Takashi Ishida

Grants and Donations

• ASPIRE 2025 Japan-UK joint call for collaborations in Advancing Human-Centered AI, JST, 2025--2031 [press release]

• Google Research Grant 2025

• Grant-in-Aid for Scientific Research (B), JSPS, 2022--2025 (Co-Investigator)

• Grant-in-Aid for Early-Career Scientists, JSPS, 2022--2026

• Frontier of mathematics and information science, ACT-X, JST, 2020--2023 Selected as one of the achievements (JP/EN) at JST in FY2024!

• Grant-in-Aid for JSPS Fellows, JSPS, 2020--2021

Awards & Achievements

• Asian Trustworthy Machine Learning (ATML) Fellowship, 2025

• Expert Reviewer, Transactions on Machine Learning Research (received in 2024)

• Achievements, Information and Communications Technology (JP/EN/PDF) at JST, 2024

• Funai Information Technology Award for Young Researchers, 2022 (received in 2023)

• IEICE TC-IBISML Research Award Finalist, 2020 (received in 2021)

• Dean's Award for Outstanding Achievement, Graduate School of Frontier Sciences 2021

• Toyota/Dwango AI Scholarship, 2020 – 2021

• Award Finalist, IBIS2020

• Top 10% reviewer, NeurIPS 2020

• JSPS Research Fellowship for Young Scientists (DC2), 2020 – 2021

• Top 50% reviewer, NeurIPS 2019

• Google PhD Fellowship 2019

• IEICE TC-IBISML Research Award, 2017 (received in 2018)

Service

• Inaugural Organizing Committee Member: Google Developer Group AI for Science Japan (2025 – present)

• Committee member: FY2022 -- FY2025 IEICE, Information-Based Induction Sciences and Machine Learning (IBISML) Technical Group

• Workshop organizer: Organizer, Statistical Safeguarding Workshop 2026. PC member, IBIS2023. Executive Group, TrustML Young Scientist Seminars. Organizer, NeurIPS Meetup Japan 2021.

• Area chair: [2027] ICLR [2026] ICLR, ICML, NeurIPS [2025] ICLR, ICML, ACML

• Conference PC/reviewer: [2025] NeurIPS [2024] ICLR, AISTATS, ICML, ACML, NeurIPS [2023] ICLR [2022] ICLR, AISTATS, ICML, NeurIPS, [2021] NeurIPS, ACML, ICLR, UAI, ICML, [2020] NeurIPS (top 10% reviewer), ICML, ICLR, AAAI, AISTATS, UAI, ACML, [2019] NeurIPS (top 50% reviewer), ICML, AAAI, AISTATS, UAI, ACML

• Journal action editor: Transactions on Machine Learning Research (2024 -- 2026)

• Journal reviewer: IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, Journal of Information Processing, Machine Learning, Artificial Intelligence (AIJ), Transactions on Machine Learning Research (TMLR; Expert Reviewer in 2024)

• Workshop reviewer: 3rd edition of Reproducibility Challenge @ NeurIPS 2019, IJCAI 2021 Workshop on Weakly Supervised Representation Learning, ICML 2025 Workshop on The Impact of Memorization on Trustworthy Foundation Models

Courses at UTokyo

• Advanced Data Analysis (Graduate, S1S2, English) with Prof. Masashi Sugiyama: 2021, 2023, 2025

• Statistical Machine Learning (Undergraduate, S1S2, Japanese) with Prof. Issei Sato and Prof. Masashi Sugiyama: 2021-present

• Statistics and Optimization (Undergraduate, A1A2, Japanese) with Prof. Issei Sato and Prof. Masashi Sugiyama: 2021-present

• Intelligent Systems (Undergraduate, A1A2, Japanese) with Prof. Issei Sato, Prof. Masashi Sugiyama, and Prof. Yusuke Miyao: 2021-present

• Machine Learning (UTokyo Extension, Japanese): 2024 Spring

Tools and Software

• capcode (Jun 2026)
  A dataset-construction framework for detecting cheating by coding agents. It modifies coding tasks so that multiple outputs are valid and randomly selects one as the expected answer, which caps the achievable pass rate. Suspiciously high scores then reveal test-gaming rather than skill. From our paper. Created by Thanawat Lodkaew.

• capreward (Jun 2026)
  A reward function that mitigates reward hacking in RL for code generation. Built on capcode's randomized tasks, it penalizes implausibly high pass rates that exceed each task's cap, making cheating solutions unattractive during training. From the same paper as capcode. Also created by Thanawat Lodkaew.

• notations-cli (Mar 2026)
  Extract notation tables from arXiv papers using LLMs. Give it an arXiv ID, URL, or local LaTeX source and it produces a searchable, self-contained HTML page listing the paper's symbols and their definitions, rendered with KaTeX. Handy when reading equation-heavy papers.

• translation-squared (Mar 2026)
  A desktop app for checking translation quality via back-translation. It translates your text, translates it back without seeing the original, and highlights the semantic differences with color-coded annotations, so you can spot errors in a language you understand. Supports English, Japanese, and Chinese.

• capbencher (Feb 2026)
  A toolkit that gives an LLM benchmark a built-in alarm for test-set overfitting. It randomizes questions so that the maximum achievable score is capped below 100%; scores above the cap signal contamination or leaderboard manipulation. Companion to our ICML 2026 paper, with a Hugging Face dataset for open evaluation without disclosing the true answers. A collaboration with Thanawat Lodkaew and Ikko Yamane.

• bibfixer (Sep 2025)
  Standardize BibTeX entries using LLMs and web search. It completes missing metadata, fixes venue names and title capitalization, and enforces a consistent style across the bibliography, so you can review a clean diff instead of fixing entries by hand. I made this before hallucinated citations became a widespread issue in the research community. The original intent was simply to fix bib entries taken from Google Scholar.

• arxiv-latex-mcp (Apr 2025)
  MCP server that fetches and processes arXiv LaTeX sources, letting MCP clients such as Claude Code and Cursor read papers from the original LaTeX instead of PDFs. This makes LLMs more reliable at interpreting math-heavy content.

• arxiv-to-prompt (Feb 2025)
  Transform an arXiv paper into a single flattened LaTeX file ready to paste into an LLM prompt. It downloads the source, resolves \input and \include commands, and can strip comments and appendices, extract specific sections, and count tokens.

• paper2slides (Jul 2024)
  Transform arXiv papers into slides using LLMs. It flattens the paper's LaTeX source, prompts an LLM to write Beamer code, and compiles the result into a PDF, with a CLI for the full pipeline and a Streamlit UI for interactive editing. Back in 2024, the first draft rarely compiled cleanly, so it runs a repair loop: the generated code goes back to the LLM along with a LaTeX linter's warnings, letting it fix its own syntax errors before the final compile.

• cleanprompt (Mar 2024)
  Anonymize sensitive information in text before sending it to LLM apps. It detects emails, phone numbers, names, organizations, and custom patterns using regexes and named entity recognition, replaces them with placeholders, and restores the originals afterward.

• irreducible (Mar 2023)
  Code for estimating the Bayes error, the irreducible error of a classification task, from soft labels alone. The method is model-free, instance-free, and hyperparameter-free. Useful for checking whether a model's accuracy is approaching the theoretical limit or overfitting the test set. From our ICLR 2023 paper, selected for oral (notable-top-5%).

• flooding (Feb 2020)
  Implementation of the flooding regularizer from our ICML 2020 paper "Do We Need Zero Training Loss After Achieving Zero Training Error?". Flooding keeps the training loss at a small constant level instead of letting it hit zero. It is a one-line change, loss = (loss-b).abs() + b, that works with any optimizer and combines with other regularizers. The repository is a collaboration with Tomoya Sakai and Ikko Yamane.

• comp (May 2019)
  Implementations of complementary-label learning, where each training example is labeled with a class it does not belong to. Covers five methods from our NeurIPS 2017 paper and ICML 2019 paper (plus a baseline from ECCV 2018), for linear models and MLPs.

• pconf (Oct 2018)
  Code for positive-confidence (Pconf) classification from our NeurIPS 2018 spotlight paper "Binary Classification from Positive-Confidence Data". The task is to train a binary classifier from positive examples equipped with confidence scores, without any negative data.