• Wasserstein quantum Monte Carlo: a novel approach for solving the quantum many-body Schrödinger equation
  Kirill Neklyudov, Jannes Nys, Luca Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani
  Advances in Neural Information Processing Systems (NeurIPS), 2023, (Spotlight)
• Action matching: learning stochastic dynamics from samples
  Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani
  International Conference on Machine Learning (ICML), 2023
• Random edge coding: one-shot bits-back coding of large labeled graphs
  Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani
  International Conference on Machine Learning (ICML), 2023
• Compressing multisets with large alphabets using bits-back coding
  Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich
  IEEE Journal on Selected Areas in Information Theory, Special Issue on Modern Compression, 2023, Also presented in Data Compression Conference, 2021, (Oral Talk)
• Quantum hypernetworks: training binary neural networks in quantum superposition
  Juan Carrasquilla, Mohamed Hibat-Allah, Estelle Inack, Alireza Makhzani, Kirill Neklyudov, Graham Taylor, Giacomo Torlai
  arXiv:2301.08292 (Submitted to Quantum), 2023
• Improving mutual information estimation with annealed and energy-based bounds
  Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver, Roger Grosse, Alireza Makhzani
  International Conference on Learning Representations (ICLR), 2022
• Variational model inversion attacks
  Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani
  Advances in Neural Information Processing Systems (NeurIPS), 2021
• Your dataset is a multiset and you should compress it like one
  Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich
  Advances in Neural Information Processing Systems (NeurIPS) Workshop on Deep Generative Models and Downstream Applications, 2021, (Best Paper Award)
• Few shot image generation via implicit autoencoding of support sets
  Andy Huang, Kuan-Chieh Wang, Guillaume Rabusseau, Alireza Makhzani
  Advances in Neural Information Processing Systems (NeurIPS) Workshop on Meta-Learning, 2021
• Improving lossless compression rates via Monte Carlo bits-back coding
  Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris Maddison
  International Conference on Machine Learning (ICML), 2021, (Long Talk)
• Likelihood ratio exponential families
  Rob Brekelmans, Frank Nielsen, Alireza Makhzani, Aram Galstyan, Greg Steeg
  Advances in Neural Information Processing Systems (NeurIPS) Workshop on Deep Learning through Information Geometry, 2020
• Evaluating lossy compression rates of deep generative models
  Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse
  International Conference on Machine Learning (ICML), 2020, Also presented in NeurIPS Workshop on Bayesian Deep Learning, 2019, (Contributed Talk)
• Implicit autoencoders
  Alireza Makhzani
  arXiv:1805.09804, 2018
• Unsupervised representation learning with autoencoders
  Alireza Makhzani
  PhD Thesis, University of Toronto (Canada), 2018
• Starcraft II: a new challenge for reinforcement learning
  Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, others
  arXiv:1708.04782, 2017
• Pixelgan autoencoders
  Alireza Makhzani, Brendan Frey
  Advances in Neural Information Processing Systems (NeurIPS), 2017
• Adversarial autoencoders
  Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, Brendan Frey
  International Conference on Learning Representations (ICLR) Workshop, 2016
• Winner-take-all autoencoders
  Alireza Makhzani, Brendan Frey
  Advances in Neural Information Processing Systems (NeurIPS), 2015
• K-sparse autoencoders
  Alireza Makhzani, Brendan Frey
  International Conference on Learning Representations (ICLR), 2014
• Compressed sensing for jointly sparse signals
  Alireza Makhzani
  Masters Thesis, University of Toronto (Canada), 2012
• Distributed spectrum sensing in cognitive radios via graphical models
  Alireza Makhzani, Shahrokh Valaee
  5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013
• Reconstruction of jointly sparse signals using iterative hard thresholding
  Alireza Makhzani, Shahrokh Valaee
  IEEE International Conference on Communications (ICC), 2012
• Reconstruction of a generalized joint sparsity model using principal component analysis
  Alireza Makhzani, Shahrokh Valaee
  IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011