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ORCIDAlexander A. Alemi
Person information
- affiliation: Google Inc, Mountain View, CA, USA
- affiliation (PhD 2015): Cornell University, Ithaca, NY, USA
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2020 – today
- 2024
[j2]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)- [c18]Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie E. Everett, Alexander A. Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith:
Small-scale proxies for large-scale Transformer training instabilities. ICLR 2024 - [c17]Katie E. Everett, Lechao Xiao, Mitchell Wortsman, Alexander A. Alemi, Roman Novak, Peter J. Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, Jeffrey Pennington:
Scaling Exponents Across Parameterizations and Optimizers. ICML 2024 - [i37]Brian Lester, Jaehoon Lee, Alex Alemi, Jeffrey Pennington, Adam Roberts, Jascha Sohl-Dickstein, Noah Constant:
Training LLMs over Neurally Compressed Text. CoRR abs/2404.03626 (2024) - [i36]Katie Everett, Lechao Xiao, Mitchell Wortsman, Alexander A. Alemi, Roman Novak, Peter J. Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, Jeffrey Pennington:
Scaling Exponents Across Parameterizations and Optimizers. CoRR abs/2407.05872 (2024) - 2023
- [c16]Yangjun Ruan, Saurabh Singh, Warren Richard Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon:
Weighted Ensemble Self-Supervised Learning. ICLR 2023 - [i35]Alexander A. Alemi, Ben Poole:
Variational Prediction. CoRR abs/2307.07568 (2023) - [i34]Inbar Seroussi, Alexander A. Alemi, Moritz Helias, Zohar Ringel:
Speed Limits for Deep Learning. CoRR abs/2307.14653 (2023) - [i33]Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith:
Small-scale proxies for large-scale Transformer training instabilities. CoRR abs/2309.14322 (2023) - [i32]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) - [i31]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]Warren R. Morningstar, Alex Alemi, Joshua V. Dillon:
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. AISTATS 2022: 8270-8298 - [c14]Yuqing Du, Daniel Ho, Alex Alemi, Eric Jang, Mohi Khansari:
Bayesian Imitation Learning for End-to-End Mobile Manipulation. ICML 2022: 5531-5546 - [i30]Yuqing Du, Daniel Ho, Alexander A. Alemi, Eric Jang, Mohi Khansari:
Bayesian Imitation Learning for End-to-End Mobile Manipulation. CoRR abs/2202.07600 (2022) - [i29]Yangjun Ruan, Saurabh Singh, Warren R. Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon:
Weighted Ensemble Self-Supervised Learning. CoRR abs/2211.09981 (2022) - 2021
- [c13]Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon:
Density of States Estimation for Out of Distribution Detection. AISTATS 2021: 3232-3240 - [c12]Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson:
Does Knowledge Distillation Really Work? NeurIPS 2021: 6906-6919 - [i28]Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson:
Does Knowledge Distillation Really Work? CoRR abs/2106.05945 (2021) - [i27]Iryna Korshunova, David Stutz
, Alexander A. Alemi, Olivia Wiles, Sven Gowal:
A Closer Look at the Adversarial Robustness of Information Bottleneck Models. CoRR abs/2107.05712 (2021) - 2020
- [j1]Ian Fischer, Alexander A. Alemi:
CEB Improves Model Robustness. Entropy 22(10): 1081 (2020) - [c11]Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Neural Tangents: Fast and Easy Infinite Neural Networks in Python. ICLR 2020 - [i26]Ian Fischer, Alexander A. Alemi:
CEB Improves Model Robustness. CoRR abs/2002.05380 (2020) - [i25]Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon:
Density of States Estimation for Out-of-Distribution Detection. CoRR abs/2006.09273 (2020) - [i24]Warren R. Morningstar, Alexander A. Alemi, Joshua V. Dillon:
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. CoRR abs/2010.09629 (2020) - [i23]Alexander A. Alemi, Warren R. Morningstar, Ben Poole, Ian Fischer, Joshua V. Dillon:
VIB is Half Bayes. CoRR abs/2011.08711 (2020)
2010 – 2019
- 2019
- [c10]Alexander A. Alemi:
Variational Predictive Information Bottleneck. AABI 2019: 1-6 - [c9]Ravid Shwartz-Ziv, Alexander A. Alemi:
Information in Infinite Ensembles of Infinitely-Wide Neural Networks. AABI 2019: 1-17 - [c8]Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker:
On Variational Bounds of Mutual Information. ICML 2019: 5171-5180 - [i22]Colin B. Clement, Matthew Bierbaum, Kevin P. O'Keeffe, Alexander A. Alemi:
On the Use of ArXiv as a Dataset. CoRR abs/1905.00075 (2019) - [i21]Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker:
On Variational Bounds of Mutual Information. CoRR abs/1905.06922 (2019) - [i20]Bryan Seybold, Emily Fertig, Alex Alemi, Ian Fischer:
Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces. CoRR abs/1905.07478 (2019) - [i19]Zhe Dong, Deniz Oktay, Ben Poole, Alexander A. Alemi:
On Predictive Information Sub-optimality of RNNs. CoRR abs/1910.09578 (2019) - [i18]Alexander A. Alemi:
Variational Predictive Information Bottleneck. CoRR abs/1910.10831 (2019) - [i17]Tom Conte, Erik DeBenedictis, Natesh Ganesh, Todd Hylton, John Paul Strachan, R. Stanley Williams, Alexander A. Alemi, Lee Altenberg, Gavin E. Crooks, James P. Crutchfield, Lídia del Rio, Josh Deutsch, Michael Robert DeWeese, Khari Douglas, Massimiliano Esposito, Michael P. Frank, Robert Fry, Peter Harsha, Mark D. Hill, Christopher T. Kello, Jeff Krichmar, Suhas Kumar, Shih-Chii Liu, Seth Lloyd, Matteo Marsili, Ilya Nemenman, Alex Nugent, Norman H. Packard, Dana Randall, Peter Sadowski, Narayana Santhanam, Robert Shaw, Adam Z. Stieg, Elan Stopnitzky, Christof Teuscher, Chris Watkins, David H. Wolpert, J. Joshua Yang, Yan Yufik:
Thermodynamic Computing. CoRR abs/1911.01968 (2019) - [i16]Ravid Shwartz-Ziv, Alexander A. Alemi:
Information in Infinite Ensembles of Infinitely-Wide Neural Networks. CoRR abs/1911.09189 (2019) - [i15]Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Neural Tangents: Fast and Easy Infinite Neural Networks in Python. CoRR abs/1912.02803 (2019) - 2018
- [c7]Alexander A. Alemi, Ian Fischer:
GILBO: One Metric to Measure Them All. ICLR (Workshop) 2018 - [c6]Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy:
Fixing a Broken ELBO. ICML 2018: 159-168 - [c5]Alexander A. Alemi, Ian Fischer:
GILBO: One Metric to Measure Them All. NeurIPS 2018: 7037-7046 - [c4]Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi:
Watch Your Step: Learning Node Embeddings via Graph Attention. NeurIPS 2018: 9198-9208 - [i14]Alexander A. Alemi, Ian Fischer:
GILBO: One Metric to Measure Them All. CoRR abs/1802.04874 (2018) - [i13]Alexander A. Alemi, Ian Fischer, Joshua V. Dillon:
Uncertainty in the Variational Information Bottleneck. CoRR abs/1807.00906 (2018) - [i12]Alexander A. Alemi, Ian Fischer:
TherML: Thermodynamics of Machine Learning. CoRR abs/1807.04162 (2018) - [i11]Emily Fertig, Aryan Arbabi, Alexander A. Alemi:
β-VAEs can retain label information even at high compression. CoRR abs/1812.02682 (2018) - 2017
- [c3]Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander A. Alemi:
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. AAAI 2017: 4278-4284 - [c2]Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy:
Deep Variational Information Bottleneck. ICLR (Poster) 2017 - [i10]Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco, Rahul Sukthankar:
Motion Prediction Under Multimodality with Conditional Stochastic Networks. CoRR abs/1705.02082 (2017) - [i9]Lorien X. Hayden, Alexander A. Alemi, Paul H. Ginsparg, James P. Sethna:
Jeffrey's prior sampling of deep sigmoidal networks. CoRR abs/1705.10589 (2017) - [i8]Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi:
Watch Your Step: Learning Graph Embeddings Through Attention. CoRR abs/1710.09599 (2017) - [i7]Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy:
An Information-Theoretic Analysis of Deep Latent-Variable Models. CoRR abs/1711.00464 (2017) - [i6]Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous:
TensorFlow Distributions. CoRR abs/1711.10604 (2017) - 2016
- [c1]Geoffrey Irving, Christian Szegedy, Alexander A. Alemi, Niklas Eén, François Chollet, Josef Urban:
DeepMath - Deep Sequence Models for Premise Selection. NIPS 2016: 2235-2243 - [i5]Alexander A. Alemi, François Chollet, Geoffrey Irving, Christian Szegedy, Josef Urban:
DeepMath - Deep Sequence Models for Premise Selection. CoRR abs/1606.04442 (2016) - [i4]Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy:
Deep Variational Information Bottleneck. CoRR abs/1612.00410 (2016) - [i3]Ben Poole, Alexander A. Alemi, Jascha Sohl-Dickstein, Anelia Angelova:
Improved generator objectives for GANs. CoRR abs/1612.02780 (2016) - 2015
- [i2]Alexander A. Alemi, Paul Ginsparg:
Text Segmentation based on Semantic Word Embeddings. CoRR abs/1503.05543 (2015) - [i1]J. Massey Cashore, Xiaoting Zhao, Alexander A. Alemi, Yujia Liu, Peter I. Frazier:
Clustering via Content-Augmented Stochastic Blockmodels. CoRR abs/1505.06538 (2015)
Coauthor Index
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