Richard Zemel

Adaptive Elicitation of Latent Information Using Natural Language ICML 2025 QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions ICML 2025 Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation ACL 2025 Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification EMNLP 2024 Controlling the World by Sleight of Hand ECCV 2024 FLIRT: Feedback Loop In-context Red Teaming EMNLP 2024 Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift ICML 2024 Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models ICLR 2024 The steerability of large language models toward data-driven personas NAACL 2024 Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies NAACL 2024 Toward Informal Language Processing: Knowledge of Slang in Large Language Models NAACL 2024 Training-free Deep Concept Injection Enables Language Models for Video Question Answering EMNLP 2024 Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities EMNLP 2024 Coordinated Replay Sample Selection for Continual Federated Learning EMNLP 2023 Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions ICLR 2023 Resolving Ambiguities in Text-to-Image Generative Models ACL 2023 SurfsUP: Learning Fluid Simulation for Novel Surfaces ICCV 2023 Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data CLeaR 2022 Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic ACL 2022 Semantically Informed Slang Interpretation NAACL 2022 A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks ICLR 2021 Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes ICLR 2021 Environment Inference for Invariant Learning ICML 2021 Learning a Universal Template for Few-shot Dataset Generalization ICML 2021 NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation UAI 2021 On Monotonic Linear Interpolation of Neural Network Parameters ICML 2021 SketchEmbedNet: Learning Novel Concepts by Imitating Drawings ICML 2021 Theoretical bounds on estimation error for meta-learning ICLR 2021 Wandering within a world: Online contextualized few-shot learning ICLR 2021 Causal Modeling for Fairness In Dynamical Systems ICML 2020 Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling ICML 2020 Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach ICML 2020 Understanding the Limitations of Conditional Generative Models ICLR 2020 A Divergence Minimization Perspective on Imitation Learning Methods CoRL 2019 Aggregated Momentum: Stability Through Passive Damping ICLR 2019 Efficient Graph Generation with Graph Recurrent Attention Networks NeurIPS 2019 Excessive Invariance Causes Adversarial Vulnerability ICLR 2019 Flexibly Fair Representation Learning by Disentanglement ICML 2019 Incremental Few-Shot Learning with Attention Attractor Networks NeurIPS 2019 LanczosNet: Multi-Scale Deep Graph Convolutional Networks ICLR 2019 Lorentzian Distance Learning for Hyperbolic Representations ICML 2019 SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies NeurIPS 2019 Understanding the Origins of Bias in Word Embeddings ICML 2019 Adversarial Distillation of Bayesian Neural Network Posteriors ICML 2018 Learning Adversarially Fair and Transferable Representations ICML 2018 Learning Latent Subspaces in Variational Autoencoders NeurIPS 2018 Neural Guided Constraint Logic Programming for Program Synthesis NeurIPS 2018 Neural Relational Inference for Interacting Systems ICML 2018 Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer NeurIPS 2018 Reviving and Improving Recurrent Back-Propagation ICML 2018 Causal Effect Inference with Deep Latent-Variable Models NeurIPS 2017 Dualing GANs NeurIPS 2017 Few-Shot Learning Through an Information Retrieval Lens NeurIPS 2017 Prototypical Networks for Few-shot Learning NeurIPS 2017 Learning Deep Parsimonious Representations NeurIPS 2016 Understanding the Effective Receptive Field in Deep Convolutional Neural Networks NeurIPS 2016 Exploring Models and Data for Image Question Answering NeurIPS 2015 Skip-Thought Vectors NeurIPS 2015 A Multiplicative Model for Learning Distributed Text-Based Attribute Representations NeurIPS 2014 A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data NeurIPS 2013 Exploring Compositional High Order Pattern Potentials for Structured Output Learning CVPR 2013 On the Representational Efficiency of Restricted Boltzmann Machines NeurIPS 2013 Randomized Optimum Models for Structured Prediction AISTATS 2012 Structured Output Learning with High Order Loss Functions AISTATS 2012 HOP-MAP: Efficient Message Passing with High Order Potentials AISTATS 2010