Kyurae Kim's Awesome Papers

The papers that made me stay awake all night long. Let me know if you have anything interesting to share!

Interests

• High-performance computing
• Probabilistic machine learning
• Bayesian Statistics
• Bayesian inference
• Bayesian optimization
• Heterogeneous, specialized hardware
• Image processing
• Signal Processing

Awesome Papers

• Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent Dirichlet Allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.

• Neal, Radford M. "Bayesian Learning for Neural Networks." Vol. 118. Springer Science & Business Media, 2012.

• Chaney, Allison, et al. "Detecting and Characterizing Events." Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2016.

• Regier, Jeffrey, et al. "Approximate inference for constructing astronomical catalogs from images." The Annals of Applied Statistics 13.3 (2019): 1884-1926.

  • Juliacon 2017 Talk

• Shanbhag, Naresh R., et al. "Shannon-inspired Statistical Computing for The Nanoscale Era." Proceedings of the IEEE 107.1 (2019): 90-107.

  • Stanford Seminar

• Ungar, David, and Sam S. Adams. "Harnessing Emergence for Manycore Programming: Early Experience Integrating Ensembles, Adverbs, and Object-based Inheritance." Proceedings of the ACM International Conference Companion on Object Oriented Programming Systems languages and Applications Companion (OOPSLA). ACM, 2010.

  • STI Conference Presentation

• Thompson, Neil, and Svenja Spanuth. "The Decline of Computers As a General Purpose Technology: Why Deep Learning and the End of Moore’s Law are Fragmenting Computing." Available at SSRN 3287769 (2018).

• Hammernik, Kerstin, et al. "Learning a Variational Network for Reconstruction of Accelerated MRI Data." Magnetic resonance in medicine 79.6 (2018): 3055-3071.

  • Previous works
    • Chen, Yunjin, Wei Yu, and Thomas Pock. "On learning optimized reaction-diffusion processes for effective image restoration." Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2015.

• Fuchs, Adi, and David Wentzlaff. "The Accelerator Wall: Limits of Chip Specialization." Proceedings of the IEEE International Symposium on High-Performance Computer Architecture (HPCA'19).

• Ulyanov, Dmitry, Andrea Vedaldi, and Victor Lempitsky. "Deep Image Prior." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'18).

  • Follow-up works
    • Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon. "A Bayesian Perspective on the Deep Image Prior". Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'19).

• de Fine Licht, Johannes, et al. "Transformations of high-level synthesis codes for high-performance computing." IEEE Transactions on Parallel and Distributed Systems 32.5 (2020): 1014-1029.

• Kendall, Alex, and Yarin Gal. "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?." Advances in neural information processing systems (NIPS). 2017.

• Boyd, Stephen, et al. "Distributed optimization and statistical learning via the alternating direction method of multipliers." Foundations and Trends® in Machine learning 3.1 (2011): 1-122.

  • Microsoft Research Talk

• Pearce, Tim, et al. "Uncertainty in Neural Networks: Bayesian Ensembling." In Proceedings of Artificial Intelligence and Statistics (AISTATS'20). 2020.

• Qiang Liu and Dilin Wang. 2016. "Stein Variational Gradient Descent: a General Purpose Bayesian Inference Algorithm." Advances in Neural Information Processing Systems (NIPS'16), 2016.

  • Interactive demo. Select SVGD for the algorithm.
  • Follow-up works
    • Han, Jun, and Qiang Liu. "Stein Variational Gradient Descent Without Gradient." Proceedings of the International Conference on Machine Learning (ICML'18), in PMLR 80:1900-1908
    • Han, Jun, and Qiang Liu. "Stein Variational Adaptive Importance Sampling." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI'17).

• Wilson Ye Chen, Alessandro Barp, Francois-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey and Chris Oates. (2019). "Stein Point Markov Chain Monte Carlo", Proceedings of the International Conference on Machine Learning (ICML'19), in PMLR 97:1011-1021

• Jordan, Michael I. "Dynamical, Symplectic and Stochastic Perspectives on Gradient-Based Optimization." University of California, Berkeley (2018).

  • ICM 2018 Talk

• Kruskal, Clyde P., and Alan Weiss. "Allocating Independent Subtasks on Parallel Processors." IEEE Transactions on Software engineering 10, 1001-1016, 1985.

  • Follow-up works
    • Bast, Hannah. Ph.D. Thesis, 2000

• Solnik, Benjamin, et al. "Bayesian Optimization for a Better Dessert." (2017).

• Dai, Z., Yu, H., Low, B.K.H. & Jaillet, P.. (2019). "Bayesian Optimization Meets Bayesian Optimal Stopping". Proceedings of the 36th International Conference on Machine Learning (ICML), in PMLR 97:1496-1506

• Hartwig Anzt, Terry Cojean, Chen Yen-Chen, Jack Dongarra, Goran Flegar, Pratik Nayak, Stanimire Tomov, Yuhsiang M. Tsai, and Weichung Wang. "Load-balancing Sparse Matrix Vector Product Kernels on GPUs". ACM Transactions on Parallel Computing. 7, 1, Article 2 (March 2020).

• Kathleen E. Hamilton, Catherine D. Schuman, Steven R. Young, Ryan S. Bennink, Neena Imam, and Travis S. Humble. "Accelerating Scientific Computing in the Post-Moore’s Era". ACM Transactions on Parallel Computing. 7, 1, Article 6 (March 2020).

• Mikkola, Petrus, et al. "Projective Preferential Bayesian Optimization". Proceedings of the International Conference on Machine Learning (ICML'20), 2020.

• Slaughter, Elliott, et al. "Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance." Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'20), 2020.

• Geoffrey Roeder, Yuhuai Wu, David K. Duvenaud. "Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference." Advances in Neural Information Processing Systems 30 (NeurIPS'17), 2017.

• Vaden Masrani, Tuan Anh Le, Frank Wood. "The Thermodynamic Variational Objective." Advances in Neural Information Processing Systems 32 (NeurIPS'19), 2019.

  • Follow-up works
    • Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan. "All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference." Proceedings of the International Conference on Machine Learning (ICML'20), PMLR 119:1111-1122, 2020.
    • Vu Nguyen, et al. "Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective." Advances in Neural Information Processing Systems 33 (NeurIPS'20).

• Tijana Radivojević, Elena Akhmatskaya. "Modified Hamiltonian Monte Carlo for Bayesian inference." Statistics and Computing 30, 377–404, 2020.

• Gilboa, Guy, Nir Sochen, and Yehoshua Y. Zeevi. "Image enhancement and denoising by complex diffusion processes." IEEE Transactions on Pattern Analysis and Machine Intelligence 26.8 (2004): 1020-1036.

• Tzu-Mao Li, Jaakko Lehtinen, Ravi Ramamoorthi, Wenzel Jakob, Frédo Durand. "Anisotropic Gaussian Mutations for Metropolis Light Transport through Hessian-Hamiltonian Dynamics." ACM Transactions on Graphics 34(6) (Proceedings of ACM SIGGRAPH Asia 2015).

• Eric Brochu, Tyson Brochu, Nando de Freitas. "A Bayesian interactive optimization approach to procedural animation design." Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA'10), 103-112.

• Fearnhead, Paul, and Benjamin M. Taylor. "An adaptive sequential Monte Carlo sampler." Bayesian Analysis 8.2 (2013): 411-438.

• Akash Kumar Dhaka, et al. "Robust, Accurate Stochastic Optimization for Variational Inference." Advances in Neural Information Processing Systems (NeurIPS'20).

• Yu, Yongjian, and Scott T. Acton. "Speckle reducing anisotropic diffusion." IEEE Transactions on image processing 11.11 (2002): 1260-1270.

  • Follow-up works
    • Aja-Fernández, Santiago, and Carlos Alberola-López. "On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering." IEEE Transactions on Image Processing 15.9 (2006): 2694-2701.
    • Krissian, Karl, et al. "Oriented speckle reducing anisotropic diffusion." IEEE Transactions on Image Processing 16.5 (2007): 1412-1424.

• Shan, Tie-Jun, and Kailath, T., "Adaptive beamforming for coherent signals and interference." IEEE Transactions on Acoustics, Speech, and Signal Processing 33(3), Jun 1985.

• Yuko Ishiwaka, Xiao S. Zeng, Michael Lee Eastman, Sho Kakazu, Sarah Gross, Ryosuke Mizutani, and Masaki Nakada, "Foids: bio-inspired fish simulation for generating synthetic datasets." ACM Transactions on Graphics 40, 6, Article 207, Dec 2021.

• Surjanovic, Nikola, et al. "Parallel Tempering With a Variational Reference." Advances in Neural Information Processing Systems 35 (2022): 565-577.

• Andrieu, Christophe, and Arnaud Doucet. "Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC." IEEE Transactions on Signal Processing 47.10 (1999): 2667-2676.

• Mishchenko, Konstantin, Ahmed Khaled, and Peter Richtárik. "Random reshuffling: Simple analysis with vast improvements." Advances in Neural Information Processing Systems 33 (2020): 17309-17320.

• Jacob, Pierre E., John O’Leary, and Yves F. Atchadé. "Unbiased Markov chain Monte Carlo methods with couplings." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82.3 (2020).

• De Bortoli, Valentin, et al. "Diffusion Schrödinger bridge with applications to score-based generative modeling." Advances in Neural Information Processing Systems 34 (2021): 17695-17709.

• Giordano, Ryan, Tamara Broderick, and Michael I. Jordan. "Covariances, robustness and variational bayes." Journal of machine learning research 19.51 (2018).

• Doucet, Arnaud, Will Grathwohl, Alexander G. Matthews, and Heiko Strathmann. "Score-based diffusion meets annealed importance sampling." Advances in Neural Information Processing Systems 35 (2022): 21482-21494.

• Heng, Jeremy, Adrian N. Bishop, George Deligiannidis, and Arnaud Doucet. "Controlled sequential Monte Carlo." Annals of Statistics 48, no. 5 (2020).

• Kobak, Dmitry, Jonathan Lomond, and Benoit Sanchez. "The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization." The Journal of Machine Learning Research 21.1 (2020): 6863-6878.

• Bernton, Espen, et al. "Schrodinger Bridge Samplers." arXiv preprint arXiv:1912.13170 (2019).

• Bardenet, Rémi, Arnaud Doucet, and Chris Holmes. "On Markov chain Monte Carlo methods for tall data." Journal of Machine Learning Research 18.47 (2017).

• Karagiannis, Georgios, and Christophe Andrieu. "Annealed importance sampling reversible jump MCMC algorithms." Journal of Computational and Graphical Statistics 22.3 (2013): 623-648.

• Aubry, Mathieu, Sylvain Paris, Samuel W. Hasinoff, Jan Kautz, and Frédo Durand. "Fast local laplacian filters: Theory and applications." ACM Transactions on Graphics (TOG) 33.5 (2014): 1-14.

• A. Dieuleveut, G. Fort, E. Moulines and H. -T. Wai, "Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning," in IEEE Transactions on Signal Processing, vol. 71, pp. 3117-3148, 2023.

• Kunstner, Frederik, Raunak Kumar, and Mark Schmidt. "Homeomorphic-invariance of em: Non-asymptotic convergence in kl divergence for exponential families via mirror descent." International Conference on Artificial Intelligence and Statistics. PMLR, 2021.

• Altschuler, Jason M., and Pablo A. Parrilo. "Acceleration by stepsize hedging II: Silver stepsize schedule for smooth convex optimization." arXiv preprint arXiv:2309.16530 (2023).

• Biron-Lattes, Miguel, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, and Alexandre Bouchard-Côté. "autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm." International Conference on Artificial Intelligence and Statistics. PMLR, 2024.

• Taylor, Adrien, and Francis Bach. "Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions." Conference on Learning Theory. PMLR, 2019.

• Durmus, Alain, Szymon Majewski, and Błażej Miasojedow. "Analysis of Langevin Monte Carlo via convex optimization." Journal of Machine Learning Research 20.73 (2019): 1-46

• Lacoste–Julien, Simon, Ferenc Huszár, and Zoubin Ghahramani. "Approximate inference for the loss-calibrated Bayesian." Proceedings of the International Conference on Artificial Intelligence and Statistics. PMLR, 2011.

• Akyildiz, Ö. Deniz, Francesca Romana Crucinio, Mark Girolami, Tim Johnston, and Sotirios Sabanis. "Interacting particle langevin algorithm for maximum marginal likelihood estimation." arXiv preprint arXiv:2303.13429 (2023).