| 2025 | ICLR | Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning. | Jiuqi Wang, Ethan Blaser, Hadi Daneshmand, Shangtong Zhang |
| 2024 | ICLR | Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. | Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rtsch, Hadi Daneshmand |
| 2023 | ICML | Efficient displacement convex optimization with particle gradient descent. | Hadi Daneshmand, Jason D. Lee, Chi Jin |
| 2023 | ICML | On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization. | Amir Joudaki, Hadi Daneshmand, Francis R. Bach |
| 2021 | AISTATS | Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. | Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand, Thomas Hofmann, Roy S. Smith |
| 2019 | AISTATS | Local Saddle Point Optimization: A Curvature Exploitation Approach. | Leonard Adolphs, Hadi Daneshmand, Aurlien Lucchi, Thomas Hofmann |
| 2019 | AISTATS | Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. | Jonas Moritz Kohler, Hadi Daneshmand, Aurlien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr |
| 2018 | ICML | Escaping Saddles with Stochastic Gradients. | Hadi Daneshmand, Jonas Moritz Kohler, Aurlien Lucchi, Thomas Hofmann |
| 2016 | ICML | Starting Small - Learning with Adaptive Sample Sizes. | Hadi Daneshmand, Aurlien Lucchi, Thomas Hofmann |
| 2014 | ICML | Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm. | Hadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, Bernhard Schlkopf |