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← Authors

Volodymyr Kuleshov

33 papers · 2013–2025 · 9 conferences · across top CS/AI conferences

Achievements

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+12 more ↓ 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (11) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (9)
🌍 Conference Polyglot (9) πŸƒ Academic Marathon (12) πŸ—ΊοΈ Taxonomy Completionist (11) 🧬 Topic Evolution πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (104) ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ’Ž Century Club (33) πŸ“ˆ Trend Setter

Conferences

ICML (10) NIPS (8) ICLR (5) UAI (3) AISTATS (2) EMNLP (2) AAAI (1) CVPR (1) IJCAI (1)

Top co-authors

Aaron Gokaslan (8) Subham Sekhar Sahoo (7) Yair Schiff (7) Stefano Ermon (5) Shachi Deshpande (5) Justin T Chiu (3) Alexander Rush (3) Marianne Arriola (2) Jiaming Song (2) Phillip Si (2)

Keywords

diffusion model (4) variational inference (3) uncertainty quantification (3) latent space (2) latent variable (2) structured prediction (2) generative model (2) uncertainty calibration (2) image generation (2) reinforcement learning (1) sequential decision-making (1) conformal prediction (1) sequence modeling (1) transfer learning (1) model-based planning (1) online learning (1) representation learning (1) density estimation (1) matrix factorization (1) semi-supervised learning (1)

Papers

Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models ICLR 2025 Simple Guidance Mechanisms for Discrete Diffusion Models ICLR 2025 Calibrated Regression Against An Adversary Without Regret UAI 2025 Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors AAAI 2025 The Diffusion Duality ICML 2025 Online Calibrated and Conformal Prediction Improves Bayesian Optimization AISTATS 2024 CommonCanvas: Open Diffusion Models Trained on Creative-Commons Images CVPR 2024 QuIP$#$: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks ICML 2024 Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs UAI 2024 Calibrated and Conformal Propensity Scores for Causal Effect Estimation UAI 2024 DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems ICML 2024 Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling ICML 2024 The GAN is dead; long live the GAN! A Modern GAN Baseline NIPS 2024 Diffusion Models With Learned Adaptive Noise NIPS 2024 Simple and Effective Masked Diffusion Language Models NIPS 2024 Semi-Parametric Inducing Point Networks and Neural Processes ICLR 2023 QuIP: 2-Bit Quantization of Large Language Models With Guarantees NIPS 2023 Text Embeddings Reveal (Almost) As Much As Text EMNLP 2023 Backpropagation through Combinatorial Algorithms: Identity with Projection Works ICLR 2023 Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows ICML 2023 InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models ICML 2023 Model Criticism for Long-Form Text Generation EMNLP 2022 Autoregressive Quantile Flows for Predictive Uncertainty Estimation ICLR 2022 Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation ICML 2022 Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies NIPS 2022 Calibrated Model-Based Deep Reinforcement Learning ICML 2019 Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. NIPS 2019 Adversarial Constraint Learning for Structured Prediction IJCAI 2018 Accurate Uncertainties for Deep Learning Using Calibrated Regression ICML 2018 Neural Variational Inference and Learning in Undirected Graphical Models NIPS 2017 Calibrated Structured Prediction NIPS 2015 Tensor Factorization via Matrix Factorization AISTATS 2015 Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration ICML 2013