暂无分享,去创建一个
Sham M. Kakade | Qi Lei | Baihe Huang | Runzhe Wang | Jason D. Lee | Jiaqi Yang | Kaixuan Huang | S. Kakade | Qi Lei | Jiaqi Yang | Baihe Huang | Kaixuan Huang | Runzhe Wang
[1] Colin Wei,et al. Shape Matters: Understanding the Implicit Bias of the Noise Covariance , 2020, COLT.
[2] Babak Hassibi,et al. Stochastic Linear Bandits with Hidden Low Rank Structure , 2019, ArXiv.
[3] Tengyu Ma,et al. Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature , 2021, ArXiv.
[4] Zheng Wen,et al. Stochastic Rank-1 Bandits , 2016, AISTATS.
[5] Adam Tauman Kalai,et al. Online convex optimization in the bandit setting: gradient descent without a gradient , 2004, SODA '05.
[6] Yuanzhi Li,et al. First Efficient Convergence for Streaming k-PCA: A Global, Gap-Free, and Near-Optimal Rate , 2016, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).
[7] Tengyu Ma,et al. Beyond Lazy Training for Over-parameterized Tensor Decomposition , 2020, NeurIPS.
[8] Nathan Srebro,et al. Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models , 2019, ICML.
[9] Tengyu Ma,et al. Learning One-hidden-layer Neural Networks with Landscape Design , 2017, ICLR.
[10] Tor Lattimore,et al. Improved Regret for Zeroth-Order Adversarial Bandit Convex Optimisation , 2020, ArXiv.
[11] Peter L. Bartlett,et al. Linear Programming for Large-Scale Markov Decision Problems , 2014, ICML.
[12] Robert D. Nowak,et al. Bilinear Bandits with Low-rank Structure , 2019, ICML.
[13] Wei Chu,et al. A contextual-bandit approach to personalized news article recommendation , 2010, WWW '10.
[14] Jason D. Lee,et al. Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks , 2019, ICLR.
[15] Sébastien Bubeck,et al. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems , 2012, Found. Trends Mach. Learn..
[16] Ruosong Wang,et al. Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension , 2020, NeurIPS.
[17] John N. Tsitsiklis,et al. Linearly Parameterized Bandits , 2008, Math. Oper. Res..
[18] Nathan Srebro,et al. Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy , 2020, NeurIPS.
[19] Xiaoyu Chen,et al. Near-optimal Representation Learning for Linear Bandits and Linear RL , 2021, ICML.
[20] Tor Lattimore,et al. High-Dimensional Sparse Linear Bandits , 2020, NeurIPS.
[21] Joel A. Tropp,et al. User-Friendly Tail Bounds for Sums of Random Matrices , 2010, Found. Comput. Math..
[22] Jason D. Lee,et al. When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima? , 2019, ArXiv.
[23] 俊一 甘利. 5分で分かる!? 有名論文ナナメ読み:Jacot, Arthor, Gabriel, Franck and Hongler, Clement : Neural Tangent Kernel : Convergence and Generalization in Neural Networks , 2020 .
[24] Tengyu Ma,et al. Online Learning of Eigenvectors , 2015, ICML.
[25] Chi Jin,et al. Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms , 2021, NeurIPS.
[26] Maria-Florina Balcan,et al. An Improved Gap-Dependency Analysis of the Noisy Power Method , 2016, COLT.
[27] Cong Fang,et al. Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks , 2020, COLT.
[28] Cho-Jui Hsieh,et al. Convergence of Adversarial Training in Overparametrized Networks , 2019, ArXiv.
[29] Tengyu Ma,et al. Label Noise SGD Provably Prefers Flat Global Minimizers , 2021, NeurIPS.
[30] S. Mendelson,et al. Minimax rate of convergence and the performance of ERM in phase recovery , 2013, 1311.5024.
[31] Moritz Hardt,et al. The Noisy Power Method: A Meta Algorithm with Applications , 2013, NIPS.
[32] Robert D. Kleinberg. Nearly Tight Bounds for the Continuum-Armed Bandit Problem , 2004, NIPS.
[33] Shachar Lovett,et al. Bilinear Classes: A Structural Framework for Provable Generalization in RL , 2021, ICML.
[34] Wojciech Kotlowski,et al. Bandit Principal Component Analysis , 2019, COLT.
[35] Zhiqiang Xu,et al. Generalized phase retrieval : measurement number, matrix recovery and beyond , 2016, Applied and Computational Harmonic Analysis.
[36] Nello Cristianini,et al. Finite-Time Analysis of Kernelised Contextual Bandits , 2013, UAI.
[37] Alessandro Lazaric,et al. Learning Near Optimal Policies with Low Inherent Bellman Error , 2020, ICML.
[38] Sham M. Kakade,et al. Few-Shot Learning via Learning the Representation, Provably , 2020, ICLR.
[39] Tor Lattimore,et al. Bandit Phase Retrieval , 2021, ArXiv.
[40] Benjamin Van Roy,et al. Eluder Dimension and the Sample Complexity of Optimistic Exploration , 2013, NIPS.
[41] Ambuj Tewari,et al. Low-Rank Generalized Linear Bandit Problems , 2020, AISTATS.
[42] Liwei Wang,et al. Gradient Descent Finds Global Minima of Deep Neural Networks , 2018, ICML.
[43] Vidyashankar Sivakumar,et al. Structured Stochastic Linear Bandits , 2016, ArXiv.
[44] Emmanuel J. Candès,et al. On the Fundamental Limits of Adaptive Sensing , 2011, IEEE Transactions on Information Theory.
[45] Cameron Musco,et al. Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition , 2015, NIPS.
[46] Aditya Gopalan,et al. Low-rank Bandits with Latent Mixtures , 2016, ArXiv.
[47] Yin Tat Lee,et al. Kernel-based methods for bandit convex optimization , 2016, STOC.
[48] Colin Wei,et al. Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel , 2018, NeurIPS.
[49] Tor Lattimore,et al. Online Sparse Reinforcement Learning , 2020, ArXiv.
[50] Raghu Meka,et al. Learning Polynomials of Few Relevant Dimensions , 2020, COLT.
[51] Anima Anandkumar,et al. Tensor decompositions for learning latent variable models , 2012, J. Mach. Learn. Res..
[52] Joan Bruna,et al. On the Expressive Power of Deep Polynomial Neural Networks , 2019, NeurIPS.
[53] Yuanzhi Li,et al. Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data , 2018, NeurIPS.
[54] Csaba Szepesvári,et al. Improved Algorithms for Linear Stochastic Bandits , 2011, NIPS.
[55] Thomas P. Hayes,et al. Stochastic Linear Optimization under Bandit Feedback , 2008, COLT.
[56] Xiaodong Li,et al. Phase Retrieval via Wirtinger Flow: Theory and Algorithms , 2014, IEEE Transactions on Information Theory.
[57] Yuanzhi Li,et al. An optimal algorithm for bandit convex optimization , 2016, ArXiv.
[58] Andrea Montanari,et al. Linearized two-layers neural networks in high dimension , 2019, The Annals of Statistics.
[59] Xiaodong Li,et al. Optimal Rates of Convergence for Noisy Sparse Phase Retrieval via Thresholded Wirtinger Flow , 2015, ArXiv.
[60] Sham M. Kakade,et al. Stochastic Convex Optimization with Bandit Feedback , 2011, SIAM J. Optim..
[61] Jie Zhou,et al. Low-rank Tensor Bandits , 2020, ArXiv.
[62] Yuanzhi Li,et al. What Can ResNet Learn Efficiently, Going Beyond Kernels? , 2019, NeurIPS.
[63] Handong Zhao,et al. Neural Contextual Bandits with Deep Representation and Shallow Exploration , 2020, ICLR.
[64] S. Kakade,et al. Reinforcement Learning: Theory and Algorithms , 2019 .
[65] Csaba Szepesvári,et al. Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits , 2012, AISTATS.
[66] Yu Bai,et al. Towards Understanding Hierarchical Learning: Benefits of Neural Representations , 2020, NeurIPS.
[67] Jasper Snoek,et al. Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling , 2018, ICLR.
[68] Nathan Srebro,et al. Kernel and Deep Regimes in Overparametrized Models , 2019, ArXiv.
[69] Anima Anandkumar,et al. Online and Differentially-Private Tensor Decomposition , 2016, NIPS.
[70] Wei Hu,et al. Provable Benefits of Representation Learning in Linear Bandits , 2020, ArXiv.