Reconstruction of a Generalized Joint Sparsity Model using Principal Component Analysis. - Alireza Makhzani; Shahrokh Valaee (CAMSAP, 2011)

Reconstruction of jointly sparse signals using iterative hard thresholding. - Alireza Makhzani; Shahrokh Valaee (ICC, 2012)

Distributed spectrum sensing in cognitive radios via graphical models. - Alireza Makhzani; Shahrokh Valaee (CAMSAP, 2013)

k-Sparse Autoencoders. - Alireza Makhzani; Brendan J. Frey (ICLR (Poster), 2014)

A Winner-Take-All Method for Training Sparse Convolutional Autoencoders. - Alireza Makhzani; Brendan J. Frey (CoRR, 2014)

Winner-Take-All Autoencoders. - Alireza Makhzani; Brendan J. Frey (NIPS, 2015)

Adversarial Autoencoders. - Alireza Makhzani; Jonathon Shlens; Navdeep Jaitly; Ian J. Goodfellow (CoRR, 2015)

PixelGAN Autoencoders. - Alireza Makhzani; Brendan J. Frey (NIPS, 2017)

StarCraft II: A New Challenge for Reinforcement Learning. - Oriol Vinyals; Timo Ewalds; Sergey Bartunov; Petko Georgiev; Alexander Sasha Vezhnevets; Michelle Yeo; Alireza Makhzani; Heinrich Küttler; John P. Agapiou; Julian Schrittwieser; John Quan; Stephen Gaffney; et al. (CoRR, 2017)

Implicit Autoencoders. - Alireza Makhzani (CoRR, 2018)

Evaluating Lossy Compression Rates of Deep Generative Models. - Sicong Huang 001; Alireza Makhzani; Yanshuai Cao; Roger B. Grosse (ICML, 2020)

Likelihood Ratio Exponential Families. - Rob Brekelmans; Frank Nielsen; Alireza Makhzani; Aram Galstyan; Greg Ver Steeg (CoRR, 2020)

Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. - Yangjun Ruan; Karen Ullrich; Daniel Severo 001; James Townsend; Ashish Khisti; Arnaud Doucet; Alireza Makhzani; Chris J. Maddison (ICML, 2021)

Variational Model Inversion Attacks. - Kuan-Chieh Wang; Yan Fu; Ke Li 011; Ashish Khisti; Richard S. Zemel; Alireza Makhzani (NeurIPS, 2021)

Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. - Rob Brekelmans; Sicong Huang 001; Marzyeh Ghassemi; Greg Ver Steeg; Roger Baker Grosse; Alireza Makhzani (ICLR, 2022)

Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples. - Kirill Neklyudov; Daniel Severo 001; Alireza Makhzani (CoRR, 2022)

Compressing Multisets With Large Alphabets. - Daniel Severo 001; James Townsend; Ashish Khisti; Alireza Makhzani; Karen Ullrich (IEEE J. Sel. Areas Inf. Theory, 2022)

One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding. - Daniel Severo 001; James Townsend; Ashish J. Khisti; Alireza Makhzani (ICML, 2023)

Action Matching: Learning Stochastic Dynamics from Samples. - Kirill Neklyudov; Rob Brekelmans; Daniel Severo 001; Alireza Makhzani (ICML, 2023)

Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. - Kirill Neklyudov; Jannes Nys; Luca A. Thiede; Juan Carrasquilla; Qiang Liu; Max Welling; Alireza Makhzani (NeurIPS, 2023)

Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition. - Juan Carrasquilla; Mohamed Hibat-Allah; Estelle M. Inack; Alireza Makhzani; Kirill Neklyudov; Graham W. Taylor; Giacomo Torlai (CoRR, 2023)

Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs. - Daniel Severo 001; James Townsend; Ashish Khisti; Alireza Makhzani (CoRR, 2023)

Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. - Wu Lin; Felix Dangel; Runa Eschenhagen; Kirill Neklyudov; Agustinus Kristiadi; Richard E. Turner; Alireza Makhzani (CoRR, 2023)

Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. - Wu Lin; Felix Dangel; Runa Eschenhagen; Juhan Bae; Richard E. Turner; Alireza Makhzani (ICML, 2024)

Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. - Wu Lin; Felix Dangel; Runa Eschenhagen; Kirill Neklyudov; Agustinus Kristiadi; Richard E. Turner; Alireza Makhzani (ICML, 2024)

A Computational Framework for Solving Wasserstein Lagrangian Flows. - Kirill Neklyudov; Rob Brekelmans; Alexander Tong 001; Lazar Atanackovic; Qiang Liu; Alireza Makhzani (ICML, 2024)

Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo. - Stephen Zhao; Rob Brekelmans; Alireza Makhzani; Roger Baker Grosse (ICML, 2024)

Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding. - Daniel Severo 001; Ashish Khisti; Alireza Makhzani (NeurIPS, 2024)