Model-Agnostic Learning to Meta-Learn. - Arnout Devos; Yatin Dandi (Preregister@NeurIPS, 2020)

Jointly Trained Image and Video Generation using Residual Vectors. - Yatin Dandi; Aniket Das; Soumye Singhal; Vinay P. Namboodiri; Piyush Rai (WACV, 2020)

Generalized Adversarially Learned Inference. - Yatin Dandi; Homanga Bharadhwaj; Abhishek Kumar 001; Piyush Rai (AAAI, 2021)

Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis. - Yatin Dandi; Arthur Jacot (CoRR, 2021)

NeurInt : Learning to Interpolate through Neural ODEs. - Avinandan Bose; Aniket Das; Yatin Dandi; Piyush Rai (CoRR, 2021)

Implicit Gradient Alignment in Distributed and Federated Learning. - Yatin Dandi; Luis Barba; Martin Jaggi (AAAI, 2022)

Data-heterogeneity-aware Mixing for Decentralized Learning. - Yatin Dandi; Anastasia Koloskova; Martin Jaggi; Sebastian U. Stich (CoRR, 2022)

Universality laws for Gaussian mixtures in generalized linear models. - Yatin Dandi; Ludovic Stephan; Florent Krzakala; Bruno Loureiro; Lenka Zdeborová (NeurIPS, 2023)

Learning Two-Layer Neural Networks, One (Giant) Step at a Time. - Yatin Dandi; Florent Krzakala; Bruno Loureiro; Luca Pesce; Ludovic Stephan (CoRR, 2023)

Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective. - Davide Ghio; Yatin Dandi; Florent Krzakala; Lenka Zdeborová (CoRR, 2023)

A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning. - Neha S. Wadia; Yatin Dandi; Michael I. Jordan (CoRR, 2023)

Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs. - Luca Arnaboldi 002; Yatin Dandi; Florent Krzakala; Bruno Loureiro; Luca Pesce; Ludovic Stephan (ICML, 2024)

Asymptotics of feature learning in two-layer networks after one gradient-step. - Hugo Cui; Luca Pesce; Yatin Dandi; Florent Krzakala; Yue M. Lu; Lenka Zdeborová; Bruno Loureiro (ICML, 2024)

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents. - Yatin Dandi; Emanuele Troiani; Luca Arnaboldi 002; Luca Pesce; Lenka Zdeborová; Florent Krzakala (ICML, 2024)

Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions. - Luca Arnaboldi 002; Yatin Dandi; Florent Krzakala; Luca Pesce; Ludovic Stephan (CoRR, 2024)

Fundamental limits of weak learnability in high-dimensional multi-index models. - Emanuele Troiani; Yatin Dandi; Leonardo Defilippis; Lenka Zdeborová; Bruno Loureiro; Florent Krzakala (CoRR, 2024)

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs. - Luca Arnaboldi 002; Yatin Dandi; Florent Krzakala; Bruno Loureiro; Luca Pesce; Ludovic Stephan (CoRR, 2024)

A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities. - Yatin Dandi; Luca Pesce; Hugo Cui; Florent Krzakala; Yue M. Lu; Bruno Loureiro (AISTATS, 2025)

Fundamental computational limits of weak learnability in high-dimensional multi-index models. - Emanuele Troiani; Yatin Dandi; Leonardo Defilippis; Lenka Zdeborová; Bruno Loureiro; Florent Krzakala (AISTATS, 2025)

Fundamental limits of learning in sequence multi-index models and deep attention networks: High-dimensional asymptotics and sharp thresholds. - Emanuele Troiani; Hugo Cui; Yatin Dandi; Florent Krzakala; Lenka Zdeborová (CoRR, 2025)

Optimal Spectral Transitions in High-Dimensional Multi-Index Models. - Leonardo Defilippis; Yatin Dandi; Pierre Mergny; Florent Krzakala; Bruno Loureiro (CoRR, 2025)

The Computational Advantage of Depth: Learning High-Dimensional Hierarchical Functions with Gradient Descent. - Yatin Dandi; Luca Pesce; Lenka Zdeborová; Florent Krzakala (CoRR, 2025)

Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula. - Matteo Vilucchio; Yatin Dandi; Cédric Gerbelot; Florent Krzakala (CoRR, 2025)