Accepted Papers (2023)

Dynamic Control of Queuing Networks via Differentiable Discrete-Event Simulation, Ethan Che, Hongseok Namkoong, Jing Dong

Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More, Sudhanshu Chanpuriya, Cameron N Musco

GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies, Takahiro Mimori, Michiaki Hamada

DNArch: Learning Convolutional Neural Architectures by Backpropagation, David W. Romero, Neil Zeghidour

From Perception to Programs: Regularize, Overparameterize, and Amortize, Hao Tang, Kevin Ellis

Efficient Surrogate Gradients for Training Spiking Neural Networks, Hao Lin, Shikuang Deng, Shi Gu

Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information, Arman Zharmagambetov, Brandon Amos, Aaron M Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian

PMaF: Deep Declarative Layers for Principal Matrix Features, Zhiwei Xu, Hao Wang, Yanbin Liu, Stephen Gould

Differentiable Set Partitioning, Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E Vogt

Interpretable Neural-Symbolic Concept Reasoning, Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frederic Precioso, Mateja Jamnik, Giuseppe Marra

Optimizing probability of barrier crossing with differentiable simulators, Martin Sipka, Johannes C. B. Dietschreit, Michal Pavelka, Lukáš Grajciar, Rafael Gomez-Bombarelli

SIMPLE: A Gradient Estimator for $k$-subset Sampling, Kareem Ahmed, Zhe Zeng, Mathias Niepert, Guy Van den Broeck

Differentiable MaxSAT Message Passing, Francesco Alesiani, Cristóbal Corvalán Morbiducci, Markus Zopf

Dilated Convolution with Learnable Spacings: beyond bilinear interpolation, Ismail Khalfaoui Hassani, Thomas Pellegrini, Timothée Masquelier

Data Models for Dataset Drift Controls in Machine Learning With Optical Images, Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti

SelMix: Selective Mixup Fine Tuning for Optimizing Non-Decomposable Metrics, Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori, Kunal Samanta, Yuhei Umeda, Venkatesh Babu Radhakrishnan

Differentiable Forward Projector for X-ray Computed Tomography, Hyojin Kim, Kyle Champley

End-to-end Differentiable Clustering with Associative Memories, Bishwajit Saha, Dmitry Krotov, Mohammed J Zaki, Parikshit Ram

Probabilistic Task-Adaptive Graph Rewiring, Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, Christopher Morris

TaskMet: Task-Driven Metric Learning for Model Learning, Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos

Plateau-Reduced Differentiable Path Tracing , Michael Fischer, Tobias Ritschel

Distributions for Compositionally Differentiating Parametric Discontinuities, Jesse Michel, Kevin Mu, Xuanda Yang, Sai Praveen Bangaru, Elias Rojas Collins, Gilbert Bernstein, Jonathan Ragan-Kelley, Michael Carbin, Tzu-Mao Li

JAX FDM: A differentiable solver for inverse form-finding, Rafael Pastrana, Deniz Oktay, Ryan P Adams, Sigrid Adriaenssens

A Unified Approach to Count-Based Weakly-Supervised Learning, Vinay Shukla, Zhe Zeng, Kareem Ahmed, Guy Van den Broeck

Some challenges of calibrating differentiable agent-based models, Arnau Quera-Bofarull, Joel Dyer, Ani Calinescu, Michael Wooldridge

Investigating Axis-Aligned Differentiable Trees through Neural Tangent Kernels, Ryuichi Kanoh, Mahito Sugiyama

EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search, Qian Jiang, Xiaofan Zhang, Deming Chen, Minh N. Do, Raymond A. Yeh

Differentiable Search of Evolutionary Trees from Leaves, Ramith Hettiarachchi, Avi Swartz, Sergey Ovchinnikov

Differentiable Causal Discovery with Smooth Acyclic Orientations, Riccardo Massidda, Francesco Landolfi, Martina Cinquini, Davide Bacciu

PDP: Parameter-free Differentiable Pruning is All You Need, Minsik Cho, Saurabh Adya, Devang Naik

Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation Based Inference, Vincent Zaballa, Elliot E Hui

Towards Understanding Gradient Approximation in Equality Constrained Deep Declarative Networks, Stephen Gould, Ming Xu, Zhiwei Xu, Yanbin Liu

Differentiable Clustering and Partial Fenchel-Young Losses, Lawrence Stewart, Francis Bach, Felipe Llinares-López, Quentin Berthet

A Gradient Flow Modification to Improve Learning from Differentiable Quantum Simulators, Patrick Schnell, Nils Thuerey

Differentiable Tree Operations Promote Compositional Generalization, Paul Soulos, Edward J Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao

A Short Review of Automatic Differentiation Pitfalls in Scientific Computing, Jan Hueckelheim, Harshitha Menon, William S. Moses, Bruce Christianson, Paul Hovland, Laurent Hascoet

Sample-efficient learning of auditory object representations using differentiable impulse response synthesis, Vinayak Agarwal, James Traer, Josh Mcdermott

Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation, Mohamad Qadri, Michael Kaess

Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick, Lennert De Smet, Emanuele Sansone, Pedro Zuidberg Dos Martires

Lossless hardening with $\partial\mathbb{B}$ nets, Ian Wright

Differentiable sorting for censored time-to-event data, Andre Vauvelle, Benjamin Wild, Roland Eils, Spiros Denaxas

Koopman Constrained Policy Optimization: A Koopman operator theoretic method for differentiable optimal control in robotics, Matthew Retchin, Brandon Amos, Steven Brunton, Shuran Song

Fine-Tuning Language Models with Just Forward Passes, Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Jason D. Lee, Danqi Chen, Sanjeev Arora

Lagrangian Proximal Gradient Descent for Learning Convex Optimization Models, Anselm Paulus, Vít Musil, Georg Martius

Differentiating Metropolis-Hastings to Optimize Intractable Densities, Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash Mansinghka, Jonathan Ragan-Kelley, Christopher Vincent Rackauckas, Moritz Schauer

Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning, Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali Mahmutogullari, Maxime Mulamba, Allegra De Filippo, Tias Guns, Michele Lombardi

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Contact

Contact the organizers: mail@differentiable.xyz

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