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ORCIDVolodymyr Kuleshov
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2020 – today
- 2024
[j5]Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov:
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. Trans. Mach. Learn. Res. 2024 (2024)- [c31]Shachi Deshpande, Charles Marx, Volodymyr Kuleshov:
Online Calibrated and Conformal Prediction Improves Bayesian Optimization. AISTATS 2024: 1450-1458 - [c30]Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov:
Common Canvas: Open Diffusion Models Trained on Creative-Commons Images. CVPR 2024: 8250-8260 - [c29]Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov:
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling. ICML 2024 - [c28]Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez:
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. ICML 2024 - [c27]Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa:
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. ICML 2024 - [i37]Top Piriyakulkij, Yingheng Wang, Volodymyr Kuleshov:
Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors. CoRR abs/2401.02739 (2024) - [i36]Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa:
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. CoRR abs/2402.04396 (2024) - [i35]Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez:
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. CoRR abs/2402.04467 (2024) - [i34]Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov:
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling. CoRR abs/2403.03234 (2024) - [i33]Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T. Chiu, Alexander M. Rush, Volodymyr Kuleshov:
Simple and Effective Masked Diffusion Language Models. CoRR abs/2406.07524 (2024) - [i32]Charles Marx, Volodymyr Kuleshov, Stefano Ermon:
Calibrated Probabilistic Forecasts for Arbitrary Sequences. CoRR abs/2409.19157 (2024) - 2023
- [c26]Qian Yang
, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, Fei Wang:
Harnessing Biomedical Literature to Calibrate Clinicians' Trust in AI Decision Support Systems. CHI 2023: 14:1-14:14 - [c25]John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush:
Text Embeddings Reveal (Almost) As Much As Text. EMNLP 2023: 12448-12460 - [c24]Richa Rastogi, Yair Schiff, Alon Hacohen, Zhaozhi Li, Ian Lee, Yuntian Deng, Mert R. Sabuncu, Volodymyr Kuleshov:
Semi-Parametric Inducing Point Networks and Neural Processes. ICLR 2023 - [c23]Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, Georg Martius:
Backpropagation through Combinatorial Algorithms: Identity with Projection Works. ICLR 2023 - [c22]Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov:
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows. ICML 2023: 31732-31753 - [c21]Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov:
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. ICML 2023: 36336-36354 - [c20]Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa:
QuIP: 2-Bit Quantization of Large Language Models With Guarantees. NeurIPS 2023 - [i31]Volodymyr Kuleshov, Shachi Deshpande:
Online Calibrated Regression for Adversarially Robust Forecasting. CoRR abs/2302.12196 (2023) - [i30]Shachi Deshpande, Volodymyr Kuleshov:
Calibrated Propensity Scores for Causal Effect Estimation. CoRR abs/2306.00382 (2023) - [i29]Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov:
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. CoRR abs/2306.08757 (2023) - [i28]Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa:
QuIP: 2-Bit Quantization of Large Language Models With Guarantees. CoRR abs/2307.13304 (2023) - [i27]Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov:
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. CoRR abs/2309.16119 (2023) - [i26]John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush:
Text Embeddings Reveal (Almost) As Much As Text. CoRR abs/2310.06816 (2023) - [i25]Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov:
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images. CoRR abs/2310.16825 (2023) - [i24]Jacqueline R. M. A. Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang:
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs. CoRR abs/2310.17816 (2023) - [i23]Top Piriyakulkij, Volodymyr Kuleshov, Kevin Ellis:
Active Preference Inference using Language Models and Probabilistic Reasoning. CoRR abs/2312.12009 (2023) - [i22]Subham Sekhar Sahoo, Aaron Gokaslan, Chris De Sa, Volodymyr Kuleshov:
Diffusion Models With Learned Adaptive Noise. CoRR abs/2312.13236 (2023) - 2022
- [c19]Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush:
Model Criticism for Long-Form Text Generation. EMNLP 2022: 11887-11912 - [c18]Phillip Si, Allan Bishop, Volodymyr Kuleshov:
Autoregressive Quantile Flows for Predictive Uncertainty Estimation. ICLR 2022 - [c17]Volodymyr Kuleshov, Shachi Deshpande:
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation. ICML 2022: 11683-11693 - [c16]Shachi Deshpande, Kaiwen Wang, Dhruv Sreenivas, Zheng Li, Volodymyr Kuleshov:
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies. NeurIPS 2022 - [i21]Shachi Deshpande, Zheng Li, Volodymyr Kuleshov:
Multi-Modal Causal Inference with Deep Structural Equation Models. CoRR abs/2203.09672 (2022) - [i20]Richa Rastogi, Yuntian Deng, Ian Lee, Mert R. Sabuncu, Volodymyr Kuleshov:
Semi-Parametric Deep Neural Networks in Linear Time and Memory. CoRR abs/2205.11718 (2022) - [i19]Subham Sekhar Sahoo, Marin Vlastelica, Anselm Paulus, Vít Musil, Volodymyr Kuleshov, Georg Martius:
Gradient Backpropagation Through Combinatorial Algorithms: Identity with Projection Works. CoRR abs/2205.15213 (2022) - [i18]Phillip Si, Volodymyr Kuleshov:
Energy Flows: Towards Determinant-Free Training of Normalizing Flows. CoRR abs/2206.06672 (2022) - [i17]Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush:
Model Criticism for Long-Form Text Generation. CoRR abs/2210.08444 (2022) - [i16]Jacqueline R. M. A. Maasch, Hao Zhang, Qian Yang, Fei Wang, Volodymyr Kuleshov:
Regularized Data Programming with Bayesian Priors. CoRR abs/2210.08677 (2022) - 2021
- [i15]Bojian Hou, Hao Zhang, Gur Ladizhinsky, Stephen Yang, Volodymyr Kuleshov, Fei Wang, Qian Yang:
Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure. CoRR abs/2111.00621 (2021) - [i14]Shachi Deshpande, Volodymyr Kuleshov:
Calibration Improves Bayesian Optimization. CoRR abs/2112.04620 (2021) - [i13]Phillip Si, Allan Bishop, Volodymyr Kuleshov:
Autoregressive Quantile Flows for Predictive Uncertainty Estimation. CoRR abs/2112.04643 (2021) - [i12]Volodymyr Kuleshov, Evgenii Nikishin, Shantanu Thakoor, Tingfung Lau, Stefano Ermon:
Quantifying and Understanding Adversarial Examples in Discrete Input Spaces. CoRR abs/2112.06276 (2021) - [i11]Volodymyr Kuleshov, Shachi Deshpande:
Calibrated and Sharp Uncertainties in Deep Learning via Simple Density Estimation. CoRR abs/2112.07184 (2021)
2010 – 2019
- 2019
- [c15]Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon:
Calibrated Model-Based Deep Reinforcement Learning. ICML 2019: 4314-4323 - [c14]Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon:
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. NeurIPS 2019: 10287-10298 - [i10]Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon:
Calibrated Model-Based Deep Reinforcement Learning. CoRR abs/1906.08312 (2019) - [i9]Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon:
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. CoRR abs/1909.06628 (2019) - 2018
- [j4]Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon:
Learning with Weak Supervision from Physics and Data-Driven Constraints. AI Mag. 39(1): 27-38 (2018) - [j3]Victoria Popic, Volodymyr Kuleshov, Michael P. Snyder, Serafim Batzoglou:
Fast Metagenomic Binning via Hashing and Bayesian Clustering. J. Comput. Biol. 25(7): 677-688 (2018) - [c13]Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon:
Accurate Uncertainties for Deep Learning Using Calibrated Regression. ICML 2018: 2801-2809 - [c12]Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon:
Adversarial Constraint Learning for Structured Prediction. IJCAI 2018: 2637-2643 - [i8]Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon:
Adversarial Constraint Learning for Structured Prediction. CoRR abs/1805.10561 (2018) - [i7]Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon:
Accurate Uncertainties for Deep Learning Using Calibrated Regression. CoRR abs/1807.00263 (2018) - 2017
- [b1]Volodymyr Kuleshov:
Intelligent systems for personalized genomic medicine. Stanford University, USA, 2017 - [c11]Volodymyr Kuleshov, Stefano Ermon:
Estimating Uncertainty Online Against an Adversary. AAAI 2017: 2110-2116 - [c10]Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon:
Audio Super-Resolution using Neural Networks. ICLR (Workshop) 2017 - [c9]Volodymyr Kuleshov, Stefano Ermon:
Neural Variational Inference and Learning in Undirected Graphical Models. NIPS 2017: 6734-6743 - [c8]Victoria Popic, Volodymyr Kuleshov, Michael P. Snyder, Serafim Batzoglou:
GATTACA: Lightweight Metagenomic Binning Using Kmer Counting. RECOMB 2017: 391-392 - [c7]Volodymyr Kuleshov, Stefano Ermon:
Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning. UAI 2017 - [i6]Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon:
Audio Super Resolution using Neural Networks. CoRR abs/1708.00853 (2017) - [i5]Volodymyr Kuleshov, Stefano Ermon:
Neural Variational Inference and Learning in Undirected Graphical Models. CoRR abs/1711.02679 (2017) - 2016
- [j2]Volodymyr Kuleshov, Michael P. Snyder, Serafim Batzoglou:
Genome assembly from synthetic long read clouds. Bioinform. 32(12): 216-224 (2016) - [i4]Volodymyr Kuleshov, Stefano Ermon:
Reliable Confidence Estimation via Online Learning. CoRR abs/1607.03594 (2016) - 2015
- [c6]Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang:
Tensor Factorization via Matrix Factorization. AISTATS 2015 - [c5]Volodymyr Kuleshov, Percy Liang:
Calibrated Structured Prediction. NIPS 2015: 3474-3482 - [c4]Volodymyr Kuleshov, Okke Schrijvers:
Inverse Game Theory: Learning Utilities in Succinct Games. WINE 2015: 413-427 - [i3]Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang:
Simultaneous diagonalization: the asymmetric, low-rank, and noisy settings. CoRR abs/1501.06318 (2015) - [i2]Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang:
Tensor Factorization via Matrix Factorization. CoRR abs/1501.07320 (2015) - 2014
- [j1]Volodymyr Kuleshov:
Probabilistic single-individual haplotyping. Bioinform. 30(17): 379-385 (2014) - [i1]Volodymyr Kuleshov, Doina Precup:
Algorithms for multi-armed bandit problems. CoRR abs/1402.6028 (2014) - 2013
- [c3]Volodymyr Kuleshov:
Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration. ICML (3) 2013: 1418-1425 - 2012
- [c2]Volodymyr Kuleshov, Gordon T. Wilfong:
On the Efficiency of the Simplest Pricing Mechanisms in Two-Sided Markets. WINE 2012: 284-297 - 2010
- [c1]Volodymyr Kuleshov, Adrian Vetta:
On the Efficiency of Markets with Two-Sided Proportional Allocation Mechanisms. SAGT 2010: 246-261
Coauthor Index
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