“Exploratory Causal Inference in SAEnce”
T. Mencattini*, R. Cadei*, F. Locatello
ICLR, 2026.

“High-dimensional Analysis of Synthetic Data Selection”
P. Rezaei, F. Kovacevic, F. Locatello*, M. Mondelli*
ICLR, 2026.

“Learning explicit single-cell dynamics using ODE representations”
J.P. von Bassewitz, A. Pervez, M. Fumero, M. Robinson, T. Karaletsos, F. Locatello
ICLR, 2026.

“Boomerang Distillation Enables Zero-Shot Model Size Interpolation”
S. Kangaslahti, N. V. Nayak, J. Geuter, M. Fumero, F. Locatello, D. Alvarez-Melis
ICLR, 2026.

“Navigating the Latent Space Dynamics of Neural Models“
M. Fumero, L. Moschella, E. Rodolà*, F. Locatello*
ICLR, 2026.

“Statistical and Structural Identifiability in Self-Supervised Learning”
W. Nelson, M. Fumero, T. Karaletsos, F. Locatello
ICLR, 2026.

“On the identifiability of causal graphs with multiple environments”
F. Montagna
ICLR, 2026.

“A Law of Data Reconstruction for Random Features (and Beyond)“
L. Iurada*, S. Bombari*, T. Tommasi, M. Mondelli*
ICLR, 2026.

“The Geometry of LLM Quantization: GPTQ as Babai’s Nearest Plane Algorithm”
J. Chen, Y. Shabanzadeh, E. Crnčević, T. Hoefler, D. Alistarh
ICLR, 2026.

“The Unseen Frontier: Pushing the Limits of LLM Sparsity with Surrogate-Free ADMM”
K. Lee, H. Jang, D. Lee, D. Alistarh, N. Lee
ICLR, 2026.

“Bridging the Gap Between Promise and Performance for FP4 Quantization”
V. Egiazarian, R. L. Castro, D. Kuznedelev, A. Panferov, S. Ashkboos, E. Kurtic, S. Pandit, A. N. Marques, M. Kurtz, T. Hoefler, D. Alistarh
ICLR, 2026.

“Beyond Outliers: A Study of Optimizers Under Quantization”
G. Vlassis, S. Ashkboos, A. Volkova, T. Hoefler, D. Alistarh
ICLR, 2026.

“FFT-Based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models”
I. Modoranu, M. Safaryan, E. Schultheis, M. Ryabinin, A. Chumachenko, D. Alistarh
ICLR, 2026.

“Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD”
N. Kalinin, J. Upadhyay, R. McKenna, C. H. Lampert
ICLR, 2026.

“ASIDE: Architectural Separation of Instructions and Data in Language Models”
E. Zverev, E. Kortukov, A. Panfilov, A. Volkova, R. Tabesh, S. Lapuschkin, W. Samek, C. H. Lampert
ICLR, 2026.

“Representing local protein environments with machine learning force fields”
M. Bojan, S. Vedula, A. Maddipatla, N. B. Sellam, F. Napoli, P. Standee, A. M. Bronstein
ICLR, 2026.

“Differentially Private Federated k-Means Clustering with Server-Side Data”
J. Scott, C. H. Lampert, D. Saulpic
ICML, 2025

“Logic Gate Neural Networks are Good for Verification”
F. Kresse, E. Yu, C. H. Lampert, T. A. Henzinger
NeuS, 2025

“Generalization in Multi-Objective Machine Learning”
P. Súkeník, C. H. Lampert
Neural Computing & Applications, 2025

“Differentially Private Continual Release of Histograms and Related Queries”
M. Henzinger, A. R. Sricharan, T. A. Steiner
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025

“Near-Optimal Differentially Private Graph Algorithms via the Multidimensional Above Threshold Mechanism”
L. Dhulipala, M. Henzinger, G. Z. Li, Q. C. Liu, A. R. Sricharan, L. Zhu
ESA 2025

“Improved Differentially Private Continual Observation Using Group Algebra”
M. Henzinger, J. Upadhyay
SODA 2025

“Privacy for Free in the Over-Parameterized Regime“
S. Bombari, M. Mondelli
arXiv, 2024

“Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing“
R. Venkataramanan, K. Kögler, and M. Mondelli
ICML, 2022

“Polar Coded Computing: The Role of the Scaling Exponent“
D. Fathollahi, M. Mondelli
ISIT, 2022

“Fairness-Aware PAC Learning from Corrupted Data”
N. Konstantinov, C. H. Lampert
JMLR, 2022

“Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks”
B. Prach, C. H. Lampert
ECCV, 2022

“Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks“
A. Shevchenko, V. Kungurtsev, M. Mondelli
JMLR, 2022

“Approximate Message Passing with Spectral Initialization for Generalized Linear Models“
M. Mondelli, R. Venkataramanan
AISTATS, 2021

“AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks”
A. Peste, E. Iofinova, A. Vladu, D. Alistarhx
NeurIPS, 2021

“Distributed Principal Component Analysis with Limited Communication“
F. Alimisis, P. Davies, B. Vandereycken, D. Alistarh
NeurIPS, 2021

“When Are Solutions Connected in Deep Networks?“
Q. Nguyen, P. Bréchet, M. Mondelli
NeurIPS, 2021

“M-FAC: Efficient Matrix-Free Approximations of Second-Order Information“
E. Frantar, E. Kurtic, D. Alistarh
NeurIPS, 2021

“The inductive bias of ReLU networks on orthogonally separable data”
M. Phoung, C. H. Lampert
ICLR, 2021

“Byzantine-Resilient Non-Convex Stochastic Gradient Descent“
Z. Allen-Zhu, F. Ebrahimianghazani, J. Li, D. Alistarh
ICLR, 2021

“Towards Tight Communication Lower Bounds for Distributed Optimisation“
J. H. Korhonen, D. Alistarh
NeurIPS, 2021

“Asynchronous Decentralized SGD with Quantized and Local Updates”
G. Nadiradze, A. Sabour, P. Davies, S. Li, D. Alistarh
NeurIPS, 2021

“PCA Initialization for Approximate Message Passing in Rotationally Invariant Models“
M. Mondelli, R. Venkataramanan
NeurIPS, 2021

“Approximate Message Passing with Spectral Initialization for Generalized Linear Models”
M. Mondelli, R. Venkataramanan
AISTATS, 2021

“Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks”
Q. Nguyen, M. Mondelli, G. F. Montufar
ICML, 2021

“Communication-Efficient Distributed Optimization with Quantized Preconditioners”
F. Alimisis, P. Davies, D. Alistarh
ICML, 2021

“Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis”
S. E. Ojavee, A. Kousathanas, D. T. Banos, E. J. Orliac, M. Patxot, K. Läll, R. Mägi, K. Fischer, Z. Kutalik, M. R. Robinson 
Nature Communications

“Parallelism versus Latency in Simplified Successive-Cancellation Decoding of Polar Codes”
S. A. Hashemi, M. Mondelli, A. Fazeli, A. Vardy, J. Cioffi, A. Goldsmith
ISIT, 2021

“Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller Codes”
D. Fathollahi, N. Farsad, S. A. Hashemi, M. Mondelli
ISIT, 2021

“Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent“
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, D. Alistarh
AAAI, 2021

“Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks”
T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, A. Peste
JMLR, 2021

“Sublinear Latency for Simplified Successive Cancellation Decoding of Polar Codes”
M. Mondelli, S. A. Hashemi, J. Cioffi, A. Goldsmith
IEEE Transactions on Wireless Communications, 2021

“Optimal Combination of Linear and Spectral Estimators for Generalized Linear Models”
M. Mondelli, C. Thrampoulidis, R. Venkataramanan
FoCM, 2021

“Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology”
Q. Nguyen, M. Mondelli
NeurIPS, 2020

“WoodFisher: Efficient Second-Order Approximation for Neural Network Compression“
S. P. Singh, D. Alistarh
NeurIPS, 2020

“Unsupervised object-centric video generation and decomposition in 3D”
P. Handerson, C. H. Lampert
NeurIPS, 2020

“Relaxed Scheduling for Scalable Belief Propagation“
V. Aksenov, D. Alistarh, J. H. Korhonen
NeurIPS, 2020

“Binary Linear Codes With Optimal Scaling: Polar Codes With Large Kernels”
A. Fazeli, H. Hassani, M. Mondelli, A. Vardy
IEEE Transactions on Information Theory, 2020

“Does SGD Implicitly Optimize for Smoothness?”
V. Volhejn, C. H. Lampert
GCPR, 2020

“Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks“
A. Shevchenko, M. Mondelli
ICML, 2020

“On the Sample Complexity of Adversarial Multi-Source PAC Learning“
N. Konstantinov, E. Frantar, D. Alistarh, C. Lampert
ICML, 2020

“Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits“
M. Patxot, M. Robinson
Nature Communications, 2020

“Bayesian reassessment of the epigenetic architecture of complex traits”
D. Trejo Banos, M. Robinson
Nature Communications, 2020

“Functional vs. parametric equivalence of ReLU networks“
M. Phoung, C. H. Lampert
ICLR, 2020

“Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios”
A. Royer, C. H. Lampert
WACV, 2020

“Rate-flexible fast polar decoders”
S. A. Hashemi, C. Condo, M. Mondelli, W. J. Gross
IEEE Transactions on Signal Processing

“Distillation-Based Training for Multi-Exit Architectures”
M. Phuong, C. H. Lampert
ICCV, 2019

“Towards Understanding Knowledge Distillation”
M. Phuong, C. H. Lampert
ICML, 2019

“On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition“
M. Mondelli, A. Montanari
AISTATS, 2019