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ORCIDEhsan Amid
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- affiliation: Google Brain
- affiliation (former): University of California, Santa Cruz
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2020 – today
- 2024
[c26]Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth:
Optimal Transport with Tempered Exponential Measures. AAAI 2024: 10838-10846- [c25]Christopher Fifty, Dennis Duan, Ronald G. Junkins, Ehsan Amid, Jure Leskovec, Christopher Ré, Sebastian Thrun:
Context-Aware Meta-Learning. ICLR 2024 - [i37]Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth:
Tempered Calculus for ML: Application to Hyperbolic Model Embedding. CoRR abs/2402.04163 (2024) - [i36]Manfred K. Warmuth, Wojciech Kotlowski, Matt Jones, Ehsan Amid:
Noise misleads rotation invariant algorithms on sparse targets. CoRR abs/2403.02697 (2024) - [i35]Andrew Hard, Antonious M. Girgis, Ehsan Amid, Sean Augenstein, Lara McConnaughey, Rajiv Mathews, Rohan Anil:
Learning from straggler clients in federated learning. CoRR abs/2403.09086 (2024) - [i34]Christopher Fifty, Ronald G. Junkins, Dennis Duan, Aniketh Iger, Jerry W. Liu, Ehsan Amid, Sebastian Thrun, Christopher Ré:
Restructuring Vector Quantization with the Rotation Trick. CoRR abs/2410.06424 (2024) - 2023
- [j1]Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth:
Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation. Trans. Mach. Learn. Res. 2023 (2023) - [c24]Ehsan Amid, Richard Nock, Manfred K. Warmuth:
Clustering above Exponential Families with Tempered Exponential Measures. AISTATS 2023: 2994-3017 - [c23]Manfred K. Warmuth, Ehsan Amid:
Open Problem: Learning sparse linear concepts by priming the features. COLT 2023: 5937-5942 - [c22]Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu:
To Aggregate or Not? Learning with Separate Noisy Labels. CSW@WSDM 2023: 8-43 - [c21]Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar:
Distributionally Robust Post-hoc Classifiers under Prior Shifts. ICLR 2023 - [c20]Jiaheng Wei
, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu:
To Aggregate or Not? Learning with Separate Noisy Labels. KDD 2023: 2523-2535 - [c19]Richard Nock, Ehsan Amid, Manfred K. Warmuth:
Boosting with Tempered Exponential Measures. NeurIPS 2023 - [i33]Christopher Fifty, Joseph M. Paggi, Ehsan Amid, Jure Leskovec, Ron O. Dror:
Harnessing Simulation for Molecular Embeddings. CoRR abs/2302.02055 (2023) - [i32]Richard Nock, Ehsan Amid, Manfred K. Warmuth:
Boosting with Tempered Exponential Measures. CoRR abs/2306.05487 (2023) - [i31]George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson:
Benchmarking Neural Network Training Algorithms. CoRR abs/2306.07179 (2023) - [i30]Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth:
Optimal Transport with Tempered Exponential Measures. CoRR abs/2309.04015 (2023) - [i29]Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar:
Distributionally Robust Post-hoc Classifiers under Prior Shifts. CoRR abs/2309.08825 (2023) - [i28]Jared Lichtarge, Ehsan Amid, Shankar Kumar, Tien-Ju Yang, Rohan Anil, Rajiv Mathews:
Heterogeneous Federated Learning Using Knowledge Codistillation. CoRR abs/2310.02549 (2023) - [i27]Christopher Fifty, Dennis Duan, Ronald G. Junkins, Ehsan Amid, Jure Leskovec, Christopher Ré, Sebastian Thrun:
Context-Aware Meta-Learning. CoRR abs/2310.10971 (2023) - [i26]Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth:
The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. CoRR abs/2311.13459 (2023) - 2022
- [c18]Ehsan Amid, Rohan Anil, Manfred K. Warmuth:
LocoProp: Enhancing BackProp via Local Loss Optimization. AISTATS 2022: 9626-9642 - [c17]Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. ICML 2022: 517-535 - [c16]Ehsan Amid, Om Dipakbhai Thakkar, Arun Narayanan, Rajiv Mathews, Françoise Beaufays:
Extracting Targeted Training Data from ASR Models, and How to Mitigate It. INTERSPEECH 2022: 2803-2807 - [i25]Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth:
Step-size Adaptation Using Exponentiated Gradient Updates. CoRR abs/2202.00145 (2022) - [i24]Ehsan Amid, Rohan Anil, Wojciech Kotlowski, Manfred K. Warmuth:
Learning from Randomly Initialized Neural Network Features. CoRR abs/2202.06438 (2022) - [i23]Ehsan Amid, Om Thakkar, Arun Narayanan, Rajiv Mathews, Françoise Beaufays:
Extracting Targeted Training Data from ASR Models, and How to Mitigate It. CoRR abs/2204.08345 (2022) - [i22]Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu:
To Aggregate or Not? Learning with Separate Noisy Labels. CoRR abs/2206.07181 (2022) - [i21]Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth:
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation. CoRR abs/2209.07080 (2022) - [i20]Ehsan Amid, Richard Nock, Manfred K. Warmuth:
Clustering above Exponential Families with Tempered Exponential Measures. CoRR abs/2211.02765 (2022) - 2021
- [c15]Manfred K. Warmuth, Wojciech Kotlowski, Ehsan Amid:
A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer. ALT 2021: 1214-1236 - [c14]Sina Rezaei Aghdam, Ehsan Amid, Marija Furdek, Alexandre Graell i Amat:
Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments. CAMAD 2021: 1-6 - [c13]Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn:
Efficiently Identifying Task Groupings for Multi-Task Learning. NeurIPS 2021: 27503-27516 - [i19]Sina Rezaei Aghdam, Ehsan Amid, Marija Furdek, Alexandre Graell i Amat:
Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments. CoRR abs/2102.10639 (2021) - [i18]Negin Majidi, Ehsan Amid, Hossein Talebi, Manfred K. Warmuth:
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond. CoRR abs/2104.01493 (2021) - [i17]Ehsan Amid, Rohan Anil, Manfred K. Warmuth:
LocoProp: Enhancing BackProp via Local Loss Optimization. CoRR abs/2106.06199 (2021) - [i16]Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn:
Efficiently Identifying Task Groupings for Multi-Task Learning. CoRR abs/2109.04617 (2021) - [i15]Abhishek Kumar, Ehsan Amid:
Constrained Instance and Class Reweighting for Robust Learning under Label Noise. CoRR abs/2111.05428 (2021) - [i14]Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. CoRR abs/2112.00193 (2021) - 2020
- [b1]Ehsan Amid:
Tempered Bregman Divergence for Continuous and Discrete Time Mirror Descent and Robust Classification. University of California, Santa Cruz, USA, 2020 - [c12]Ehsan Amid, Manfred K. Warmuth:
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. AAAI 2020: 3179-3186 - [c11]Ehsan Amid, Manfred K. Warmuth:
Winnowing with Gradient Descent. COLT 2020: 163-182 - [c10]Hossein Talebi, Ehsan Amid, Peyman Milanfar, Manfred K. Warmuth:
Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment. ICIP 2020: 3413-3417 - [c9]Ehsan Amid, Manfred K. Warmuth:
Reparameterizing Mirror Descent as Gradient Descent. NeurIPS 2020 - [c8]Ehsan Amid, Manfred K. Warmuth:
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. UAI 2020: 81-90 - [i13]Ehsan Amid, Manfred K. Warmuth:
Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent. CoRR abs/2002.10487 (2020) - [i12]Manfred K. Warmuth, Wojciech Kotlowski, Ehsan Amid:
A case where a spindly two-layer linear network whips any neural network with a fully connected input layer. CoRR abs/2010.08625 (2020) - [i11]Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn:
Measuring and Harnessing Transference in Multi-Task Learning. CoRR abs/2010.15413 (2020) - [i10]Hossein Talebi, Ehsan Amid, Peyman Milanfar, Manfred K. Warmuth:
Rank-smoothed Pairwise Learning In Perceptual Quality Assessment. CoRR abs/2011.10893 (2020)
2010 – 2019
- 2019
- [c7]Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan:
Two-temperature logistic regression based on the Tsallis divergence. AISTATS 2019: 2388-2396 - [c6]Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren:
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. NeurIPS 2019: 14987-14996 - [i9]Ehsan Amid, Manfred K. Warmuth:
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. CoRR abs/1902.04107 (2019) - [i8]Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren:
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. CoRR abs/1906.03361 (2019) - [i7]Ehsan Amid, Manfred K. Warmuth:
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. CoRR abs/1909.04803 (2019) - [i6]Ehsan Amid, Manfred K. Warmuth:
TriMap: Large-scale Dimensionality Reduction Using Triplets. CoRR abs/1910.00204 (2019) - 2018
- [i5]Ehsan Amid, Manfred K. Warmuth:
A more globally accurate dimensionality reduction method using triplets. CoRR abs/1803.00854 (2018) - 2017
- [i4]Ehsan Amid, Manfred K. Warmuth:
Two-temperature logistic regression based on the Tsallis divergence. CoRR abs/1705.07210 (2017) - 2016
- [i3]Ehsan Amid, Nikos Vlassis, Manfred K. Warmuth:
t-Exponential Triplet Embedding. CoRR abs/1611.09957 (2016) - [i2]Ehsan Amid, Aristides Gionis, Antti Ukkonen:
Semi-supervised Kernel Metric Learning Using Relative Comparisons. CoRR abs/1612.00086 (2016) - 2015
- [c5]Ehsan Amid, Antti Ukkonen:
Multiview Triplet Embedding: Learning Attributes in Multiple Maps. ICML 2015: 1472-1480 - [c4]Ehsan Amid, Aristides Gionis, Antti Ukkonen:
A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons. ECML/PKDD (1) 2015: 219-234 - [i1]Ehsan Amid, Onur Dikmen, Erkki Oja:
Optimizing the Information Retrieval Trade-off in Data Visualization Using $α$-Divergence. CoRR abs/1505.05821 (2015) - 2014
- [c3]Ehsan Amid, Annamaria Mesaros, Kalle J. Palomäki, Jorma Laaksonen, Mikko Kurimo:
Unsupervised feature extraction for multimedia event detection and ranking using audio content. ICASSP 2014: 5939-5943 - 2013
- [c2]Ehsan Amid:
Bayesian Non-parametric Image Segmentation with Markov Random Field Prior. SCIA 2013: 76-84 - [c1]Satoru Ishikawa, Markus Koskela, Mats Sjöberg, Jorma Laaksonen, Erkki Oja, Ehsan Amid, Kalle J. Palomäki, Annamaria Mesaros, Mikko Kurimo:
PicSOM Experiments in TRECVID 2013. TRECVID 2013
Coauthor Index
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last updated on 2024-11-19 20:44 CET by the dblp team
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