Ehsan Amid

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 57

Top Topics

Exponentiated Gradient

Dimensionality Reduction

Top Venues

Publications

Andrew Hard, Antonious M. Girgis, Ehsan Amid, Sean Augenstein, Lara McConnaughey, Rajiv Mathews, Rohan Anil
Learning from straggler clients in federated learning. CoRR (2024)
Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth
Tempered Calculus for ML: Application to Hyperbolic Model Embedding. CoRR (2024)
Christopher Fifty, Dennis Duan, Ronald G. Junkins, Ehsan Amid, Jure Leskovec, Christopher Ré, Sebastian Thrun
Context-Aware Meta-Learning. ICLR (2024)
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
Optimal Transport with Tempered Exponential Measures. AAAI (2024)
Manfred K. Warmuth, Wojciech Kotlowski, Matt Jones, Ehsan Amid
Noise misleads rotation invariant algorithms on sparse targets. CoRR (2024)
Richard Nock, Ehsan Amid, Manfred K. Warmuth
Boosting with Tempered Exponential Measures. NeurIPS (2023)
Christopher Fifty, Joseph M. Paggi, Ehsan Amid, Jure Leskovec, Ron Dror
Harnessing Simulation for Molecular Embeddings. CoRR (2023)
Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar
Distributionally Robust Post-hoc Classifiers under Prior Shifts. ICLR (2023)
Christopher Fifty, Dennis Duan, Ronald G. Junkins, Ehsan Amid, Jure Leskovec, Christopher Ré, Sebastian Thrun
Context-Aware Meta-Learning. CoRR (2023)
Jared Lichtarge, Ehsan Amid, Shankar Kumar, Tien-Ju Yang, Rohan Anil, Rajiv Mathews
Heterogeneous Federated Learning Using Knowledge Codistillation. CoRR (2023)
Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar
Distributionally Robust Post-hoc Classifiers under Prior Shifts. CoRR (2023)
Manfred K. Warmuth, Ehsan Amid
Open Problem: Learning sparse linear concepts by priming the features. COLT (2023)
Richard Nock, Ehsan Amid, Manfred K. Warmuth
Boosting with Tempered Exponential Measures. CoRR (2023)
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
Optimal Transport with Tempered Exponential Measures. CoRR (2023)
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)
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 (2023)
Ehsan Amid, Richard Nock, Manfred K. Warmuth
Clustering above Exponential Families with Tempered Exponential Measures. AISTATS (2023)
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu
To Aggregate or Not? Learning with Separate Noisy Labels. KDD (2023)
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. CoRR (2023)
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu
To Aggregate or Not? Learning with Separate Noisy Labels. CSW@WSDM (2023)
Ehsan Amid, Om Thakkar, Arun Narayanan, Rajiv Mathews, Françoise Beaufays
Extracting Targeted Training Data from ASR Models, and How to Mitigate It. CoRR (2022)
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu
To Aggregate or Not? Learning with Separate Noisy Labels. CoRR (2022)
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)
Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation. CoRR (2022)
Ehsan Amid, Richard Nock, Manfred K. Warmuth
Clustering above Exponential Families with Tempered Exponential Measures. CoRR (2022)
Ehsan Amid, Rohan Anil, Manfred K. Warmuth
LocoProp: Enhancing BackProp via Local Loss Optimization. AISTATS (2022)
Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth
Step-size Adaptation Using Exponentiated Gradient Updates. CoRR (2022)
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)
Ehsan Amid, Rohan Anil, Wojciech Kotlowski, Manfred K. Warmuth
Learning from Randomly Initialized Neural Network Features. CoRR (2022)
Sina Rezaei Aghdam, Ehsan Amid, Marija Furdek, Alexandre Graell i Amat
Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments. CAMAD (2021)
Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn
Efficiently Identifying Task Groupings for Multi-Task Learning. NeurIPS (2021)
Abhishek Kumar, Ehsan Amid
Constrained Instance and Class Reweighting for Robust Learning under Label Noise. CoRR (2021)
Sina Rezaei Aghdam, Ehsan Amid, Marija Furdek, Alexandre Graell i Amat
Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments. CoRR (2021)
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)
Negin Majidi, Ehsan Amid, Hossein Talebi, Manfred K. Warmuth
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond. CoRR (2021)
Ehsan Amid, Rohan Anil, Manfred K. Warmuth
LocoProp: Enhancing BackProp via Local Loss Optimization. CoRR (2021)
Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn
Efficiently Identifying Task Groupings for Multi-Task Learning. CoRR (2021)
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 (2021)
Ehsan Amid, Manfred K. Warmuth
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. UAI (2020)
Ehsan Amid, Manfred K. Warmuth
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. AAAI (2020)
Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn
Measuring and Harnessing Transference in Multi-Task Learning. CoRR (2020)
Ehsan Amid, Manfred K. Warmuth
Winnowing with Gradient Descent. COLT (2020)
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 (2020)
Hossein Talebi, Ehsan Amid, Peyman Milanfar, Manfred K. Warmuth
Rank-smoothed Pairwise Learning In Perceptual Quality Assessment. CoRR (2020)
Hossein Talebi, Ehsan Amid, Peyman Milanfar, Manfred K. Warmuth
Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment. ICIP (2020)
Ehsan Amid, Manfred K. Warmuth
Reparameterizing Mirror Descent as Gradient Descent. NeurIPS (2020)
Ehsan Amid, Manfred K. Warmuth
Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent. CoRR (2020)
Ehsan Amid, Manfred K. Warmuth
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. CoRR (2019)
Ehsan Amid, Manfred K. Warmuth
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. CoRR (2019)
Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. NeurIPS (2019)
Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. CoRR (2019)
Ehsan Amid, Manfred K. Warmuth
TriMap: Large-scale Dimensionality Reduction Using Triplets. CoRR (2019)
Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan
Two-temperature logistic regression based on the Tsallis divergence. AISTATS (2019)
Ehsan Amid, Manfred K. Warmuth
A more globally accurate dimensionality reduction method using triplets. CoRR (2018)
Ehsan Amid, Manfred K. Warmuth
Two-temperature logistic regression based on the Tsallis divergence. CoRR (2017)
Ehsan Amid, Aristides Gionis, Antti Ukkonen
Semi-supervised Kernel Metric Learning Using Relative Comparisons. CoRR (2016)
Ehsan Amid, Nikos Vlassis, Manfred K. Warmuth
t-Exponential Triplet Embedding. CoRR (2016)
Ehsan Amid, Aristides Gionis, Antti Ukkonen
A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons. ECML/PKDD (1) (2015)
Ehsan Amid, Onur Dikmen, Erkki Oja
Optimizing the Information Retrieval Trade-off in Data Visualization Using $α$-Divergence. CoRR (2015)
Ehsan Amid, Antti Ukkonen
Multiview Triplet Embedding: Learning Attributes in Multiple Maps. ICML (2015)
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)
Ehsan Amid
Bayesian Non-parametric Image Segmentation with Markov Random Field Prior. SCIA (2013)
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)