Simple, scalable and effective clustering via one-dimensional projections
M. Charikar, L. Hu, M.H. Henzinger, M. Vötsch, E. Waingarten, in:, 37th Conference on Neural Information Processing Systems, 2023.

Knowledge distillation performs partial variance reduction
M. Safaryan, A. Krumes, D.-A. Alistarh, in:, 36th Conference on Neural Information Processing Systems, 2023.

Online learning of neural computations from sparse temporal feedback
L. Braun, T.P. Vogels, in:, Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 16437–16450.

Distributed principal component analysis with limited communication
F. Alimisis, P. Davies, B. Vandereycken, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 2823–2834.

M-FAC: Efficient matrix-free approximations of second-order information
E. Frantar, E. Kurtic, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 14873–14886.

Towards tight communication lower bounds for distributed optimisation
D.-A. Alistarh, J. Korhonen, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 7254–7266.

AC/DC: Alternating Compressed/DeCompressed training of deep neural networks
E.-A. Peste, E.B. Iofinova, A. Vladu, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 8557–8570.

PCA initialization for approximate message passing in rotationally invariant models
M. Mondelli, R. Venkataramanan, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 29616–29629.

When are solutions connected in deep networks?
Q. Nguyen, P. Bréchet, M. Mondelli, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021.

A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances in Neural Information Processing Systems, 2020, pp. 16398–16408.

Powerset convolutional neural networks
C. Wendler, D.-A. Alistarh, M. Püschel, in:, Neural Information Processing Systems Foundation, 2019, pp. 927–938.