Bayesian Non-parametric Image Segmentation with Markov Random Field Prior. - Ehsan Amid (SCIA, 2013)

PicSOM Experiments in TRECVID 2013. - Satoru Ishikawa; Markus Koskela; Mats Sjöberg; Jorma Laaksonen; Erkki Oja; Ehsan Amid; Kalle J. Palomäki; Annamaria Mesaros; Mikko Kurimo (TRECVID, 2013)

Unsupervised feature extraction for multimedia event detection and ranking using audio content. - Ehsan Amid; Annamaria Mesaros; Kalle J. Palomäki; Jorma Laaksonen; Mikko Kurimo (ICASSP, 2014)

Multiview Triplet Embedding: Learning Attributes in Multiple Maps. - Ehsan Amid; Antti Ukkonen (ICML, 2015)

A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons. - Ehsan Amid; Aristides Gionis; Antti Ukkonen (ECML/PKDD (1), 2015)

Optimizing the Information Retrieval Trade-off in Data Visualization Using $α$-Divergence. - Ehsan Amid; Onur Dikmen; Erkki Oja (CoRR, 2015)

Semi-supervised Kernel Metric Learning Using Relative Comparisons. - Ehsan Amid; Aristides Gionis; Antti Ukkonen (CoRR, 2016)

t-Exponential Triplet Embedding. - Ehsan Amid; Nikos Vlassis; Manfred K. Warmuth (CoRR, 2016)

Two-temperature logistic regression based on the Tsallis divergence. - Ehsan Amid; Manfred K. Warmuth (CoRR, 2017)

A more globally accurate dimensionality reduction method using triplets. - Ehsan Amid; Manfred K. Warmuth (CoRR, 2018)

Two-temperature logistic regression based on the Tsallis divergence. - Ehsan Amid; Manfred K. Warmuth; Sriram Srinivasan 004 (AISTATS, 2019)

Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. - Ehsan Amid; Manfred K. Warmuth; Rohan Anil; Tomer Koren (NeurIPS, 2019)

TriMap: Large-scale Dimensionality Reduction Using Triplets. - Ehsan Amid; Manfred K. Warmuth (CoRR, 2019)

An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. - Ehsan Amid; Manfred K. Warmuth (AAAI, 2020)

Winnowing with Gradient Descent. - Ehsan Amid; Manfred K. Warmuth (COLT, 2020)

Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment. - Hossein Talebi; Ehsan Amid; Peyman Milanfar; Manfred K. Warmuth (ICIP, 2020)

Reparameterizing Mirror Descent as Gradient Descent. - Ehsan Amid; Manfred K. Warmuth (NeurIPS, 2020)

Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. - Ehsan Amid; Manfred K. Warmuth (UAI, 2020)

Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent. - Ehsan Amid; Manfred K. Warmuth (CoRR, 2020)

A case where a spindly two-layer linear network whips any neural network with a fully connected input layer. - Manfred K. Warmuth; Wojciech Kotlowski; Ehsan Amid (CoRR, 2020)

Measuring and Harnessing Transference in Multi-Task Learning. - Christopher Fifty; Ehsan Amid; Zhe Zhao 001; Tianhe Yu; Rohan Anil; Chelsea Finn (CoRR, 2020)

Tempered Bregman Divergence for Continuous and Discrete Time Mirror Descent and Robust Classification. - Ehsan Amid (2020)

A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer. - Manfred K. Warmuth; Wojciech Kotlowski; Ehsan Amid (ALT, 2021)

Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments. - Sina Rezaei Aghdam; Ehsan Amid; Marija Furdek; Alexandre Graell i Amat (CAMAD, 2021)

Efficiently Identifying Task Groupings for Multi-Task Learning. - Chris Fifty; Ehsan Amid; Zhe Zhao 001; Tianhe Yu; Rohan Anil; Chelsea Finn (NeurIPS, 2021)

Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond. - Negin Majidi; Ehsan Amid; Hossein Talebi; Manfred K. Warmuth (CoRR, 2021)

Constrained Instance and Class Reweighting for Robust Learning under Label Noise. - Abhishek Kumar; Ehsan Amid (CoRR, 2021)

LocoProp: Enhancing BackProp via Local Loss Optimization. - Ehsan Amid; Rohan Anil; Manfred K. Warmuth (AISTATS, 2022)

Public Data-Assisted Mirror Descent for Private Model Training. - Ehsan Amid; Arun Ganesh; Rajiv Mathews; Swaroop Ramaswamy; Shuang Song 001; Thomas Steinke 002; Vinith M. Suriyakumar; Om Thakkar 001; Abhradeep Thakurta (ICML, 2022)

Extracting Targeted Training Data from ASR Models, and How to Mitigate It. - Ehsan Amid; Om Dipakbhai Thakkar; Arun Narayanan; Rajiv Mathews; Françoise Beaufays (INTERSPEECH, 2022)

Step-size Adaptation Using Exponentiated Gradient Updates. - Ehsan Amid; Rohan Anil; Christopher Fifty; Manfred K. Warmuth (CoRR, 2022)

Learning from Randomly Initialized Neural Network Features. - Ehsan Amid; Rohan Anil; Wojciech Kotlowski; Manfred K. Warmuth (CoRR, 2022)

Layerwise Bregman Representation Learning with Applications to Knowledge Distillation. - Ehsan Amid; Rohan Anil; Christopher Fifty; Manfred K. Warmuth (CoRR, 2022)

Clustering above Exponential Families with Tempered Exponential Measures. - Ehsan Amid; Richard Nock; Manfred K. Warmuth (AISTATS, 2023)

Open Problem: Learning sparse linear concepts by priming the features. - Manfred K. Warmuth; Ehsan Amid (COLT, 2023)

To Aggregate or Not? Learning with Separate Noisy Labels. - Jiaheng Wei; Zhaowei Zhu; Tianyi Luo; Ehsan Amid; Abhishek Kumar; Yang Liu 018 (CSW@WSDM, 2023)

Distributionally Robust Post-hoc Classifiers under Prior Shifts. - Jiaheng Wei; Harikrishna Narasimhan; Ehsan Amid; Wen-Sheng Chu; Yang Liu 018; Abhishek Kumar (ICLR, 2023)

Boosting with Tempered Exponential Measures. - Richard Nock; Ehsan Amid; Manfred K. Warmuth (NeurIPS, 2023)

Harnessing Simulation for Molecular Embeddings. - Christopher Fifty; Joseph M. Paggi; Ehsan Amid; Jure Leskovec; Ron O. Dror (CoRR, 2023)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (CoRR, 2023)

Heterogeneous Federated Learning Using Knowledge Codistillation. - Jared Lichtarge; Ehsan Amid; Shankar Kumar; Tien-Ju Yang; Rohan Anil; Rajiv Mathews (CoRR, 2023)

The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. - Ehsan Amid; Frank Nielsen; Richard Nock; Manfred K. Warmuth (CoRR, 2023)

Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation. - Ehsan Amid; Rohan Anil; Christopher Fifty; Manfred K. Warmuth (Trans. Mach. Learn. Res., 2023)

Optimal Transport with Tempered Exponential Measures. - Ehsan Amid; Frank Nielsen; Richard Nock; Manfred K. Warmuth (AAAI, 2024)

Context-Aware Meta-Learning. - Christopher Fifty; Dennis Duan; Ronald G. Junkins; Ehsan Amid; Jure Leskovec; Christopher Ré; Sebastian Thrun (ICLR, 2024)

Hyperbolic Embeddings of Supervised Models. - Richard Nock; Ehsan Amid; Frank Nielsen; Alexander Soen; Manfred K. Warmuth (NeurIPS, 2024)

Tempered Calculus for ML: Application to Hyperbolic Model Embedding. - Richard Nock; Ehsan Amid; Frank Nielsen; Alexander Soen; Manfred K. Warmuth (CoRR, 2024)

Noise misleads rotation invariant algorithms on sparse targets. - Manfred K. Warmuth; Wojciech Kotlowski; Matt Jones 002; Ehsan Amid (CoRR, 2024)

Learning from straggler clients in federated learning. - Andrew Hard; Antonious M. Girgis; Ehsan Amid; Sean Augenstein; Lara McConnaughey; Rajiv Mathews; Rohan Anil (CoRR, 2024)

Restructuring Vector Quantization with the Rotation Trick. - Christopher Fifty; Ronald G. Junkins; Dennis Duan; Aniketh Iger; Jerry W. Liu; Ehsan Amid; Sebastian Thrun; Christopher Ré (CoRR, 2024)

How rotation invariant algorithms are fooled by noise on sparse targets. - Manfred K. Warmuth; Wojciech Kotlowski; Matt Jones 002; Ehsan Amid (ALT, 2025)

Restructuring Vector Quantization with the Rotation Trick. - Christopher Fifty; Ronald Guenther Junkins; Dennis Duan; Aniketh Iyengar; Jerry Weihong Liu; Ehsan Amid; Sebastian Thrun; Christopher Ré (ICLR, 2025)

Principled Foundations for Preference Optimization. - Wenxuan Zhou; Shujian Zhang; Brice Magdalou; John Lambert; Ehsan Amid; Richard Nock; Andrew Hard (CoRR, 2025)