Mantas Mazeika
15 papers · 2018–2025 · 4 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
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(14)
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(6)
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Artificial Intelligence
(9)
Machine Learning
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(9)
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(6)
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Learning Types Β· Machine Learning
(6)
Natural Language Processing
(4)
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(1)
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(1)
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(1)
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large language model
(4)
out-of-distribution detection
(4)
anomaly detection
(3)
adversarial robustness
(3)
uncertainty estimation
(3)
deep neural network
(2)
language model
(2)
label noise
(2)
uncertainty quantification
(1)
question answering
(1)
adversarial learning
(1)
toxicity detection
(1)
confidence calibration
(1)
video understanding
(1)
temporal reasoning
(1)
data augmentation
(1)
model security
(1)
affective computing
(1)
evaluation framework
(1)
self-supervised learning
(1)
Papers
Tamper-Resistant Safeguards for Open-Weight LLMs
ICLR 2025
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
ICML 2024
Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?
NeurIPS 2024
The WMDP Benchmark: Measuring and Reducing Malicious Use with Unlearning
ICML 2024
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
NeurIPS 2023
Forecasting Future World Events With Neural Networks
NeurIPS 2022
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection
ICML 2022
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios
NeurIPS 2022
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
CVPR 2022
Scaling Out-of-Distribution Detection for Real-World Settings
ICML 2022
Measuring Massive Multitask Language Understanding
ICLR 2021
Deep Anomaly Detection with Outlier Exposure
ICLR 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty
ICML 2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
NeurIPS 2019
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
NeurIPS 2018