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ORCIDHanie Sedghi
Person information
- affiliation: Google Research
- affiliation (former): Allen Institute for Artificial Intelligence
- affiliation (former): University of Southern California, Los Angeles, USA
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2020 – today
- 2024
[j4]Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 2024 (2024)- [i31]Bernd Bohnet, Kevin Swersky, Rosanne Liu, Pranjal Awasthi, Azade Nova, Javier Snaider, Hanie Sedghi, Aaron T. Parisi, Michael Collins, Angeliki Lazaridou, Orhan Firat, Noah Fiedel:
Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation. CoRR abs/2406.00179 (2024) - [i30]Bernd Bohnet, Azade Nova, Aaron T. Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi:
Exploring and Benchmarking the Planning Capabilities of Large Language Models. CoRR abs/2406.13094 (2024) - [i29]Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith:
Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR abs/2408.07852 (2024) - 2023
- [c18]Mahsa Forouzesh, Hanie Sedghi, Patrick Thiran:
Leveraging Unlabeled Data to Track Memorization. ICLR 2023 - [c17]Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur:
REPAIR: REnormalizing Permuted Activations for Interpolation Repair. ICLR 2023 - [c16]Pratyush Maini, Michael Curtis Mozer, Hanie Sedghi, Zachary Chase Lipton, J. Zico Kolter, Chiyuan Zhang:
Can Neural Network Memorization Be Localized? ICML 2023: 23536-23557 - [e1]Sarath Chandar, Razvan Pascanu, Hanie Sedghi, Doina Precup:
Conference on Lifelong Learning Agents, 22-25 August 2023, McGill University, Montréal, Québec, Canada. Proceedings of Machine Learning Research 232, PMLR 2023 [contents] - [i28]Rahim Entezari, Mitchell Wortsman, Olga Saukh, Moein Shariatnia, Hanie Sedghi, Ludwig Schmidt:
The Role of Pre-training Data in Transfer Learning. CoRR abs/2302.13602 (2023) - [i27]Pratyush Maini, Michael C. Mozer, Hanie Sedghi, Zachary C. Lipton, J. Zico Kolter, Chiyuan Zhang:
Can Neural Network Memorization Be Localized? CoRR abs/2307.09542 (2023) - [i26]C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein:
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR abs/2311.07587 (2023) - [i25]Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR abs/2312.06585 (2023) - 2022
- [c15]Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi:
Exploring the Limits of Large Scale Pre-training. ICLR 2022 - [c14]Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur:
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks. ICLR 2022 - [c13]Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging unlabeled data to predict out-of-distribution performance. ICLR 2022 - [i24]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance. CoRR abs/2201.04234 (2022) - [i23]Lukas Timpl, Rahim Entezari, Hanie Sedghi, Behnam Neyshabur, Olga Saukh:
Understanding the effect of sparsity on neural networks robustness. CoRR abs/2206.10915 (2022) - [i22]Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur:
REPAIR: REnormalizing Permuted Activations for Interpolation Repair. CoRR abs/2211.08403 (2022) - [i21]Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron C. Courville, Behnam Neyshabur, Hanie Sedghi:
Teaching Algorithmic Reasoning via In-context Learning. CoRR abs/2211.09066 (2022) - [i20]Amr Khalifa, Michael C. Mozer, Hanie Sedghi, Behnam Neyshabur, Ibrahim Alabdulmohsin:
Layer-Stack Temperature Scaling. CoRR abs/2211.10193 (2022) - [i19]Mahsa Forouzesh, Hanie Sedghi, Patrick Thiran:
Leveraging Unlabeled Data to Track Memorization. CoRR abs/2212.04461 (2022) - 2021
- [c12]Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi:
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers. ICLR 2021 - [i18]Samira Abnar, Rianne van den Berg, Golnaz Ghiasi, Mostafa Dehghani, Nal Kalchbrenner, Hanie Sedghi:
Gradual Domain Adaptation in the Wild: When Intermediate Distributions are Absent. CoRR abs/2106.06080 (2021) - [i17]Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi:
Exploring the Limits of Large Scale Pre-training. CoRR abs/2110.02095 (2021) - [i16]Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur:
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks. CoRR abs/2110.06296 (2021) - 2020
- [c11]Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi:
The intriguing role of module criticality in the generalization of deep networks. ICLR 2020 - [c10]Philip M. Long, Hanie Sedghi:
Generalization bounds for deep convolutional neural networks. ICLR 2020 - [c9]Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang:
What is being transferred in transfer learning? NeurIPS 2020 - [i15]Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang:
What is being transferred in transfer learning? CoRR abs/2008.11687 (2020) - [i14]Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi:
The Deep Bootstrap: Good Online Learners are Good Offline Generalizers. CoRR abs/2010.08127 (2020)
2010 – 2019
- 2019
- [j3]Philip M. Long, Hanie Sedghi:
On the Effect of the Activation Function on the Distribution of Hidden Nodes in a Deep Network. Neural Comput. 31(12): 2562-2580 (2019) - [c8]Hanie Sedghi, Vineet Gupta, Philip M. Long:
The Singular Values of Convolutional Layers. ICLR (Poster) 2019 - [i13]Philip M. Long, Hanie Sedghi:
On the effect of the activation function on the distribution of hidden nodes in a deep network. CoRR abs/1901.02104 (2019) - [i12]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li
, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i11]Philip M. Long, Hanie Sedghi:
Size-free generalization bounds for convolutional neural networks. CoRR abs/1905.12600 (2019) - [i10]Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi:
The intriguing role of module criticality in the generalization of deep networks. CoRR abs/1912.00528 (2019) - 2018
- [j2]Hanie Sedghi, Ashish Sabharwal:
Knowledge Completion for Generics Using Guided Tensor Factorization. Trans. Assoc. Comput. Linguistics 6: 197-210 (2018) - [i9]Hanie Sedghi, Vineet Gupta, Philip M. Long:
The Singular Values of Convolutional Layers. CoRR abs/1805.10408 (2018) - 2017
- [c7]Ashish Sabharwal, Hanie Sedghi:
How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets. UAI 2017 - 2016
- [c6]Hanie Sedghi, Majid Janzamin, Anima Anandkumar:
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models. AISTATS 2016: 1223-1231 - [i8]Hanie Sedghi, Anima Anandkumar:
Training Input-Output Recurrent Neural Networks through Spectral Methods. CoRR abs/1603.00954 (2016) - [i7]Hanie Sedghi, Ashish Sabharwal:
Knowledge Completion for Generics using Guided Tensor Factorization. CoRR abs/1612.03871 (2016) - 2015
- [j1]Hanie Sedghi, Edmond A. Jonckheere:
Statistical Structure Learning to Ensure Data Integrity in Smart Grid. IEEE Trans. Smart Grid 6(4): 1924-1933 (2015) - [c5]Majid Janzamin, Hanie Sedghi, U. N. Niranjan, Animashree Anandkumar:
FEAST at Play: Feature ExtrAction using Score function Tensors. FE@NIPS 2015: 130-144 - [c4]Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Score Function Features for Discriminative Learning. ICLR (Workshop) 2015 - [c3]Hanie Sedghi, Anima Anandkumar:
Provable Methods for Training Neural Networks with Sparse Connectivity. ICLR (Workshop) 2015 - [i6]Anima Anandkumar, Hanie Sedghi:
Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods. CoRR abs/1503.04567 (2015) - [i5]Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Generalization Bounds for Neural Networks through Tensor Factorization. CoRR abs/1506.08473 (2015) - 2014
- [c2]Hanie Sedghi, Anima Anandkumar, Edmond A. Jonckheere:
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition. NIPS 2014: 2771-2779 - [i4]Hanie Sedghi, Anima Anandkumar, Edmond A. Jonckheere:
Guarantees for Multi-Step Stochastic ADMM in High Dimensions. CoRR abs/1402.5131 (2014) - [i3]Hanie Sedghi, Edmond A. Jonckheere:
Statistical Structure Learning, Towards a Robust Smart Grid. CoRR abs/1403.1863 (2014) - [i2]Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Score Function Features for Discriminative Learning: Matrix and Tensor Framework. CoRR abs/1412.2863 (2014) - [i1]Hanie Sedghi, Anima Anandkumar:
Provable Tensor Methods for Learning Mixtures of Classifiers. CoRR abs/1412.3046 (2014) - 2010
- [c1]Majid Janzamin, Mohammad Reza Pakravan, Hanie Sedghi:
A Game-Theoretic Approach for Power Allocation in Bidirectional Cooperative Communication. WCNC 2010: 1-6
Coauthor Index
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last updated on 2024-09-25 01:38 CEST by the dblp team
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