Trees with an On-Line Degree Ramsey Number of Four. - David Rolnick (Electron. J. Comb., 2011)

The on-line degree Ramsey number of cycles. - David Rolnick (Discret. Math., 2013)

Gröbner Bases and Nullstellensätze for Graph-Coloring Ideals. - Jesús A. De Loera; Susan Margulies; Michael Pernpeintner; Eric Riedl; David Rolnick; Gwen Spencer; Despina Stasi; Jon Swenson (CoRR, 2014)

Graph-Coloring Ideals: Nullstellensatz Certificates, Gröbner Bases for Chordal Graphs, and Hardness of Gröbner Bases. - Jesús A. De Loera; Susan Margulies; Michael Pernpeintner; Eric Riedl; David Rolnick; Gwen Spencer; Despina Stasi; Jon Swenson (ISSAC, 2015)

Acyclic Subgraphs of Planar Digraphs. - Noah Golowich; David Rolnick (Electron. J. Comb., 2015)

On the robust hardness of Gröbner basis computation. - Gwen Spencer; David Rolnick (CoRR, 2015)

On the growth of Stanley sequences. - David Rolnick; Praveen S. Venkataramana (Discret. Math., 2015)

A Multi-Pass Approach to Large-Scale Connectomics. - Yaron Meirovitch; Alexander Matveev; Hayk Saribekyan; David M. Budden; David Rolnick; Gergely Ódor; Seymour Knowles-Barley; Thouis Raymond Jones; Hanspeter Pfister; Jeff William Lichtman; Nir Shavit (CoRR, 2016)

GeoCUTS: Geographic Clustering Using Travel Statistics. - David Rolnick; Kevin Aydin; Shahab Kamali; Vahab S. Mirrokni; Amir Najmi (CoRR, 2016)

Algorithmic aspects of Tverberg's Theorem. - David Rolnick; Pablo Soberón (CoRR, 2016)

Novel structures in Stanley sequences. - Richard A. Moy; David Rolnick (Discret. Math., 2016)

Markov Transitions between Attractor States in a Recurrent Neural Network. - Jeremy Bernstein; Ishita Dasgupta 001; David Rolnick; Haim Sompolinsky (AAAI Spring Symposia, 2017)

Morphological Error Detection in 3D Segmentations. - David Rolnick; Yaron Meirovitch; Toufiq Parag; Hanspeter Pfister; Viren Jain; Jeff W. Lichtman; Edward S. Boyden; Nir Shavit (CoRR, 2017)

Deep Learning is Robust to Massive Label Noise. - David Rolnick; Andreas Veit; Serge J. Belongie; Nir Shavit (CoRR, 2017)

Quantitative Combinatorial Geometry for Continuous Parameters. - Jesús A. De Loera; Reuben N. La Haye; David Rolnick; Pablo Soberón (Discret. Comput. Geom., 2017)

Quantitative Tverberg Theorems Over Lattices and Other Discrete Sets. - Jesús A. De Loera; Reuben N. La Haye; David Rolnick; Pablo Soberón (Discret. Comput. Geom., 2017)

Quantitative (p, q) theorems in combinatorial geometry. - David Rolnick; Pablo Soberón (Discret. Math., 2017)

On the classification of Stanley sequences. - David Rolnick (Eur. J. Comb., 2017)

The power of deeper networks for expressing natural functions. - David Rolnick; Max Tegmark (ICLR (Poster), 2018)

How to Start Training: The Effect of Initialization and Architecture. - Boris Hanin; David Rolnick (NeurIPS, 2018)

Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. - Yaron Meirovitch; Lu Mi; Hayk Saribekyan; Alexander Matveev; David Rolnick; Casimir Wierzynski; Nir Shavit (CoRR, 2018)

Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. - Yaron Meirovitch; Lu Mi; Hayk Saribekyan; Alexander Matveev; David Rolnick; Nir Shavit (CVPR, 2019)

Measuring and regularizing networks in function space. - Ari S. Benjamin; David Rolnick; Konrad P. Körding (ICLR (Poster), 2019)

Complexity of Linear Regions in Deep Networks. - Boris Hanin; David Rolnick (ICML, 2019)

Randomized Experimental Design via Geographic Clustering. - David Rolnick; Kevin Aydin; Jean Pouget-Abadie; Shahab Kamali; Vahab S. Mirrokni; Amir Najmi (KDD, 2019)

Deep ReLU Networks Have Surprisingly Few Activation Patterns. - Boris Hanin; David Rolnick (NeurIPS, 2019)

Experience Replay for Continual Learning. - David Rolnick; Arun Ahuja; Jonathan Schwarz; Timothy P. Lillicrap; Gregory Wayne (NeurIPS, 2019)

Identifying Weights and Architectures of Unknown ReLU Networks. - David Rolnick; Konrad P. Körding (CoRR, 2019)

Reverse-engineering deep ReLU networks. - David Rolnick; Konrad P. Kording (ICML, 2020)

DC3: A learning method for optimization with hard constraints. - Priya L. Donti; David Rolnick; J. Zico Kolter (ICLR, 2021)

ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models. - Salva Rühling Cachay; Venkatesh Ramesh; Jason N. S. Cole; Howard Barker; David Rolnick (NeurIPS Datasets and Benchmarks, 2021)

Techniques for Symbol Grounding with SATNet. - Sever Topan; David Rolnick; Xujie Si (NeurIPS, 2021)

Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification. - Charles A. Kantor; Marta Skreta; Brice Rauby; Léonard Boussioux; Emmanuel Jehanno; Alexandra Luccioni; David Rolnick; Hugues Talbot (CoRR, 2021)

Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks. - Christopher Hillar; Tenzin Chan; Rachel Taubman; David Rolnick (Entropy, 2021)

Deep ReLU Networks Preserve Expected Length. - Boris Hanin; Ryan S. Jeong; David Rolnick (ICLR, 2022)

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning. - Setareh Cohan; Nam Hee Kim; David Rolnick; Michiel van de Panne (NeurIPS, 2022)

TIML: Task-Informed Meta-Learning for Agriculture. - Gabriel Tseng; Hannah Kerner; David Rolnick (CoRR, 2022)

On Neural Architecture Inductive Biases for Relational Tasks. - Giancarlo Kerg; Sarthak Mittal; David Rolnick; Yoshua Bengio; Blake A. Richards; Guillaume Lajoie (CoRR, 2022)

Neural Networks as Paths through the Space of Representations. - Richard D. Lange; Jordan Matelsky; Xinyue Wang; Devin Kwok; David S. Rolnick; Konrad P. Kording (CoRR, 2022)

Generating physically-consistent high-resolution climate data with hard-constrained neural networks. - Paula Harder; Qidong Yang; Venkatesh Ramesh; Prasanna Sattigeri; Alex Hernández-García; Campbell D. Watson; Daniela Szwarcman; David S. Rolnick (CoRR, 2022)

Clustering units in neural networks: upstream vs downstream information. - Richard D. Lange; David Rolnick; Konrad P. Kording (Trans. Mach. Learn. Res., 2022)

Bugs in the Data: How ImageNet Misrepresents Biodiversity. - Alexandra Sasha Luccioni; David Rolnick (AAAI, 2023)

FAENet: Frame Averaging Equivariant GNN for Materials Modeling. - Alexandre Duval; Victor Schmidt; Alex Hernández-García; Santiago Miret; Fragkiskos D. Malliaros; Yoshua Bengio; David Rolnick (ICML, 2023)

Hidden Symmetries of ReLU Networks. - J. Elisenda Grigsby; Kathryn Lindsey; David Rolnick (ICML, 2023)

Maximal Initial Learning Rates in Deep ReLU Networks. - Gaurav Iyer; Boris Hanin; David Rolnick (ICML, 2023)

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. - Julia Kaltenborn; Charlotte E. E. Lange; Venkatesh Ramesh; Philippe Brouillard; Yaniv Gurwicz; Chandni Nagda; Jakob Runge; Peer Nowack; David Rolnick (NeurIPS, 2023)

Normalization Layers Are All That Sharpness-Aware Minimization Needs. - Maximilian Müller; Tiffany Vlaar; David Rolnick; Matthias Hein 001 (NeurIPS, 2023)

SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data. - Mélisande Teng; Amna Elmustafa; Benjamin Akera; Yoshua Bengio; Hager Radi Abdelwahed; Hugo Larochelle; David Rolnick (NeurIPS, 2023)

Deep Networks as Paths on the Manifold of Neural Representations. - Richard D. Lange; Devin Kwok; Jordan Kyle Matelsky; Xinyue Wang; David Rolnick; Konrad P. Kording (TAG-ML, 2023)

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries. - Gabriel Tseng; Ivan Zvonkov; Mirali Purohit; David Rolnick; Hannah Kerner (CoRR, 2023)

Bird Distribution Modelling using Remote Sensing and Citizen Science data. - Mélisande Teng; Amna Elmustafa; Benjamin Akera; Hugo Larochelle; David Rolnick (CoRR, 2023)

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling. - Qidong Yang; Alex Hernández-García; Paula Harder; Venkatesh Ramesh; Prasanna Sattigeri; Daniela Szwarcman; Campbell D. Watson; David Rolnick (CoRR, 2023)

Multi-variable Hard Physical Constraints for Climate Model Downscaling. - Jose González-Abad; Alex Hernández-García; Paula Harder; David Rolnick; José Manuel Gutiérrez Llorente (CoRR, 2023)

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions. - Alvaro Carbonero; Alexandre Duval; Victor Schmidt; Santiago Miret; Alex Hernández-García; Yoshua Bengio; David Rolnick (CoRR, 2023)

OpenForest: A data catalogue for machine learning in forest monitoring. - Arthur Ouaknine; Teja Kattenborn; Etienne Laliberté; David Rolnick (CoRR, 2023)

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data. - Mélisande Teng; Amna Elmustafa; Benjamin Akera; Yoshua Bengio; Hager Radi Abdelwahed; Hugo Larochelle; David Rolnick (CoRR, 2023)

Towards Causal Representations of Climate Model Data. - Julien Boussard; Chandni Nagda; Julia Kaltenborn; Charlotte Emilie Elektra Lange; Philippe Brouillard; Yaniv Gurwicz; Peer Nowack; David Rolnick (CoRR, 2023)

FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models. - Nikolaos-Ioannis Bountos; Arthur Ouaknine; David Rolnick (CoRR, 2023)

Tackling Climate Change with Machine Learning. - David Rolnick; Priya L. Donti; Lynn H. Kaack; Kelly Kochanski; Alexandre Lacoste; Kris Sankaran; Andrew Slavin Ross; Nikola Milojevic-Dupont; Natasha Jaques; Anna Waldman-Brown; Alexandra Sasha Luccioni; Tegan Maharaj; et al. (ACM Comput. Surv., 2023)

Hard-Constrained Deep Learning for Climate Downscaling. - Paula Harder; Alex Hernández-García; Venkatesh Ramesh; Qidong Yang; Prasanna Sattegeri; Daniela Szwarcman; Campbell D. Watson; David Rolnick (J. Mach. Learn. Res., 2023)

Insect Identification in the Wild: The AMI Dataset. - Aditya Jain; Fagner Cunha; Michael James Bunsen; Juan Sebastián Cañas; Léonard Pasi; Nathan Pinoy; Flemming Helsing; JoAnne Russo; Marc Botham; Michael Sabourin; Jonathan Fréchette; Alexandre Anctil; et al. (ECCV (37), 2024)

Stealing part of a production language model. - Nicholas Carlini; Daniel Paleka; Krishnamurthy Dj Dvijotham; Thomas Steinke 002; Jonathan Hayase; A. Feder Cooper; Katherine Lee; Matthew Jagielski; Milad Nasr; Arthur Conmy; Eric Wallace; David Rolnick; et al. (ICML, 2024)

Position: Application-Driven Innovation in Machine Learning. - David Rolnick; Alán Aspuru-Guzik; Sara Beery; Bistra Dilkina; Priya L. Donti; Marzyeh Ghassemi; Hannah Kerner; Claire Monteleoni; Esther Rolf; Milind Tambe; Adam White (ICML, 2024)

Simultaneous Linear Connectivity of Neural Networks Modulo Permutation. - Ekansh Sharma; Devin Kwok; Tom Denton; Daniel M. Roy 001; David Rolnick; Gintare Karolina Dziugaite (ECML/PKDD (7), 2024)

Dataset Difficulty and the Role of Inductive Bias. - Devin Kwok; Nikhil Anand; Jonathan Frankle; Gintare Karolina Dziugaite; David Rolnick (CoRR, 2024)

Application-Driven Innovation in Machine Learning. - David Rolnick; Alán Aspuru-Guzik; Sara Beery; Bistra Dilkina; Priya L. Donti; Marzyeh Ghassemi; Hannah Kerner; Claire Monteleoni; Esther Rolf; Milind Tambe; Adam White (CoRR, 2024)

Predicting Species Occurrence Patterns from Partial Observations. - Hager Radi Abdelwahed; Mélisande Teng; David Rolnick (CoRR, 2024)

Climate Variable Downscaling with Conditional Normalizing Flows. - Christina Winkler; Paula Harder; David Rolnick (CoRR, 2024)

A machine learning pipeline for automated insect monitoring. - Aditya Jain; Fagner Cunha; Michael James Bunsen; Léonard Pasi; Anna Viklund; Maxim Larrivée; David Rolnick (CoRR, 2024)

Improving Molecular Modeling with Geometric GNNs: an Empirical Study. - Ali Ramlaoui; Théo Saulus; Basile Terver; Victor Schmidt; David Rolnick; Fragkiskos D. Malliaros; Alexandre Duval (CoRR, 2024)

Evaluating the transferability potential of deep learning models for climate downscaling. - Ayush Prasad; Paula Harder; Qidong Yang; Prasanna Sattegeri; Daniela Szwarcman; Campbell D. Watson; David Rolnick (CoRR, 2024)

Tree semantic segmentation from aerial image time series. - Venkatesh Ramesh; Arthur Ouaknine; David Rolnick (CoRR, 2024)

Causal Representation Learning in Temporal Data via Single-Parent Decoding. - Philippe Brouillard; Sébastien Lachapelle; Julia Kaltenborn; Yaniv Gurwicz; Dhanya Sridhar; Alexandre Drouin; Peer Nowack; Jakob Runge; David Rolnick (CoRR, 2024)

Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery. - Pratinav Seth; Michelle P. Lin; Brefo Yaw Dwamena; Jade Boutot; Mary Kang; David Rolnick (CoRR, 2024)

PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design. - Alexandre Duval; Victor Schmidt; Santiago Miret; Yoshua Bengio; Alex Hernández-García; David Rolnick (J. Mach. Learn. Res., 2024)

Linear Weight Interpolation Leads to Transient Performance Gains. - Gaurav Iyer; Gintare Karolina Dziugaite; David Rolnick (Trans. Mach. Learn. Res., 2024)

FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring. - Nikolaos-Ioannis Bountos; Arthur Ouaknine; Ioannis Papoutsis; David Rolnick (AAAI, 2025)

Task-Informed Meta-Learning for Remote Sensing. - Gabriel Tseng; Hannah Kerner; David Rolnick (CVPR Workshops, 2025)

Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models. - Gabriel Tseng; Anthony Fuller; Marlena Reil; Henry Herzog; Patrick Beukema; Favyen Bastani; James R. Green; Evan Shelhamer; Hannah Kerner; David Rolnick (CoRR, 2025)

Open-Set Recognition of Novel Species in Biodiversity Monitoring. - Yuyan Chen; Nico Lang; B. Christian Schmidt; Aditya Jain; Yves Basset; Sara Beery; Maxim Larrivée; David Rolnick (CoRR, 2025)

Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery. - Mélisande Teng; Arthur Ouaknine; Etienne Laliberté; Yoshua Bengio; David Rolnick; Hugo Larochelle (CoRR, 2025)

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery. - Mélisande Teng; Arthur Ouaknine; Etienne Laliberté; Yoshua Bengio; David Rolnick; Hugo Larochelle (CoRR, 2025)

Causal Climate Emulation with Bayesian Filtering. - Sebastian Hickman; Ilija Trajkovic; Julia Kaltenborn; Francis Pelletier; Alex Archibald; Yaniv Gurwicz; Peer Nowack; David Rolnick; Julien Boussard (CoRR, 2025)

The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions. - Devin Kwok; Gül Sena Altintas; Colin Raffel; David Rolnick (CoRR, 2025)

RainShift: A Benchmark for Precipitation Downscaling Across Geographies. - Paula Harder; Luca Schmidt; Francis Pelletier; Nicole Ludwig 002; Matthew Chantry; Christian Lessig; Alex Hernández-García; David Rolnick (CoRR, 2025)

GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction. - Eya Cherif; Arthur Ouaknine; Luke A. Brown; Phuong D. Dao; Kyle R. Kovach; Bing Lu; Daniel Mederer; Hannes Feilhauer; Teja Kattenborn; David Rolnick (CoRR, 2025)

Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal. - Christina Butsko; Kristof Van Tricht; Gabriel Tseng; Giorgia Milli; David Rolnick; Ruben Cartuyvels; Inbal Becker-Reshef; Zoltan Szantoi; Hannah Kerner (CoRR, 2025)

CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations. - Hager Radi Abdelwahed; Mélisande Teng; Robin Zbinden; Laura Pollock; Hugo Larochelle; Devis Tuia; David Rolnick (CoRR, 2025)