Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm. - Hadi Daneshmand; Manuel Gomez-Rodriguez; Le Song; Bernhard Schölkopf (ICML, 2014)

Starting Small - Learning with Adaptive Sample Sizes. - Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001 (ICML, 2016)

Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy. - Aryan Mokhtari; Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001; Alejandro Ribeiro (NIPS, 2016)

DynaNewton - Accelerating Newton's Method for Machine Learning. - Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001 (CoRR, 2016)

Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm. - Manuel Gomez-Rodriguez; Le Song; Hadi Daneshmand; Bernhard Schölkopf (J. Mach. Learn. Res., 2016)

Accelerated Dual Learning by Homotopic Initialization. - Hadi Daneshmand; Hamed Hassani; Thomas Hofmann 001 (CoRR, 2017)

Escaping Saddles with Stochastic Gradients. - Hadi Daneshmand; Jonas Moritz Kohler; Aurélien Lucchi; Thomas Hofmann 001 (ICML, 2018)

Towards a Theoretical Understanding of Batch Normalization. - Jonas Moritz Kohler; Hadi Daneshmand; Aurélien Lucchi; Ming Zhou 007; Klaus Neymeyr; Thomas Hofmann 001 (CoRR, 2018)

Local Saddle Point Optimization: A Curvature Exploitation Approach. - Leonard Adolphs; Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001 (AISTATS, 2019)

Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. - Jonas Moritz Kohler; Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001; Ming Zhou 007; Klaus Neymeyr (AISTATS, 2019)

Mixing of Stochastic Accelerated Gradient Descent. - Peiyuan Zhang; Hadi Daneshmand; Thomas Hofmann 001 (CoRR, 2019)

Batch normalization provably avoids ranks collapse for randomly initialised deep networks. - Hadi Daneshmand; Jonas Moritz Kohler; Francis R. Bach; Thomas Hofmann 001; Aurélien Lucchi (NeurIPS, 2020)

Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. - Hadi Daneshmand; Jonas Moritz Kohler; Francis R. Bach; Thomas Hofmann 001; Aurélien Lucchi (CoRR, 2020)

Optimization for Neural Networks: Quest for Theoretical Understandings. - Hadi Daneshmand (2020)

Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. - Peiyuan Zhang; Antonio Orvieto; Hadi Daneshmand; Thomas Hofmann 001; Roy S. Smith (AISTATS, 2021)

Batch Normalization Orthogonalizes Representations in Deep Random Networks. - Hadi Daneshmand; Amir Joudaki; Francis R. Bach (NeurIPS, 2021)

Rethinking the Variational Interpretation of Accelerated Optimization Methods. - Peiyuan Zhang; Antonio Orvieto; Hadi Daneshmand (NeurIPS, 2021)

Polynomial-time sparse measure recovery. - Hadi Daneshmand; Francis R. Bach (CoRR, 2022)

Entropy Maximization with Depth: A Variational Principle for Random Neural Networks. - Amir Joudaki; Hadi Daneshmand; Francis R. Bach (CoRR, 2022)

Efficient displacement convex optimization with particle gradient descent. - Hadi Daneshmand; Jason D. Lee; Chi Jin 001 (ICML, 2023)

On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization. - Amir Joudaki; Hadi Daneshmand; Francis R. Bach (ICML, 2023)

Transformers learn to implement preconditioned gradient descent for in-context learning. - Kwangjun Ahn; Xiang Cheng; Hadi Daneshmand; Suvrit Sra (NeurIPS, 2023)

On the impact of activation and normalization in obtaining isometric embeddings at initialization. - Amir Joudaki; Hadi Daneshmand; Francis R. Bach (NeurIPS, 2023)

Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. - Alexandru Meterez; Amir Joudaki; Francesco Orabona; Alexander Immer; Gunnar Rätsch; Hadi Daneshmand (ICLR, 2024)

Transformers Learn Temporal Difference Methods for In-Context Reinforcement Learning. - Jiuqi Wang; Ethan Blaser; Hadi Daneshmand; Shangtong Zhang (CoRR, 2024)

Provable optimal transport with transformers: The essence of depth and prompt engineering. - Hadi Daneshmand (CoRR, 2024)

Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning. - Jiuqi Wang; Ethan Blaser; Hadi Daneshmand; Shangtong Zhang (ICLR, 2025)

Data Generation without Function Estimation. - Hadi Daneshmand; Ashkan Soleymani (CoRR, 2025)