Showing results 16 to 35 of 41
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Publication Date | Article Title | Author(s) |
Jun-2017 | Mapping between fMRI responses to movies and their natural language annotations | Vodrahalli, Kiran; Chen, Po-Hsuan; Liang, Yingyu; Baldassano, Christopher; Chen, Janice; et al |
2018 | Mathematics of machine learning: An introduction | Arora, Sanjeev |
2012 | Message-Passing Algorithms and Improved LP Decoding | Arora, Sanjeev; Daskalakis, Constantinos; Steurer, David |
2014 | New algorithms for learning incoherent and overcomplete dictionaries | Arora, Sanjeev; Ge, R; Moitra, A |
2019 | On Exact Computation with an Infinitely Wide Neural Net | Arora, Sanjeev; Du, Simon S; Hu, Wei; Li, Zhiyuan; Salakhutdinov, Russ R; et al |
2018 | On the optimization of deep networks: Implicit acceleration by overparameterization | Arora, Sanjeev; Cohen, N; Hazan, Elad |
2020 | Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality | Zhang, Yi; Plevrakis, Orestis; Du, Simon S; Li, Xingguo; Song, Zhao; et al |
2013 | A Practical Algorithm for Topic Modeling with Provable Guarantees | Arora, Sanjeev; Ge, Rong; Halpern, Yonatan; Mimno, David; Moitra, Ankur; et al |
2016 | Provable algorithms for inference in topic models | Arora, Sanjeev; Ge, R; Koehler, F; Ma, T; Moitra, A |
2014 | Provable bounds for learning some deep representations | Arora, Sanjeev; Bhaskara, A; Ge, R; Ma, T |
2015 | Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders | Arora, Sanjeev; Ge, Rong; Moitra, Ankur; Sachdeva, Sushant |
2012 | Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders | Arora, Sanjeev; Ge, Rong; Moitra, Ankur; Sachdeva, Sushant |
2017 | Provable learning of noisy-or networks | Arora, Sanjeev; Ge, R; Ma, T; Risteski, A |
2020 | Provable Representation Learning for Imitation Learning via Bi-level Optimization | Arora, Sanjeev; Du, Simon; Kakade, Sham; Luo, Yuping; Saunshi, Nikunj |
2020 | Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate | Li, Zhiyuan; Lyu, Kaifeng; Arora, Sanjeev |
2020 | A Sample Complexity Separation between Non-Convex and Convex Meta-Learning | Saunshi, Nikunj; Zhang, Yi; Khodak, Mikhail; Arora, Sanjeev |
2017 | A Simple but Tough-to-Beat Baseline for Sentence Embeddings | Arora, Sanjeev; Liang, Yingyu; Ma, Tengyu |
2015 | Simple, efficient, and neural algorithms for sparse coding | Arora, Sanjeev; Ge, R; Ma, T; Moitra, A |
2018 | Stronger generalization bounds for deep nets via a compression approach | Arora, Sanjeev; Ge, R; Neyshabur, B; Zhang, Y |
Nov-2015 | Subexponential Algorithms for Unique Games and Related Problems | Arora, Sanjeev; Barak, Boaz; Steurer, David |