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Philippe Rigollet
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2020 – today
- 2024
- [j6]Vianney Perchet
, Philippe Rigollet, Thibaut Le Gouic:
An Algorithmic Solution to the Blotto Game Using Multimarginal Couplings. Oper. Res. 72(5): 2061-2075 (2024) - [i37]Borjan Geshkovski, Hugo Koubbi, Yury Polyanskiy, Philippe Rigollet:
Dynamic metastability in the self-attention model. CoRR abs/2410.06833 (2024) - 2023
- [j5]Subhroshekhar Ghosh, Philippe Rigollet:
Sparse Multi-Reference Alignment: Phase Retrieval, Uniform Uncertainty Principles and the Beltway Problem. Found. Comput. Math. 23(5): 1851-1898 (2023) - [c30]Tyler Maunu, Thibaut Le Gouic, Philippe Rigollet:
Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery. AISTATS 2023: 8183-8210 - [c29]Borjan Geshkovski, Cyril Letrouit, Yury Polyanskiy, Philippe Rigollet:
The emergence of clusters in self-attention dynamics. NeurIPS 2023 - [i36]Yuling Yan, Kaizheng Wang
Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow. CoRR abs/2301.01766 (2023) - [i35]Borjan Geshkovski, Cyril Letrouit, Yury Polyanskiy, Philippe Rigollet:
The emergence of clusters in self-attention dynamics. CoRR abs/2305.05465 (2023) - [i34]Marie Breeur, George Stepaniants, Pekka Keski-Rahkonen, Philippe Rigollet, Vivian Viallon:
Optimal transport for automatic alignment of untargeted metabolomic data. CoRR abs/2306.03218 (2023) - [i33]Yanjun Han, Philippe Rigollet, George Stepaniants:
Covariance alignment: from maximum likelihood estimation to Gromov-Wasserstein. CoRR abs/2311.13595 (2023) - [i32]Borjan Geshkovski, Cyril Letrouit, Yury Polyanskiy, Philippe Rigollet:
A mathematical perspective on Transformers. CoRR abs/2312.10794 (2023) - 2022
- [j4]Sinho Chewi
Gaussian discrepancy: A probabilistic relaxation of vector balancing. Discret. Appl. Math. 322: 123-141 (2022) - [c28]Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet:
Rejection sampling from shape-constrained distributions in sublinear time. AISTATS 2022: 2249-2265 - [c27]Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet:
The query complexity of sampling from strongly log-concave distributions in one dimension. COLT 2022: 2041-2059 - [c26]Enric Boix-Adserà, Hannah Lawrence, George Stepaniants, Philippe Rigollet:
GULP: a prediction-based metric between representations. NeurIPS 2022 - [c25]Marc Lambert, Sinho Chewi, Francis R. Bach, Silvère Bonnabel, Philippe Rigollet:
Variational inference via Wasserstein gradient flows. NeurIPS 2022 - [c24]Vianney Perchet, Philippe Rigollet, Thibaut Le Gouic:
An Algorithmic Solution to the Blotto Game using Multi-marginal Couplings. EC 2022: 208-209 - [i31]Vianney Perchet, Philippe Rigollet, Thibaut Le Gouic:
An algorithmic solution to the Blotto game using multi-marginal couplings. CoRR abs/2202.07318 (2022) - [i30]Marc Lambert, Sinho Chewi, Francis R. Bach, Silvère Bonnabel, Philippe Rigollet:
Variational inference via Wasserstein gradient flows. CoRR abs/2205.15902 (2022) - [i29]Enric Boix Adserà, Hannah Lawrence, George Stepaniants, Philippe Rigollet:
GULP: a prediction-based metric between representations. CoRR abs/2210.06545 (2022) - 2021
- [c23]Paxton Turner, Jingbo Liu, Philippe Rigollet:
Efficient Interpolation of Density Estimators. AISTATS 2021: 2503-2511 - [c22]Paxton Turner, Jingbo Liu, Philippe Rigollet:
A Statistical Perspective on Coreset Density Estimation. AISTATS 2021: 2512-2520 - [c21]Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, Austin J. Stromme:
Fast and Smooth Interpolation on Wasserstein Space. AISTATS 2021: 3061-3069 - [c20]Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet:
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm. COLT 2021: 1260-1300 - [i28]Sinho Chewi, Patrik Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet:
The query complexity of sampling from strongly log-concave distributions in one dimension. CoRR abs/2105.14163 (2021) - [i27]Sinho Chewi, Patrik Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet:
Rejection sampling from shape-constrained distributions in sublinear time. CoRR abs/2105.14166 (2021) - [i26]Subhro Ghosh, Philippe Rigollet:
Multi-Reference Alignment for sparse signals, Uniform Uncertainty Principles and the Beltway Problem. CoRR abs/2106.12996 (2021) - [i25]Sinho Chewi, Patrik Gerber, Philippe Rigollet, Paxton Turner:
Gaussian discrepancy: a probabilistic relaxation of vector balancing. CoRR abs/2109.08280 (2021) - [i24]Subhro Ghosh, Philippe Rigollet:
Gaussian Determinantal Processes: a new model for directionality in data. CoRR abs/2111.09990 (2021) - 2020
- [c19]Sinho Chewi, Tyler Maunu, Philippe Rigollet, Austin J. Stromme:
Gradient descent algorithms for Bures-Wasserstein barycenters. COLT 2020: 1276-1304 - [c18]Paxton Turner, Raghu Meka, Philippe Rigollet:
Balancing Gaussian vectors in high dimension. COLT 2020: 3455-3486 - [c17]Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet:
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. NeurIPS 2020 - [c16]Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin J. Stromme:
Exponential ergodicity of mirror-Langevin diffusions. NeurIPS 2020 - [c15]Jan-Christian Hütter, Philippe Rigollet:
Estimation Rates for Sparse Linear Cyclic Causal Models. UAI 2020: 1169-1178 - [i23]Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin J. Stromme:
Exponential ergodicity of mirror-Langevin diffusions. CoRR abs/2005.09669 (2020) - [i22]Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet:
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. CoRR abs/2006.02509 (2020) - [i21]Paxton Turner, Jingbo Liu, Philippe Rigollet:
A Statistical Perspective on Coreset Density Estimation. CoRR abs/2011.04907 (2020) - [i20]Paxton Turner, Jingbo Liu, Philippe Rigollet:
Efficient Interpolation of Density Estimators. CoRR abs/2011.04922 (2020) - [i19]Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet:
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm. CoRR abs/2012.12810 (2020)
2010 – 2019
- 2019
- [j3]Amelia Perry, Jonathan Weed, Afonso S. Bandeira, Philippe Rigollet, Amit Singer
The Sample Complexity of Multireference Alignment. SIAM J. Math. Data Sci. 1(3): 497-517 (2019) - [c14]Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Philippe Rigollet, Geoffrey Schiebinger, Jonathan Weed:
Statistical Optimal Transport via Factored Couplings. AISTATS 2019: 2454-2465 - [c13]Jingbo Liu, Philippe Rigollet:
Power analysis of knockoff filters for correlated designs. NeurIPS 2019: 15420-15429 - [i18]Jan-Christian Hütter, Cheng Mao, Philippe Rigollet, Elina Robeva:
Estimation of Monge Matrices. CoRR abs/1904.03136 (2019) - [i17]Jingbo Liu, Philippe Rigollet:
Power analysis of knockoff filters for correlated designs. CoRR abs/1910.12428 (2019) - [i16]Raghu Meka, Philippe Rigollet, Paxton Turner:
Balancing Gaussian vectors in high dimension. CoRR abs/1910.13972 (2019) - 2018
- [c12]Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu:
Teacher Improves Learning by Selecting a Training Subset. AISTATS 2018: 1366-1375 - [c11]Cheng Mao, Jonathan Weed, Philippe Rigollet:
Minimax Rates and Efficient Algorithms for Noisy Sorting. ALT 2018: 821-847 - [c10]Sébastien Bubeck, Philippe Rigollet:
Conference on Learning Theory 2018: Preface. COLT 2018: 1 - [e1]Sébastien Bubeck, Vianney Perchet, Philippe Rigollet:
Conference On Learning Theory, COLT 2018, Stockholm, Sweden, 6-9 July 2018. Proceedings of Machine Learning Research 75, PMLR 2018 [contents] - [i15]Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu:
Teacher Improves Learning by Selecting a Training Subset. CoRR abs/1802.08946 (2018) - [i14]Nilin Abrahamsen, Philippe Rigollet:
Sparse Gaussian ICA. CoRR abs/1804.00408 (2018) - [i13]Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Geoffrey Schiebinger, Philippe Rigollet, Jonathan Weed:
Statistical Optimal Transport via Geodesic Hubs. CoRR abs/1806.07348 (2018) - 2017
- [c9]Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet, John C. Urschel:
Rates of estimation for determinantal point processes. COLT 2017: 343-345 - [c8]John C. Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet:
Learning Determinantal Point Processes with Moments and Cycles. ICML 2017: 3511-3520 - [c7]Jason M. Altschuler, Jonathan Weed, Philippe Rigollet:
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. NIPS 2017: 1964-1974 - [i12]Jason M. Altschuler, Jonathan Weed, Philippe Rigollet:
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. CoRR abs/1705.09634 (2017) - [i11]Amelia Perry, Jonathan Weed, Afonso S. Bandeira, Philippe Rigollet, Amit Singer:
The sample complexity of multi-reference alignment. CoRR abs/1707.00943 (2017) - [i10]Cheng Mao, Jonathan Weed, Philippe Rigollet:
Minimax Rates and Efficient Algorithms for Noisy Sorting. CoRR abs/1710.10388 (2017) - 2016
- [c6]Jonathan Weed, Vianney Perchet, Philippe Rigollet:
Online learning in repeated auctions. COLT 2016: 1562-1583 - [i9]Quentin Berthet, Philippe Rigollet, Piyush Srivastava:
Exact recovery in the Ising blockmodel. CoRR abs/1612.03880 (2016) - 2015
- [c5]Vianney Perchet, Philippe Rigollet, Sylvain Chassang, Erik Snowberg:
Batched Bandit Problems. COLT 2015: 1456 - [i8]Jonathan Weed, Vianney Perchet, Philippe Rigollet:
Online learning in repeated auctions. CoRR abs/1511.05720 (2015) - 2013
- [c4]Sébastien Bubeck, Vianney Perchet, Philippe Rigollet:
Bounded regret in stochastic multi-armed bandits. COLT 2013: 122-134 - [c3]Quentin Berthet, Philippe Rigollet:
Complexity Theoretic Lower Bounds for Sparse Principal Component Detection. COLT 2013: 1046-1066 - [i7]Sébastien Bubeck, Vianney Perchet, Philippe Rigollet:
Bounded regret in stochastic multi-armed bandits. CoRR abs/1302.1611 (2013) - [i6]Quentin Berthet, Philippe Rigollet:
Computational Lower Bounds for Sparse PCA. CoRR abs/1304.0828 (2013) - [i5]Dong Dai, Philippe Rigollet, Lucy Xia, Tong Zhang:
Aggregation of Affine Estimators. CoRR abs/1311.2799 (2013) - 2012
- [i4]Dong Dai, Philippe Rigollet, Tong Zhang:
Deviation Optimal Learning using Greedy Q-aggregation. CoRR abs/1203.2507 (2012) - 2011
- [j2]Philippe Rigollet, Xin Tong:
Neyman-Pearson Classification, Convexity and Stochastic Constraints. J. Mach. Learn. Res. 12: 2831-2855 (2011) - [c2]Philippe Rigollet, Xin Tong:
Neyman-Pearson classification under a strict constraint. COLT 2011: 595-614 - [i3]Philippe Rigollet, Xin Tong:
Neyman-Pearson classification, convexity and stochastic constraints. CoRR abs/1102.5750 (2011) - [i2]Vianney Perchet, Philippe Rigollet:
The multi-armed bandit problem with covariates. CoRR abs/1110.6084 (2011) - 2010
- [c1]Philippe Rigollet, Assaf Zeevi:
Nonparametric Bandits with Covariates. COLT 2010: 54-66
2000 – 2009
- 2007
- [j1]Philippe Rigollet:
Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption. J. Mach. Learn. Res. 8: 1369-1392 (2007) - 2006
- [i1]Philippe Rigollet:
Generalization error bounds in semi-supervised classification under the cluster assumption. CoRR abs/math/0604233 (2006)
Coauthor Index
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last updated on 2024-11-20 21:56 CET by the dblp team
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