Yatin Dandi - RankMe

35 papers A* 7A 3Journal 24Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Deep Learning of Compositional Targets with Hierarchical Spectral Methods. CoRR Hugo Tabanelli, Yatin Dandi, Luca Pesce, Florent Krzakala
2026	J	`jnl`	Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining. CoRR Yunwei Ren, Yatin Dandi, Florent Krzakala, Jason D. Lee
2026	J	`jnl`	Rigorous Asymptotics for First-Order Algorithms Through the Dynamical Cavity Method. CoRR Yatin Dandi, David Gamarnik, Francisco Pernice, Lenka Zdeborová
2025	A	`conf`	A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities. AISTATS Yatin Dandi, Luca Pesce, Hugo Cui, Florent Krzakala, Yue M. Lu, Bruno Loureiro
2025	J	`jnl`	Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula. CoRR Matteo Vilucchio, Yatin Dandi, Cédric Gerbelot, Florent Krzakala
2025	A	`conf`	Fundamental computational limits of weak learnability in high-dimensional multi-index models. AISTATS Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborová, Bruno Loureiro, Florent Krzakala
2025	J	`jnl`	Fundamental limits of learning in sequence multi-index models and deep attention networks: High-dimensional asymptotics and sharp thresholds. CoRR Emanuele Troiani, Hugo Cui, Yatin Dandi, Florent Krzakala, Lenka Zdeborová
2025	A*	`conf`	Fundamental limits of learning in sequence multi-index models and deep attention networks: high-dimensional asymptotics and sharp thresholds. ICML Emanuele Troiani, Hugo Cui, Yatin Dandi, Florent Krzakala, Lenka Zdeborová
2025	J	`jnl`	Optimal Spectral Transitions in High-Dimensional Multi-Index Models. CoRR Leonardo Defilippis, Yatin Dandi, Pierre Mergny, Florent Krzakala, Bruno Loureiro
2025	J	`jnl`	The Computational Advantage of Depth: Learning High-Dimensional Hierarchical Functions with Gradient Descent. CoRR Yatin Dandi, Luca Pesce, Lenka Zdeborová, Florent Krzakala
2024	J	`jnl`	A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities. CoRR Yatin Dandi, Luca Pesce, Hugo Cui, Florent Krzakala, Yue M. Lu, Bruno Loureiro
2024	A*	`conf`	Asymptotics of feature learning in two-layer networks after one gradient-step. ICML Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro
2024	J	`jnl`	Asymptotics of feature learning in two-layer networks after one gradient-step. CoRR Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro
2024	J	`jnl`	Fundamental limits of weak learnability in high-dimensional multi-index models. CoRR Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborová, Bruno Loureiro, Florent Krzakala
2024	J	`jnl`	How Two-Layer Neural Networks Learn, One (Giant) Step at a Time. J. Mach. Learn. Res. Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan
2024	J	`jnl`	Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs. CoRR Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan
2024	A*	`conf`	Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs. ICML Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan
2024	J	`jnl`	Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions. CoRR Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Luca Pesce, Ludovic Stephan
2024	A*	`conf`	The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents. ICML Yatin Dandi, Emanuele Troiani, Luca Arnaboldi, Luca Pesce, Lenka Zdeborová, Florent Krzakala
2024	J	`jnl`	The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents. CoRR Yatin Dandi, Emanuele Troiani, Luca Arnaboldi, Luca Pesce, Lenka Zdeborová, Florent Krzakala
2023	J	`jnl`	A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning. CoRR Neha S. Wadia, Yatin Dandi, Michael I. Jordan
2023	J	`jnl`	Learning Two-Layer Neural Networks, One (Giant) Step at a Time. CoRR Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan
2023	J	`jnl`	Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective. CoRR Davide Ghio, Yatin Dandi, Florent Krzakala, Lenka Zdeborová
2023	A*	`conf`	Universality laws for Gaussian mixtures in generalized linear models. NeurIPS Yatin Dandi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro, Lenka Zdeborová
2022	J	`jnl`	Data-heterogeneity-aware Mixing for Decentralized Learning. CoRR Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich
2022	A*	`conf`	Implicit Gradient Alignment in Distributed and Federated Learning. AAAI Yatin Dandi, Luis Barba, Martin Jaggi
2021	A*	`conf`	Generalized Adversarially Learned Inference. AAAI Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar, Piyush Rai
2021	J	`jnl`	Implicit Gradient Alignment in Distributed and Federated Learning. CoRR Yatin Dandi, Luis Barba, Martin Jaggi
2021	J	`jnl`	NeurInt : Learning to Interpolate through Neural ODEs. CoRR Avinandan Bose, Aniket Das, Yatin Dandi, Piyush Rai
2021	J	`jnl`	Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis. CoRR Yatin Dandi, Arthur Jacot
2020	J	`jnl`	Generalized Adversarially Learned Inference. CoRR Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar, Piyush Rai
2020	A	`conf`	Jointly Trained Image and Video Generation using Residual Vectors. WACV Yatin Dandi, Aniket Das, Soumye Singhal, Vinay P. Namboodiri, Piyush Rai
2020	—	`conf`	Model-Agnostic Learning to Meta-Learn. Preregister@NeurIPS Arnout Devos, Yatin Dandi
2020	J	`jnl`	Model-Agnostic Learning to Meta-Learn. CoRR Arnout Devos, Yatin Dandi
2019	J	`jnl`	Jointly Trained Image and Video Generation using Residual Vectors. CoRR Yatin Dandi, Aniket Das, Soumye Singhal, Vinay P. Namboodiri, Piyush Rai