Ivan Ermakov — accepted AI-conference papers

2025

AutoJudge: Judge Decoding Without Manual Annotation

Roman Garipov, Fedor Velikonivtsev, Ivan Ermakov, Ruslan Svirschevski, Vage Egiazarian, Max Ryabinin

NeurIPS 2025poster

We introduce AutoJudge, a method that accelerates large language model (LLM) inference with task-specific lossy speculative decoding. Instead of matching the original model output distribution token-by-token, we identify the generated tokens that affect the downstream quality of the response, relax…

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2025

Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models

Alina Shutova, Vladimir Malinovskii, Vage Egiazarian, Denis Kuznedelev, Denis Mazur, Surkov Nikita +2

ICML 2025poster

Efficient real-world deployments of large language models (LLMs) rely on Key-Value (KV) caching for processing and generating long outputs, reducing the need for repetitive computation. For large contexts, Key-Value caches can take up tens of gigabytes of device memory, as they store vector represen…

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2023

Physically-Plausible Illumination Distribution Estimation

Egor Ershov, Vasily Tesalin, Ivan Ermakov, Michael S. Brown

ICCV 2023poster

A camera's auto-white-balance (AWB) module operates under the assumption that there is a single dominant illumination in a captured scene. AWB methods estimate an image's dominant illumination and use it as the target "white point" for correction. However, in natural scenes, there are often many lig…

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