Richard Zemel
Professor
Studies representation learning, generative models and algorithmic fairness.
Richard Zemel's lab at Toronto works on generative & foundation models and trustworthy & safe ML. 25 papers since 2023, 18 at top venues, mostly EMNLP and ICML. 26 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 72%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 26
Papers by yearfrom DBLP, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 3
- 12 papers2024 · T1 8
- 8 papers2023 · T1 7
T1 venuesOtherVenues: EMNLP, ICML, FAccT, ICLR, NAACL
Research focuspaper titles that use each topic's words
- Generative & foundation models9 papers
- Trustworthy & safe ML2 papers
- algorithmic fairness0 papers
- algorithmic0 papers
- representation learning0 papers
Paper list
From DBLP.
20255 papers
- ICML (T1)Adaptive Elicitation of Latent Information Using Natural Language (opens in a new tab)
- NeurIPS (T1)Guiding LLM Decision-Making with Fairness Reward Models (opens in a new tab)
- ICML (T1)QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions (opens in a new tab)
- FAccTTowards Effective Discrimination Testing for Generative AI (opens in a new tab)
- Findings of ACLTowards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation (opens in a new tab)
202412 papers
- Findings of EMNLPAttribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification (opens in a new tab)
- ECCV (T1)Controlling the World by Sleight of Hand (opens in a new tab)
- EMNLP (T1)FLIRT: Feedback Loop In-context Red Teaming (opens in a new tab)
- Trans. Mach. Learn. Res.Improving Predictor Reliability with Selective Recalibration (opens in a new tab)
- CoLLAsIntegrating Present and Past in Unsupervised Continual Learning (opens in a new tab)
- ICML (T1)Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift (opens in a new tab)
- ICLR (T1)Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models (opens in a new tab)
- NAACL (T1)The steerability of large language models toward data-driven personas (opens in a new tab)
- Findings of NAACLTokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies (opens in a new tab)
- NAACL (T1)Toward Informal Language Processing: Knowledge of Slang in Large Language Models (opens in a new tab)
- EMNLP (T1)Training-free Deep Concept Injection Enables Language Models for Video Question Answering (opens in a new tab)
- EMNLP (T1)Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities (opens in a new tab)
20238 papers
- FAccT"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation (opens in a new tab)
- EMNLP (T1)Coordinated Replay Sample Selection for Continual Federated Learning (opens in a new tab)
- NeurIPS (T1)Distribution-Free Statistical Dispersion Control for Societal Applications (opens in a new tab)
- WSDM (T1)Incorporating Fairness in Large Scale NLU Systems (opens in a new tab)
- WSDM (T1)Privacy in the Time of Language Models (opens in a new tab)
- ICLR (T1)Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions (opens in a new tab)
- ACL (T1)Resolving Ambiguities in Text-to-Image Generative Models (opens in a new tab)
- ICCV (T1)SurfsUp: Learning Fluid Simulation for Novel Surfaces (opens in a new tab)
Where alumni went26 listed
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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