Kolya Malkin's homepage

Portrait of a person (Nikolay Malkin in Singapore in 2025) with medium-length wavy brown hair, a floral shirt, purple earrings, and glasses smiling against a blurry green background. In Singapore for a Bayesian computation conference, 2025.

How to address / refer to me

Name

For official purposes I'm “Nikolay Malkin” (at least for now). You can call me simply by first name. If we are working together or on friendly terms, please call me “Kolya” – a standard short/familiar form for Russian-origin names.

Pronouns

they≥she>he≥0.
Variety is welcome.

For the curious

This is the order of frequency I hope to maintain in both professional and personal contexts. Please do make an effort to get it right.

If unsure or when introducing me for the first time, singular they is safest.

If this is confusing, just ask.

Contact

Communication is accepted in English, French, Russian, and occasionally other languages.

Address

Informatics Forum 2.27
10 Crichton Street
Edinburgh EH8 9AB
Scotland, UK

Email

UoE (preferred)
Mila (old)
backup

Other profiles

GitHub
X / Twitter
Bluesky
Google Scholar
Semantic Scholar

Math(s) Out Loud


Available from the American Mathematical Society or Amazon.

Bayesian machine learning • neurosymbolic AI • ML for science and scientific reasoning

Since July 2024: Chancellor's Fellow @ University of Edinburgh, School of Informatics
Since April 2025: Programme Fellow @ CIFAR, Learning in Machines and Brains

Expand for affiliation details Member of Institute for Adaptive and Neural Computation
Affiliate member of Institute for Language, Cognition and Computation
Affiliate member of Institute for Action, Perception and Behaviour
Generative AI Laboratory Fellow

Call for students

I am always looking for motivated students to fill fully-funded PhD positions in the School of Informatics, University of Edinburgh, ideally to begin in September 2026. Interested students should contact me to discuss research directions and the application processes for the various degree programmes that I can supervise. Ideally, you should write before November 2025 to be considered for all available opportunities.

Expand for details

Research topics are in the areas of Bayesian machine learning (including probabilistic reasoning in language, generative models, and neurosymbolic methods) and applications in the sciences. I am especially happy to work with students who have a strong background in mathematics and an interest in robust and interpretable AI. Please see my research interests and recent publications for examples of the kind of work we might do together.

Studentships include:

The typical length of a PhD in the UK is 3-3.5 years. All students have at least one secondary supervisor – please let me know if you have a specific person or lab in mind – and there are many opportunities for collaboration.

(I regret that I cannot engage in individual discussions with everyone who contacts me, despite my best intentions. If I do not respond, please remind me. However, I am unlikely to answer messages that are hallucinated by a language model, which account for an increasing number. No need for polished essays! I just want to know about you, your interests, and how you think we might work together.)

On diversity and inclusion

Diverse perspectives shape impactful research. If you have an unusual education history, come from an ethnic/cultural minority, or identify as a woman, non-binary person, or queer individual, and you are interested in working with me, I especially encourage you to apply and to mention this if you feel comfortable doing so.

I am committed to maintaining an inclusive environment for everyone I interact with and to considering ethics and fairness in both collaboration choices and research topics. I’ve participated in and contributed to the activities of Women in Machine Learning and Queer in AI, groups that take direct action to reduce barriers to participation and reshape the culture in our field.

Teaching

In October-November 2024 I taught a mini-course on diffusion probabilistic models. Slides will be posted here eventually.

In the spring of 2026 I will give a course on Advanced Topics in Machine Learning, newly designed with my colleagues Slava Borovitskiy and Rik Sarkar. My track will focus on deep generative modelling, complementing those on geometric learning and optimisation.

The prior distribution

Before moving to Edinburgh, I was a postdoc at Mila – Québec AI institute and Department of Informatics and Operations Research, Université de Montréal, where I was fortunate to work with Prof. Yoshua Bengio and also to collaborate with Profs. Aaron Courville, Gauthier Gidel, and Guillaume Lajoie, among others:

Expand for collaborators and students

I have also been lucky to work with many fellow postdoctoctoral researchers (including Kilian Fatras, Alex Hernández-García, Pablo Lemos, Alex Tong) and M.S./Ph.D. students and interns (including Tristan Deleu, Edward Hu, Moksh Jain, Minsu Kim, Salem Lahlou, Jarrid Rector-Brooks, Alexandra Volokhova, Dinghuai Zhang, among many others).

I was formally trained as a pure mathematician: at the University of Washington (Seattle) (B.S., 2015) and Yale University (M.S. and Ph.D., 2021). In addition, I believe many individuals and societies could benefit from a dose of friendly mathematical education. Some organizations I have been involved in: UW Math Circles (Seattle), Math-M-Addicts (New York City). I am also a coauthor of this collection of problems and puzzles.

Short bio in third person

(Feel free to use if needed for talks and similar.)

Nikolay Malkin is a Chancellor's Fellow in Informatics at the University of Edinburgh and a fellow of CIFAR's Learning in Machines and Brains programme. Their research focuses on algorithms for probabilistic inference and Bayesian machine learning, with applications in generative modelling, neurosymbolic AI, and machine reasoning. Within machine learning, Nikolay's work explores modelling of Bayesian posteriors over high-dimensional and structured variables, induction and discovery of compositional structure in generative models, and neurosymbolic methods for uncertainty-aware reasoning in language and formal systems. Their work has found applications in pure and applied sciences, including inverse imaging, remote sensing, discovery of novel biological and chemical structures, and, most recently, robot control. Nikolay holds a PhD in mathematics from Yale University (2021) and was previously a postdoctoral researcher at Mila – Québec AI Institute in Montréal (2021 to 2024).

Invited talks

I intend to eventually post slides for some of these.

2025

Diffusion modelling for amortised inference Isaac Newton Institute for Mathematical Sciences, Accelerating statistical inference and experimental design with machine learning workshop Cambridge, June 2025 Diffusion model tutorial Isaac Newton Institute for Mathematical Sciences, Accelerating statistical inference and experimental design with machine learning workshop Cambridge, June 2025 Stochastic control for black-box inference: Insights from deep reinforcement learning BayesComp 2025 Singapore, June 2025 Amortised inference meets LLMs: Algorithms and implications for faithful knowledge extraction Simons Institute for the Theory of Computing, Safety-Guaranteed LLMs workshop Berkeley, April 2025 The latest in generative AI research Edinburgh Futures Institute / Morgan Stanley Inclusive Ventures Lab Edinburgh, February 2025 Diffusion models without data: Towards plans and representations from stochastic dynamics ETH Zürich, AI Center Zürich, February 2025

2024

Plans and symbolic representations from stochastic dynamics CIFAR, Learning in Machines and Brains program meeting Toronto, November 2024 Diffusion models without data KTH Royal Institute of Technology, Digital Futures Stockholm, October 2024 Amortising intractable inference with diffusion models and off-policy RL Higher School of Economics, High-Dimensional Inference Lab Moscow / virtual, August 2024

2023

Developments in amortized posterior inference with foundation models CIFAR, Learning in Machines and Brains program meeting New Orleans, December 2023 Two variational perspectives on diffusion models Google DeepMind London / virtual, December 2023 Bayesian neurosymbolic AI for reasoning and scientific discovery University of Edinburgh, School of Informatics Edinburgh, November 2023 Generative flow networks for inference over structured objects Stony Brook University, Computer Science colloquium Stony Brook, March 2023 Probabilistic inference for reasoning with large language models Columbia University, NLP seminar New York, March 2023 Probabilistic inference for reasoning with large language models Microsoft Research Montréal / virtual, February 2023 Generative flow networks: Theory, applications, and connections Google Research, Bayesflow seminar New York / virtual, January 2023

2022

Coherence boosting: When your pretrained language model is not paying enough attention ACL 2022 Dublin, May 2022

2021

Studying word order through iterative shuffling EMNLP 2021 Punta Cana, November 2021 GPT Perdetry Test: Generating new meanings for new words NAACL 2021 virtual, June 2021 New approaches to computer vision for land cover mapping and change detection Microsoft Research Redmond / virtual, March 2021

2020

Motivic fundamental groups of CM elliptic curves and geometry of Bianchi hyperbolic threefolds Johns Hopkins University, Junior Number Theory Days Baltimore / virtual, December 2020 Land cover mapping with epitomes and clustering models ML for Remote Sensing seminar virtual, August 2020 Human-machine collaboration for fast land cover mapping ICLR 2020, Climate Change AI virtual, April 2020

Research

I work on algorithms for deep-learning-based reasoning and their applications. I am specifically interested in the following subjects:

  • Machine learning for generative models, in particular, induction of compositional structure in generative models and modeling of posteriors over high-dimensional explanatory variables (including with continuous-time (diffusion) generative models). Much of my recent work is on generative flow networks, which are a path towards inference machines that build structured, uncertainty-aware explanations for observed data.
  • Applications to natural language processing and reasoning in language: what large language models can do, what they cannot do, and how to overcome their limitations with improved inference procedures. I view human-like symbolic, formal, and mathematical reasoning via Bayesian neurosymbolic methods as a long-term aspiration for artificial intelligence.
  • Applications to computer vision: notably, below you can find my (older) work on AI for remote sensing (land cover mapping and change detection), which can be used for tracking land use patterns over time and monitoring the effects of climate change.

Current students

I am fortunate to be primary PhD supervisor to:

and secondary or co-supervisor to:

Publications and preprints

In submission / preparation

Robust reinforcement learning for discrete compositional generation via general soft operators Marco Jiralerspong, Esther Derman, Danilo Vucetic, Nikolay Malkin, Bilun Sun, Tianyu Zhang, Pierre-Luc Bacon, Gauthier Gidel preprint Adaptive destruction processes for diffusion samplers Timofei Gritsaev, Nikita Morozov, Kirill Tamogashev, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry Vetrov, Nikolay Malkin preprint On designing diffusion autoencoders for efficient generation and representation learning Magdalena Proszewska, Nikolay Malkin, N. Siddharth preprint Fast flow-based visuomotor policies via conditional optimal transport couplings Andreas Sochopoulos, Nikolay Malkin, Nikolaos Tsagkas, João Moura, Michael Gienger, Sethu Vijayakumar preprint Learning decision trees as amortized structure inference Mohammed Mahfoud, Ghait Boukachab, Michał Koziarski, Alex Hernández-García, Stefan Bauer, Yoshua Bengio, Nikolay Malkin preprint From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training Julius Berner*, Lorenz Richter*, Marcin Sendera*, Jarrid Rector-Brooks, Nikolay Malkin preprint Discrete, compositional, and symbolic representations through attractor dynamics Andrew J. Nam, Eric Elmoznino, Nikolay Malkin, James L. McClelland, Yoshua Bengio, Guillaume Lajoie preprint

2025

Accepted / published

Mixtures of in-context learners Giwon Hong, Emile Van Krieken, Nikolay Malkin, Edoardo Ponti, Pasquale Minervini ACL 2025 (to appear) Can a Bayesian oracle prevent harm from an agent? Yoshua Bengio*, Michael K. Cohen*, Nikolay Malkin*, Matt MacDermott, Damiano Fornasiere, Pietro Greiner, Younesse Kaddar UAI 2025 (to appear) Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models Siddarth Venkatraman*, Mohsin Hasan*, Minsu Kim, Luca Scimeca, Marcin Sendera, Yoshua Bengio, Glen Berseth, Nikolay Malkin ICML 2025 (to appear) Importance-weighted training of diffusion samplers Sanghyeok Choi, Sarthak Mittal, Victor Elvira, Jinkyoo Park, Nikolay Malkin ICML 2025 “Generative AI and Biology” workshop (to appear) Action abstractions for amortized sampling Oussama Boussif, Léna Néhale Ezzine, Joseph Viviano, Michał Koziarski, Moksh Jain, Nikolay Malkin, Emmanuel Bengio, Rim Assouel, Yoshua Bengio ICLR 2025 Adaptive teachers for amortized samplers Minsu Kim*, Sanghyeok Choi*, Taeyoung Yun, Emmanuel Bengio, Leo Feng, Jarrid Rector-Brooks, Sungsoo Ahn, Jinkyoo Park, Nikolay Malkin, Yoshua Bengio ICLR 2025 Learning diverse attacks on large language models for robust red-teaming and safety tuning Seanie Lee, Minsu Kim, Lynn Cherif, David Dobre, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Moksh Jain ICLR 2025 PQMass: Probabilistic assessment of the quality of generative models using probability mass estimation Pablo Lemos, Sammy Nasser Sharief, Nikolay Malkin, Laurence Perreault-Levasseur, Yashar Hezaveh ICLR 2025

Preprints / notes

In-context parametric inference: Point or distribution estimators? Sarthak Mittal, Yoshua Bengio, Nikolay Malkin, Guillaume Lajoie preprint

2024

Accepted / published

Amortizing intractable inference in diffusion models for vision, language, and control Siddarth Venkatraman*, Moksh Jain*, Luca Scimeca*, Minsu Kim*, Marcin Sendera*, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yoshua Bengio, Glen Berseth, Nikolay Malkin NeurIPS 2024 Improved off-policy training of diffusion samplers Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Nikolay Malkin NeurIPS 2024 Proof Flow: Preliminary study on generative flow network language model tuning for formal reasoning Matthew Ho, Vincent Zhu, Xiaoyin Chen, Moksh Jain, Nikolay Malkin, Edwin Zhang NeurIPS 2024 “System-2 Reasoning at Scale” workshop Amortizing intractable inference in diffusion models for Bayesian inverse problems [extension of conference paper] Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yashar Hezaveh, Laurence Perreault-Levasseur, Yoshua Bengio, Glen Berseth, Nikolay Malkin NeurIPS 2024 “Machine Learning and the Physical Sciences” workshop Path-filtering in path-integral simulations of open quantum systems using GFlowNets Jeremy Lackman-Mincoff, Moksh Jain, Nikolay Malkin, Yoshua Bengio, Lena Simine Journal of Chemical Physics 161(14), 2024 V-STaR: Training verifiers for self-taught reasoners Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville, Alessandro Sordoni, Rishabh Agarwal COLM 2024 Machine learning and information theory concepts towards an AI Mathematician Yoshua Bengio, Nikolay Malkin Bulletin of the American Mathematical Society, 2024 Iterated denoising energy matching for sampling from Boltzmann densities Tara Akhound-Sadegh*, Jarrid Rector-Brooks*, Joey Bose*, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong ICML 2024 Improving gradient-guided nested sampling for posterior inference Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault-Levasseur ICML 2024 Discrete probabilistic inference as control in multi-path environments Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio UAI 2024 Amortizing intractable inference in large language models Edward Hu*, Moksh Jain*, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin ICLR 2024; best paper honourable mention Delta-AI: Local objectives for amortized inference in sparse graphical models Jean-Pierre Falet*, Hae-Beom Lee*, Nikolay Malkin*, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio ICLR 2024 Expected flow networks in stochastic environments and two-player zero-sum games Marco Jiralerspong*, Bilun Sun*, Danilo Vucetic*, Tianyu Zhang, Yoshua Bengio, Gauthier Gidel, Nikolay Malkin ICLR 2024 PhyloGFN: Phylogenetic inference with GFlowNets Ming Yang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, Yoshua Bengio ICLR 2024 Simulation-free Schrödinger bridges via score and flow matching Alexander Tong*, Nikolay Malkin*, Kilian Fatras*, Lazar Atanackovic, Yanlei Zhang, Guillaume Huguet, Hananeh Aliee, Guy Wolf, Yoshua Bengio AISTATS 2024 Improving and generalizing flow-based generative models with minibatch optimal transport Alexander Tong*, Kilian Fatras*, Nikolay Malkin*, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, Yoshua Bengio TMLR, 2024

Preprints / notes

On generalization for generative flow networks Anas Krichel, Nikolay Malkin, Salem Lahlou, Yoshua Bengio preprint

2023

Accepted / published

Joint Bayesian inference of graphical structure and parameters with a single generative flow network Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, Yoshua Bengio NeurIPS 2023 Let the flows tell: Solving graph combinatorial problems with GFlowNets Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan NeurIPS 2023 Discrete, compositional, and symbolic representations through attractor dynamics [workshop version] Andrew J. Nam, Eric Elmoznino, Nikolay Malkin, Chen Sun, Yoshua Bengio, Guillaume Lajoie NeurIPS 2023 “Information-Theoretic Principles in Cognitive Systems” workshop Donor activity is associated with US legislators’ attention to political issues Pranav Goel, Nikolay Malkin*, SoRelle Gaynor*, Nebojsa Jojic, Kristina Miler, Philip Resnik PLOS One, 2023 GFlowNet-EM for learning compositional latent variable models Edward Hu*, Nikolay Malkin*, Moksh Jain, Katie Everett, Alexandros Graikos, Yoshua Bengio ICML 2023 A theory of continuous generative flow networks Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin ICML 2023 Learning GFlowNets from partial episodes for improved convergence and stability Kanika Madan, Jarrid Rector-Brooks*, Maksym Korablyov*, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin ICML 2023 Better training of GFlowNets with local credit and incomplete trajectories Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio ICML 2023 GFlowOut: Dropout with generative flow networks Dianbo Liu, Moksh Jain, Bonaventure Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio ICML 2023 Thompson sampling for improved exploration in GFlowNets Jarrid Rector-Brooks, Kanika Madan, Moksh Jain, Maksym Korablyov, Cheng-Hao Liu, Sarath Chandar, Nikolay Malkin, Yoshua Bengio ICML 2023 “Structured Probabilistic Inference and Generative Modeling” workshop BatchGFN: Generative flow networks for batch active learning Shreshth Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal ICML 2023 “Structured Probabilistic Inference and Generative Modeling” workshop Probabilistic reasoning over sets using large language models Batu Ozturkler, Nikolay Malkin, Zhen Wang, Nebojsa Jojic ACL 2023 GFlowNets and variational inference Nikolay Malkin*, Salem Lahlou*, Tristan Deleu*, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio ICLR 2023

Preprints / notes

Unifying generative models with GFlowNets and beyond Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio preprint

2022

Accepted / published

Diffusion models as plug-ang-play priors Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, Dimitris Samaras NeurIPS 2022 Trajectory balance: Improved credit assignment in GFlowNets Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, Yoshua Bengio NeurIPS 2022 Posterior samples of source galaxies in strong gravitational lenses with score-based priors Alexandre Adam, Adam Coogan, Nikolay Malkin, Ronan Legin, Laurence Perreault Levasseur, Yashar Hezaveh, Yoshua Bengio NeurIPS 2022 “Machine Learning for the Physical Sciences” workshop Resolving label uncertainty with implicit posterior models Esther Rolf*, Nikolay Malkin*, Alexandros Graikos, Ana Jojic, Caleb Robinson, Nebojsa Jojic UAI 2022 Generative flow networks for discrete probabilistic modeling Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio ICML 2022 Unifying generative models with GFlowNets Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio ICML 2022 “Beyond Bayes: Paths Towards Universal Reasoning Systems” workshop Coherence boosting: When your pretrained language model is not paying enough attention Nikolay Malkin, Zhen Wang, Nebojsa Jojic ACL 2022 The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal semantic change detection Zhuohong Li, Fangxiao Lu, Hongyan Zhang, Lilin Tu, Jiayi Li, Xin Huang, Caleb Robinson, Nikolay Malkin, Nebojsa Jojic, Pedram Ghamisi, Ronny Hänsch, Naoto Yokoya JSTARS vol.15

2021

Accepted / published

Studying word order through iterative shuffling Nikolay Malkin, Sameera Lanka, Pranav Goel, Nebojsa Jojic EMNLP 2021 GPT Perdetry Test: Generating new meanings for new words Nikolay Malkin, Sameera Lanka, Pranav Goel, Sudha Rao, Nebojsa Jojic NAACL 2021 From local algorithms to global results: Human-machine collaboration for robust analysis of geographically diverse imagery Nebojsa Jojic, Nikolay Malkin, Caleb Robinson, Anthony Ortiz IGARSS 2021 On the Galois action on motivic fundamental groups of punctured elliptic and rational curves Nikolay Malkin; thesis advisor A.B. Goncharov PhD thesis Global land cover mapping with weak supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest Caleb Robinson, Nikolay Malkin, Nebojsa Jojic, Huijun Chen, Rongjun Qin, Changlin Xiao, Michael Schmitt, Pedram Ghamisi, Ronny Hänsch, Naoto Yokoya JSTARS vol.14

Preprints / notes

High-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest Nikolay Malkin, Caleb Robinson, Nebojsa Jojic preprint

2020

Accepted / published

Weakly supervised semantic segmentation in the 2020 IEEE GRSS Data Fusion Contest Caleb Robinson, Nikolay Malkin, Lucas Hu, Bistra Dilkina, Nebojsa Jojic IGARSS 2020; contest winner Mining self-similarity: Label super-resolution with epitomic representations Nikolay Malkin, Anthony Ortiz, Nebojsa Jojic ECCV 2020 Human-machine collaboration for fast land cover mapping Caleb Robinson, Anthony Ortiz, Nikolay Malkin, Blake Elias, Andi Peng, Dan Morris, Bistra Dilkina, Nebojsa Jojic AAAI 2020

Preprints / notes

Learning intersecting representations of short random walks on graphs Nikolay Malkin, Nebojsa Jojic preprint Motivic fundamental groups of CM elliptic curves and geometry of Bianchi hyperbolic threefolds Nikolay Malkin preprint Shuffle relations for Hodge and motivic correlators Nikolay Malkin preprint

2019

Accepted / published

Large scale high-resolution land cover mapping with multi-resolution data Caleb Robinson, Le Hou, Nikolay Malkin, Rachel Soobitsky, Jacob Czawlytko, Bistra Dilkina, Nebojsa Jojic CVPR 2019 Label super-resolution networks Nikolay Malkin, Caleb Robinson, Le Hou, Rachel Soobitsky, Jacob Czawlytko, Dimitris Samaras, Joel Saltz, Lucas Joppa, Nebojsa Jojic ICLR 2019

Preprints / notes

Label super-resolution with inter-instance loss Maozheng Zhao, Le Hou, Han Le, Dimitris Samaras, Nebojsa Jojic, Danielle Fassler, Tahsin Kurc, Rajarsi Gupta, Nikolay Malkin, Shroyer Kenneth, Joel Saltz preprint