Been Kim
26 papers · 2014–2025 · 4 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (11) π Conference Polyglot (4) π§ Keyword Pioneer π Interdisciplinary Bridge π£ Hot Topic Early Bird
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Conference Polyglot
(4)
π
Keyword Trendsetter Combo
(5)
π
Triple Crown
π
Keyword Champion
(3)
π¬
Deep Specialist
(12)
π§¬
Topic Evolution
ποΈ
Keyword Collector
(90)
π
Century Club
(26)
β
The Questioner
π
Conference Pioneer
β‘
Prolific Year
(5)
π
Trend Setter
Conferences
NIPS (15)
ICML (6)
ICLR (4)
AISTATS (1)
Top co-authors
Keywords
feature importance
(4)
model interpretability
(3)
concept-based explanation
(3)
generative model
(2)
image classification
(2)
neural network interpretability
(2)
out-of-distribution detection
(2)
feature attribution
(2)
saliency map
(2)
saliency method
(2)
neural network
(2)
prototype learning
(2)
knowledge editing
(1)
sequential decision making
(1)
feature extraction
(1)
epistemic uncertainty
(1)
variational inference
(1)
uncertainty quantification
(1)
feature selection
(1)
explainable ai
(1)
Papers
How new data permeates LLM knowledge and how to dilute it
ICLR 2025
Position: We Canβt Understand AI Using our Existing Vocabulary
ICML 2025
Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty
ICML 2025
Donβt trust your eyes: on the (un)reliability of feature visualizations
ICML 2024
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
NIPS 2023
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
NIPS 2023
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding
NIPS 2023
On the Relationship Between Explanation and Prediction: A Causal View
ICML 2023
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
ICLR 2022
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis
NIPS 2022
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation
ICLR 2022
Debugging Tests for Model Explanations
NIPS 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
NIPS 2020
Concept Bottleneck Models
ICML 2020
Towards Automatic Concept-based Explanations
NIPS 2019
Interpreting Black Box Predictions using Fisher Kernels
AISTATS 2019
Visualizing and Measuring the Geometry of BERT
NIPS 2019
A Benchmark for Interpretability Methods in Deep Neural Networks
NIPS 2019
Learning how to explain neural networks: PatternNet and PatternAttribution
ICLR 2018
Sanity Checks for Saliency Maps
NIPS 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
ICML 2018
Human-in-the-Loop Interpretability Prior
NIPS 2018
To Trust Or Not To Trust A Classifier
NIPS 2018
Examples are not enough, learn to criticize! Criticism for Interpretability
NIPS 2016
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
NIPS 2015
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
NIPS 2014