014) - [j16]Gábor Bartók, Dean P. Foster, Dávid Pál, Alexander Rakhlin, Csaba Szepesvári:
Partial Monitoring - Classification, Regret Bounds, and Algorithms. Math. Oper. Res. 39(4): 967-997 (2014) - [c35]Shane T. Jensen, Dean P. Foster:
A Level-set Hit-and-run Sampler for Quasi-Concave Distributions. AISTATS 2014: 439-447 - [c34]Yichao Lu, Dean P. Foster:
large scale canonical correlation analysis with iterative least squares. NIPS 2014: 91-99 - [c33]Yichao Lu, Dean P. Foster:
Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent. UAI 2014: 525-532 - [c32]Zhuang Ma, Dean P. Foster, Robert A. Stine:
Adaptive Monotone Shrinkage for Regression. UAI 2014: 533-542 - [i8]Dean P. Foster, Howard J. Karloff, Justin Thaler:
Variable Selection is Hard. CoRR abs/1412.4832 (2014) - 2013
- [j15]Paramveer S. Dhillon, Dean P. Foster, Sham M. Kakade, Lyle H. Ungar:
A risk comparison of ordinary least squares vs ridge regression. J. Mach. Learn. Res. 14(1): 1505-1511 (2013) - [j14]Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin:
Stochastic Convex Optimization with Bandit Feedback. SIAM J. Optim. 23(1): 213-240 (2013) - [c31]Shay B. Cohen, Michael Collins, Dean P. Foster, Karl Stratos, Lyle H. Ungar:
Spectral Learning Algorithms for Natural Language Processing. HLT-NAACL 2013: 13-15 - [c30]Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster, Lyle H. Ungar:
Experiments with Spectral Learning of Latent-Variable PCFGs. HLT-NAACL 2013: 148-157 - [c29]Lee H. Dicker, Dean P. Foster:
One-shot learning and big data with n=2. NIPS 2013: 270-278 - [c28]Paramveer S. Dhillon, Yichao Lu, Dean P. Foster, Lyle H. Ungar:
New Subsampling Algorithms for Fast Least Squares Regression. NIPS 2013: 360-368 - [c27]Yichao Lu, Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform. NIPS 2013: 369-377 - [c26]Jordan Rodu, Dean P. Foster, Weichen Wu, Lyle H. Ungar:
Using Regression for Spectral Estimation of HMMs. SLSP 2013: 212-223 - 2012
- [c25]Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster, Lyle H. Ungar:
Spectral Learning of Latent-Variable PCFGs. ACL (1) 2012: 223-231 - [c24]Adam Kapelner, Krishna Kaliannan, Hansen Andrew Schwartz, Lyle H. Ungar, Dean P. Foster:
New Insights from Coarse Word Sense Disambiguation in the Crowd. COLING (Posters) 2012: 539-548 - [c23]Paramveer S. Dhillon, Jordan Rodu, Michael Collins, Dean P. Foster, Lyle H. Ungar:
Spectral Dependency Parsing with Latent Variables. EMNLP-CoNLL 2012: 205-213 - [c22]Paramveer S. Dhillon, Jordan Rodu, Dean P. Foster, Lyle H. Ungar:
Using CCA to improve CCA: A new spectral method for estimating vector models of words. ICML 2012 - [c21]Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu:
A Spectral Algorithm for Latent Dirichlet Allocation. NIPS 2012: 926-934 - [c20]Dean P. Foster, Alexander Rakhlin:
No Internal Regret via Neighborhood Watch. AISTATS 2012: 382-390 - [c19]Ruslan Salakhutdinov, Sham M. Kakade, Dean P. Foster:
Domain Adaptation: A Small Sample Statistical Approach. AISTATS 2012: 960-968 - [i7]Dean P. Foster, Jordan Rodu, Lyle H. Ungar:
Spectral dimensionality reduction for HMMs. CoRR abs/1203.6130 (2012) - [i6]Animashree Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu:
Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation. CoRR abs/1204.6703 (2012) - [i5]Yichao Lu, Dean P. Foster:
Optimal Weighting of Multi-View Data with Low Dimensional Hidden States. CoRR abs/1209.5477 (2012) - 2011
- [j13]Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
Minimum Description Length Penalization for Group and Multi-Task Sparse Learning. J. Mach. Learn. Res. 12: 525-564 (2011) - [c18]Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
Multi-View Learning of Word Embeddings via CCA. NIPS 2011: 199-207 - [c17]Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin:
Stochastic convex optimization with bandit feedback. NIPS 2011: 1035-1043 - [c16]John Blitzer, Sham M. Kakade, Dean P. Foster:
Domain Adaptation with Coupled Subspaces. AISTATS 2011: 173-181 - [c15]Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Complexity-Based Approach to Calibration with Checking Rules. COLT 2011: 293-314 - [i4]Dean P. Foster, Sham M. Kakade, Ruslan Salakhutdinov:
Domain Adaptation: Overfitting and Small Sample Statistics. CoRR abs/1105.0857 (2011) - [i3]Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin:
Stochastic convex optimization with bandit feedback. CoRR abs/1107.1744 (2011) - [i2]Dean P. Foster, Alexander Rakhlin:
No Internal Regret via Neighborhood Watch. CoRR abs/1108.6088 (2011) - 2010
- [c14]Rushin Shah, Paramveer S. Dhillon, Mark Liberman, Dean P. Foster, Mohamed Maamouri, Lyle H. Ungar:
A New Approach to Lexical Disambiguation of Arabic Text. EMNLP 2010: 725-735 - [c13]Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
Feature Selection using Multiple Streams. AISTATS 2010: 153-160
2000 – 2009
- 2009
- [j12]Martha J. Farah, M. Elizabeth Smith, Cyrena Gawuga, Dennis Lindsell, Dean P. Foster:
Brain Imaging and Brain Privacy: A Realistic Concern? J. Cogn. Neurosci. 21(1): 119-127 (2009) - [c12]Dongyu Lin, Dean P. Foster:
VIF Regression: A Fast Regression Algorithm for Large Data. ICDM 2009: 848-853 - [c11]Paramveer S. Dhillon, Brian Tomasik, Dean P. Foster, Lyle H. Ungar:
Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC). ECML/PKDD (1) 2009: 276-289 - [i1]Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
Transfer Learning Using Feature Selection. CoRR abs/0905.4022 (2009) - 2008
- [j11]Sham M. Kakade, Dean P. Foster:
Deterministic calibration and Nash equilibrium. J. Comput. Syst. Sci. 74(1): 115-130 (2008) - [j10]Matthias W. Seeger, Sham M. Kakade, Dean P. Foster:
Information Consistency of Nonparametric Gaussian Process Methods. IEEE Trans. Inf. Theory 54(5): 2376-2382 (2008) - [c10]Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
Efficient Feature Selection in the Presence of Multiple Feature Classes. ICDM 2008: 779-784 - 2007
- [c9]Sham M. Kakade, Dean P. Foster:
Multi-view Regression Via Canonical Correlation Analysis. COLT 2007: 82-96 - 2006
- [j9]Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar:
Streamwise Feature Selection. J. Mach. Learn. Res. 7: 1861-1885 (2006) - [c8]Dean P. Foster, Sham M. Kakade:
Calibration via Regression. ITW 2006: 82-86 - 2005
- [c7]Lyle H. Ungar, Jing Zhou, Dean P. Foster, Bob A. Stine:
Streaming Feature Selection using IIC. AISTATS 2005: 357-364 - [c6]Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar:
Streaming feature selection using alpha-investing. KDD 2005: 384-393 - [c5]Sham M. Kakade, Matthias W. Seeger, Dean P. Foster:
Worst-Case Bounds for Gaussian Process Models. NIPS 2005: 619-626 - 2004
- [c4]Sham M. Kakade, Dean P. Foster:
Deterministic Calibration and Nash Equilibrium. COLT 2004: 33-48 - 2003
- [j8]Dean P. Foster, H. Peyton Young:
Learning, hypothesis testing, and Nash equilibrium. Games Econ. Behav. 45(1): 73-96 (2003) - 2002
- [j7]Dean P. Foster, Robert A. Stine, Abraham J. Wyner:
Universal codes for finite sequences of integers drawn from a monotone distribution. IEEE Trans. Inf. Theory 48(6): 1713-1720 (2002)
1990 – 1999
- 1999
- [j6]Dean P. Foster, Robert A. Stine:
Local Asymptotic Coding and the Minimum Description Length. IEEE Trans. Inf. Theory 45(4): 1289-1293 (1999) - 1998
- [b2]Dean P. Foster, Robert A. Stine, Richard P. Waterman:
Basic Business Statistics - A Casebook. Springer 1998, ISBN 978-0-387-98354-7, pp. I-XVI, 1-244 - [b1]Dean P. Foster, Robert A. Stine, Richard P. Waterman:
Business Analysis Using Regression - A Casebook. Springer 1998, ISBN 978-0-387-98356-1, pp. I-XVII, 1-348 - [j5]Dean P. Foster, Rakesh V. Vohra:
An Axiomatic Characterization of a Class of Locations in Tree Networks. Oper. Res. 46(3): 347-354 (1998) - [j4]Amos Fiat, Dean P. Foster, Howard J. Karloff, Yuval Rabani, Yiftach Ravid, Sundar Vishwanathan:
Competitive Algorithms for Layered Graph Traversal. SIAM J. Comput. 28(2): 447-462 (1998) - [c3]David C. Parkes, Lyle H. Ungar, Dean P. Foster:
Accounting for Cognitive Costs in On-Line Auction Design. AMET 1998: 25-40 - 1997
- [c2]Dale Schuurmans, Lyle H. Ungar, Dean P. Foster:
Characterizing the generalization performance of model selection strategies. ICML 1997: 340-348 - 1993
- [j3]Dean P. Foster, Rakesh V. Vohra:
A Randomization Rule for Selecting Forecasts. Oper. Res. 41(4): 704-709 (1993) - [j2]Dean P. Foster, Rakesh V. Vohra:
Reply to Professor Clemen. Oper. Res. 41(4): 802-803 (1993) - 1991
- [c1]Amos Fiat, Dean P. Foster, Howard J. Karloff, Yuval Rabani, Yiftach Ravid, Sundar Vishwanathan:
Competitive Algorithms for Layered Graph Traversal. FOCS 1991: 288-297
1980 – 1989
- 1989
- [j1]Dean P. Foster, Rakesh Vohra:
Probabilistic Analysis of a Heuristics for the Dual Bin Packing Problem. Inf. Process. Lett. 31(6): 287-290 (1989)
Coauthor Index
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