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ORCIDJason D. Lee
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- affiliation: Stanford University, Institute of Computational and Mathematical Engineering
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2020 – today
- 2023
[i101]Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon S. Du, Jason D. Lee:
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. CoRR abs/2301.11500 (2023)- [i100]Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris S. Papailiopoulos:
Looped Transformers as Programmable Computers. CoRR abs/2301.13196 (2023) - [i99]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR abs/2302.02392 (2023) - [i98]Hadi Daneshmand, Jason D. Lee, Chi Jin:
Efficient displacement convex optimization with particle gradient descent. CoRR abs/2302.04753 (2023) - [i97]Hanlin Zhu, Ruosong Wang, Jason D. Lee:
Provably Efficient Reinforcement Learning via Surprise Bound. CoRR abs/2302.11634 (2023) - [i96]Zhuoqing Song, Jason D. Lee, Zhuoran Yang:
Can We Find Nash Equilibria at a Linear Rate in Markov Games? CoRR abs/2303.03095 (2023) - [i95]Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee:
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. CoRR abs/2305.04819 (2023) - [i94]Eshaan Nichani, Alex Damian, Jason D. Lee:
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks. CoRR abs/2305.06986 (2023) - [i93]Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, Yuxin Chen:
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning. CoRR abs/2305.10282 (2023) - [i92]Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee:
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. CoRR abs/2305.10633 (2023) - [i91]Jingfeng Wu, Vladimir Braverman, Jason D. Lee:
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. CoRR abs/2305.11788 (2023) - 2022
- [c77]Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du:
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. AISTATS 2022: 2736-2761 - [c76]Itay Safran, Jason D. Lee:
Optimization-Based Separations for Neural Networks. COLT 2022: 3-64 - [c75]Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee:
Offline Reinforcement Learning with Realizability and Single-policy Concentrability. COLT 2022: 2730-2775 - [c74]Alexandru Damian, Jason D. Lee, Mahdi Soltanolkotabi:
Neural Networks can Learn Representations with Gradient Descent. COLT 2022: 5413-5452 - [c73]DiJia Su, Jason D. Lee, John M. Mulvey, H. Vincent Poor:
Competitive Multi-Agent Reinforcement Learning with Self-Supervised Representation. ICASSP 2022: 4098-4102 - [c72]Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang:
Towards General Function Approximation in Zero-Sum Markov Games. ICLR 2022 - [c71]Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora:
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. NeurIPS 2022 - [c70]Eshaan Nichani, Yu Bai, Jason D. Lee:
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. NeurIPS 2022 - [c69]Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS 2022 - [c68]Itay Safran, Gal Vardi, Jason D. Lee:
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. NeurIPS 2022 - [c67]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS 2022 - [i90]Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee:
Offline Reinforcement Learning with Realizability and Single-policy Concentrability. CoRR abs/2202.04634 (2022) - [i89]Jiaqi Yang, Qi Lei, Jason D. Lee, Simon S. Du:
Nearly Minimax Algorithms for Linear Bandits with Shared Representation. CoRR abs/2203.15664 (2022) - [i88]Itay Safran, Gal Vardi, Jason D. Lee:
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. CoRR abs/2205.09072 (2022) - [i87]Wenhao Zhan, Jason D. Lee, Zhuoran Yang:
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games. CoRR abs/2206.01588 (2022) - [i86]Eshaan Nichani, Yu Bai, Jason D. Lee:
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. CoRR abs/2206.03688 (2022) - [i85]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR abs/2206.12020 (2022) - [i84]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR abs/2206.12081 (2022) - [i83]Alex Damian, Jason D. Lee, Mahdi Soltanolkotabi:
Neural Networks can Learn Representations with Gradient Descent. CoRR abs/2206.15144 (2022) - [i82]Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora:
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. CoRR abs/2207.04036 (2022) - [i81]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
PAC Reinforcement Learning for Predictive State Representations. CoRR abs/2207.05738 (2022) - [i80]Alex Damian, Eshaan Nichani, Jason D. Lee:
Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability. CoRR abs/2209.15594 (2022) - [i79]Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR abs/2210.06705 (2022) - [i78]Zihan Wang, Jason D. Lee, Qi Lei:
Reconstructing Training Data from Model Gradient, Provably. CoRR abs/2212.03714 (2022) - 2021
- [j9]Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan:
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift. J. Mach. Learn. Res. 22: 98:1-98:76 (2021) - [j8]Songtao Lu
, Jason D. Lee, Meisam Razaviyayn, Mingyi Hong:
Linearized ADMM Converges to Second-Order Stationary Points for Non-Convex Problems. IEEE Trans. Signal Process. 69: 4859-4874 (2021) - [c66]Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang:
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. COLT 2021: 1887-1936 - [c65]Jeff Z. HaoChen, Colin Wei, Jason D. Lee, Tengyu Ma:
Shape Matters: Understanding the Implicit Bias of the Noise Covariance. COLT 2021: 2315-2357 - [c64]Simon Shaolei Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei:
Few-Shot Learning via Learning the Representation, Provably. ICLR 2021 - [c63]Jiaqi Yang, Wei Hu, Jason D. Lee, Simon Shaolei Du:
Impact of Representation Learning in Linear Bandits. ICLR 2021 - [c62]Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong:
How Important is the Train-Validation Split in Meta-Learning? ICML 2021: 543-553 - [c61]Tianle Cai, Ruiqi Gao, Jason D. Lee, Qi Lei:
A Theory of Label Propagation for Subpopulation Shift. ICML 2021: 1170-1182 - [c60]Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang:
Bilinear Classes: A Structural Framework for Provable Generalization in RL. ICML 2021: 2826-2836 - [c59]Qi Lei, Wei Hu, Jason D. Lee:
Near-Optimal Linear Regression under Distribution Shift. ICML 2021: 6164-6174 - [c58]Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo:
Predicting What You Already Know Helps: Provable Self-Supervised Learning. NeurIPS 2021: 309-323 - [c57]Kurtland Chua, Qi Lei, Jason D. Lee:
How Fine-Tuning Allows for Effective Meta-Learning. NeurIPS 2021: 8871-8884 - [c56]Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang:
Going Beyond Linear RL: Sample Efficient Neural Function Approximation. NeurIPS 2021: 8968-8983 - [c55]Alex Damian, Tengyu Ma, Jason D. Lee:
Label Noise SGD Provably Prefers Flat Global Minimizers. NeurIPS 2021: 27449-27461 - [c54]Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang:
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. NeurIPS 2021: 29101-29115 - [i77]Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du:
Provably Efficient Policy Gradient Methods for Two-Player Zero-Sum Markov Games. CoRR abs/2102.08903 (2021) - [i76]Tianle Cai, Ruiqi Gao, Jason D. Lee, Qi Lei:
A Theory of Label Propagation for Subpopulation Shift. CoRR abs/2102.11203 (2021) - [i75]DiJia Su, Jason D. Lee, John M. Mulvey, H. Vincent Poor:
MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning. CoRR abs/2102.11448 (2021) - [i74]Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang:
Bilinear Classes: A Structural Framework for Provable Generalization in RL. CoRR abs/2103.10897 (2021) - [i73]Kurtland Chua, Qi Lei, Jason D. Lee:
How Fine-Tuning Allows for Effective Meta-Learning. CoRR abs/2105.02221 (2021) - [i72]Wenhao Zhan, Shicong Cen, Baihe Huang, Yuxin Chen, Jason D. Lee, Yuejie Chi:
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. CoRR abs/2105.11066 (2021) - [i71]Alex Damian, Tengyu Ma, Jason D. Lee:
Label Noise SGD Provably Prefers Flat Global Minimizers. CoRR abs/2106.06530 (2021) - [i70]Qi Lei, Wei Hu, Jason D. Lee:
Near-Optimal Linear Regression under Distribution Shift. CoRR abs/2106.12108 (2021) - [i69]Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei:
A Short Note on the Relationship of Information Gain and Eluder Dimension. CoRR abs/2107.02377 (2021) - [i68]Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang:
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. CoRR abs/2107.04518 (2021) - [i67]Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang:
Going Beyond Linear RL: Sample Efficient Neural Function Approximation. CoRR abs/2107.06466 (2021) - [i66]Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang:
Towards General Function Approximation in Zero-Sum Markov Games. CoRR abs/2107.14702 (2021) - [i65]Xinyi Chen, Edgar Minasyan, Jason D. Lee, Elad Hazan:
Provable Regret Bounds for Deep Online Learning and Control. CoRR abs/2110.07807 (2021) - [i64]Kurtland Chua, Qi Lei, Jason D. Lee:
Provable Hierarchy-Based Meta-Reinforcement Learning. CoRR abs/2110.09507 (2021) - [i63]Itay Safran, Jason D. Lee:
Optimization-Based Separations for Neural Networks. CoRR abs/2112.02393 (2021) - 2020
- [j7]Damek Davis, Dmitriy Drusvyatskiy, Sham M. Kakade, Jason D. Lee:
Stochastic Subgradient Method Converges on Tame Functions. Found. Comput. Math. 20(1): 119-154 (2020) - [c53]Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan:
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. COLT 2020: 64-66 - [c52]Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro:
Kernel and Rich Regimes in Overparametrized Models. COLT 2020: 3635-3673 - [c51]Yu Bai, Jason D. Lee:
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks. ICLR 2020 - [c50]Qi Lei, Jason D. Lee, Alex Dimakis, Constantinos Daskalakis:
SGD Learns One-Layer Networks in WGANs. ICML 2020: 5799-5808 - [c49]Ashok Vardhan Makkuva, Amirhossein Taghvaei, Sewoong Oh, Jason D. Lee:
Optimal transport mapping via input convex neural networks. ICML 2020: 6672-6681 - [c48]Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher:
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. NeurIPS 2020 - [c47]Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang:
Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity. NeurIPS 2020 - [c46]Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, Tong Zhang:
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. NeurIPS 2020 - [c45]Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor:
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters. NeurIPS 2020 - [c44]Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, Zheng Yu:
Generalized Leverage Score Sampling for Neural Networks. NeurIPS 2020 - [c43]Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry:
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. NeurIPS 2020 - [c42]Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, Jason D. Lee:
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot. NeurIPS 2020 - [c41]Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge:
Beyond Lazy Training for Over-parameterized Tensor Decomposition. NeurIPS 2020 - [i62]Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang:
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity. CoRR abs/2002.07125 (2020) - [i61]Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro:
Kernel and Rich Regimes in Overparametrized Models. CoRR abs/2002.09277 (2020) - [i60]Simon S. Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei:
Few-Shot Learning via Learning the Representation, Provably. CoRR abs/2002.09434 (2020) - [i59]Lemeng Wu, Mao Ye, Qi Lei, Jason D. Lee, Qiang Liu:
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting. CoRR abs/2003.10392 (2020) - [i58]Xi Chen, Jason D. Lee, He Li, Yun Yang:
Distributed Estimation for Principal Component Analysis: a Gap-free Approach. CoRR abs/2004.02336 (2020) - [i57]Jeff Z. HaoChen, Colin Wei, Jason D. Lee, Tengyu Ma:
Shape Matters: Understanding the Implicit Bias of the Noise Covariance. CoRR abs/2006.08680 (2020) - [i56]Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor:
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters. CoRR abs/2006.09486 (2020) - [i55]Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher:
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. CoRR abs/2006.13436 (2020) - [i54]Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang:
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. CoRR abs/2007.01452 (2020) - [i53]Edward Moroshko, Suriya Gunasekar, Blake E. Woodworth, Jason D. Lee, Nathan Srebro, Daniel Soudry:
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. CoRR abs/2007.06738 (2020) - [i52]Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo:
Predicting What You Already Know Helps: Provable Self-Supervised Learning. CoRR abs/2008.01064 (2020) - [i51]Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, Zheng Yu:
Generalized Leverage Score Sampling for Neural Networks. CoRR abs/2009.09829 (2020) - [i50]Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, Jason D. Lee:
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot. CoRR abs/2009.11094 (2020) - [i49]Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong:
How Important is the Train-Validation Split in Meta-Learning? CoRR abs/2010.05843 (2020) - [i48]Jiaqi Yang, Wei Hu, Jason D. Lee, Simon S. Du:
Provable Benefits of Representation Learning in Linear Bandits. CoRR abs/2010.06531 (2020) - [i47]Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge:
Beyond Lazy Training for Over-parameterized Tensor Decomposition. CoRR abs/2010.11356 (2020)
2010 – 2019
- 2019
- [j6]Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht:
First-order methods almost always avoid strict saddle points. Math. Program. 176(1-2): 311-337 (2019) - [j5]Mahdi Soltanolkotabi, Adel Javanmard, Jason D. Lee:
Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks. IEEE Trans. Inf. Theory 65(2): 742-769 (2019) - [c40]Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry:
Convergence of Gradient Descent on Separable Data. AISTATS 2019: 3420-3428 - [c39]Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai:
Gradient Descent Finds Global Minima of Deep Neural Networks. ICML 2019: 1675-1685 - [c38]Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry:
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. ICML 2019: 4683-4692 - [c37]Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma:
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel. NeurIPS 2019: 9709-9721 - [c36]Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang:
Neural Temporal-Difference Learning Converges to Global Optima. NeurIPS 2019: 11312-11322 - [c35]Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, Jason D. Lee:
Convergence of Adversarial Training in Overparametrized Neural Networks. NeurIPS 2019: 13009-13020 - [c34]Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn:
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods. NeurIPS 2019: 14905-14916 - [i46]Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn:
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods. CoRR abs/1902.08297 (2019) - [i45]Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry:
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. CoRR abs/1905.07325 (2019) - [i44]Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang:
Neural Temporal-Difference Learning Converges to Global Optima. CoRR abs/1905.10027 (2019) - [i43]Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang, Cho-Jui Hsieh, Jason D. Lee:
Convergence of Adversarial Training in Overparametrized Networks. CoRR abs/1906.07916 (2019) - [i42]Xiao Li, Zhihui Zhu, Anthony Man-Cho So, Jason D. Lee:
Incremental Methods for Weakly Convex Optimization. CoRR abs/1907.11687 (2019) - [i41]Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan:
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. CoRR abs/1908.00261 (2019) - [i40]Ashok Vardhan Makkuva, Amirhossein Taghvaei, Sewoong Oh, Jason D. Lee:
Optimal transport mapping via input convex neural networks. CoRR abs/1908.10962 (2019) - [i39]Yu Bai, Jason D. Lee:
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks. CoRR abs/1910.01619 (2019) - [i38]Qi Lei, Jason D. Lee, Alexandros G. Dimakis, Constantinos Daskalakis:
SGD Learns One-Layer Networks in WGANs. CoRR abs/1910.07030 (2019) - [i37]Maziar Sanjabi, Sina Baharlouei, Meisam Razaviyayn, Jason D. Lee:
When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima? CoRR abs/1911.09815 (2019) - 2018
- [c33]Rong Ge, Jason D. Lee, Tengyu Ma:
Learning One-hidden-layer Neural Networks with Landscape Design. ICLR (Poster) 2018 - [c32]Simon S. Du, Jason D. Lee, Yuandong Tian:
When is a Convolutional Filter Easy to Learn? ICLR (Poster) 2018 - [c31]Chenwei Wu, Jiajun Luo, Jason D. Lee:
No Spurious Local Minima in a Two Hidden Unit ReLU Network. ICLR (Workshop) 2018 - [c30]Simon S. Du, Jason D. Lee:
On the Power of Over-parametrization in Neural Networks with Quadratic Activation. ICML 2018: 1328-1337 - [c29]Simon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabás Póczos:
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. ICML 2018: 1338-1347 - [c28]Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro:
Characterizing Implicit Bias in Terms of Optimization Geometry. ICML 2018: 1827-1836 - [c27]