Top Tweets for #MachineUnlearning
📢🚀 NPTEL Course Alert 2026!
Excited to co-teach #ResponsibleAI this July with Profs. @ravi_iitm @arunrajkumar485 ! 🙌
Covers: #Fairness #transparency #Interpretability #MachineUnlearning & more.
Register: https://t.co/H8tUeW9HHY
#CFBR #ProfGiri #Teaching @nptel_official
Presenting Today at #AISTATS2026
An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations (1/3)
#MachineUnlearning #NeuralCollapse
🧵 [6/5] It was nice to know from the industry folks visiting our poster yesterday that #MachineUnlearning has many practical use cases! Also attracted many 2nd order optimization folks looking for use cases, such as unlearning. @lululu0082
![bryanklow's tweet photo. 🧵 [6/5] It was nice to know from the industry folks visiting our poster yesterday that #MachineUnlearning has many practical use cases! Also attracted many 2nd order optimization folks looking for use cases, such as unlearning. @lululu0082 https://t.co/V2x1kjg4Vd](https://pbs.twimg.com/media/HGrZM69WgAAgU9T.jpg)
![bryanklow's tweet photo. 🧵 [6/5] It was nice to know from the industry folks visiting our poster yesterday that #MachineUnlearning has many practical use cases! Also attracted many 2nd order optimization folks looking for use cases, such as unlearning. @lululu0082 https://t.co/V2x1kjg4Vd](https://pbs.twimg.com/media/HGrZM69WIAA7ohM.jpg)
Interested in #MechanisticInterpretability #MachineUnlearning #PrivacyLeakage #MultiAgentSafety and more? 🤖🔍
Join CS7.405 Responsible & Safe AI Systems course project posters (21!) 🎓✨
🗓️ 25 April ⏰ 3:30 PM @iiit_hyderabad
Open to all… do join 🙌🏽 #ResponsibleAI #SafeAI
'AI Forever' is a liability. 🧠❌
The next frontier of data privacy is Machine Unlearning—forcing AI to forget what it shouldn't know. Intelligence is as much about selection as it is memory.
Full Take: https://t.co/Almt8v0QZa
#AI #MachineUnlearning #Privacy #AchieveAI
[1/5] Making AI “forget” without damaging its “brain” 🧠💻 is notoriously hard, but vital!
An AI model provider should give users the right to “own” and remove their data from its AI model in real time 📈 while retaining its performance. This is the core of “#MachineUnlearning”.
In our #ICLR2026 paper, we put one of the competitive unlearning algorithms known as Newton unlearning to the test, reveal where it fails on modern AI models (#LLMs included!), and turn those failures around with a rigorous fix 🛠️✅.
Ready for us to spill the tea? More below ☕ 🧵👇
=============
📄📌Paper “How to Cure Newton for Unlearning Neural Networks? An Empirical Study from the Hessian Perspective” (🔗https://t.co/NAv5EsJUjx) – a joint work with @nhungbui1299 @lululu0082 @RachaelSim2 See-Kiong Ng.
📅🗣️Meet us @iclr_conf 🇧🇷 Poster Session 2 Thurs Apr 23 3:15PM Pavilion 4 P4-#5304.
Graph-based unlearning erases class info 10-20x faster across vision & audio models. Privacy-preserving updates without retraining. #MachineUnlearning #AudioMIR https://t.co/YASfzLQHod
🔔 New Published Papers of #MDPIfutureinternet
Title: Machine Unlearning: A Perspective, Taxonomy, and Benchmark Evaluation
https://t.co/XTqnI5XWot
#machineunlearning #privacy #deeplearning
Hirundo joins HPE's Unleash AI partner program, bringing machine unlearning to help businesses remove sensitive training data, boost compliance and optimize AI models. #HPE #AI #MachineUnlearning
https://t.co/goBhCGGLrs
💻 L’uso aziendale dell’#IntelligenzaArtificiale pone sfide oltre il #GDPR. Il problema del Machine Unlearning, la tutela del segreto industriale e il ruolo dei nuovi #neurodiritti 👇
#Altalex #DatiPersonali #Privacy #MachineUnlearning #SegretoIndustriale
https://t.co/bL7Pw2EYgO
OpenAI will no longer evaluate against SWE-bench Verified. | Next in AI | Astha La Vista #ArtificialIntelligence #AgenticAI #CyberSecurity #AIInfrastructure #AIEvaluation #MachineUnlearning #EnterpriseAI #PhysicalAI #LLMs #AIResearch
Next in AI: Issue #115 https://t.co/k1zhGgzxI2 via @LinkedIn #ArtificialIntelligence #AgenticAI #CyberSecurity #AIInfrastructure #AIEvaluation #MachineUnlearning #EnterpriseAI #PhysicalAI #LLMs #AIResearch
When a company claims that your personal data has been removed from their model, have you ever wondered whether they've indeed done so? 🤔
If a new paper on arXiv claims that its proposed #MachineUnlearning algorithm can unlearn your personal data from an #LLM, how do we know if it can indeed do so?
🛢️WaterDrum is the first data-centric LLM unlearning metric based on watermarking that is calibrated, requires no retraining, works for blackbox models and when forget/retain sets have similar data.
Let the (Water)Drums roll at Rio! @iclr_conf #ICLR2026
Introducing WaterDrum🛢️, the first data-centric #LLM #unlearning metric that leverages robust text #watermarking💧to provide an effective, practical, and resilient way to evaluate LLM unlearning performance😉! (1/n)
#MachineUnlearning #LLMs
Is your unlearned model truly forgetting, or is it just hiding the evidence? For years, #MachineUnlearning evaluation has relied heavily on logit-based metrics (like accuracy). But in our new paper, "Are we truly forgetting? A critical re-examination of machine unlearning evaluation protocols", accepted to #EAAI, we found that these metrics can be misleading.
We observed that while models appear to "forget" based on their outputs, they often merely modify the classifier head while preserving the original knowledge in their feature representations. Essentially, the model isn't unlearning; it's taking a shortcut.
To address this, we propose a holistic benchmark framework:
1. Representation-based metrics: Using CKA and k-NN to rigorously verify if the feature space has truly diverged from the original model.
2. Top Class-wise forgetting: A novel large-scale scenario where forgetting targets are semantically similar to downstream tasks, preventing the model from relying on residual knowledge.
Huge thanks to my co-authors Yongwoo Kim and Donghyun Kim.
Full paper available here: https://t.co/ALBvL9z3p6
Github code: https://t.co/b2iWYfcc9c
🎉 "Neural Machine UnRanking" accepted in IEEE TNNLS! We formalise machine unlearning for neural IR and propose CoCoL – enabling search systems to selectively forget data for privacy and accuracy, without retraining. Preprint: https://t.co/I6ogY9jmc4 #machineunlearning #NeuralIR
How Machine Unlearning Revolutionizes AI Integrity
https://t.co/pSLcIVxcjf #MachineUnlearning #AIIntegrity #ResponsibleAI #AIEthics #DataPrivacy #AIGovernance #TrustworthyAI #ModelSafety
#PrivacyPreservingAI #FutureOfAI #TechUnity
If you are interested in a rigorous reality check for #MachineUnlearning, please stop by our poster at the #NeurIPS2025 Workshop!
Current unlearning benchmarks often rely on reference-specific metrics, which can hide the truth about a model's retained knowledge.
We propose FADE (Functional Alignment for Distributional Equivalence), a new metric that measures how well an unlearned model aligns with a "retain-only" model that never saw the unwanted data.
- Paper: Reference-Specific Metrics Can Hide the Truth: A Reality Check (https://t.co/WHFRAVNkRO)
- Workshop: Evaluating the Evolving LLM Lifecycle: Benchmarks, Emergent Abilities, and Scaling (Sunday, Dec 7)
Ready to work on #NLP and #MachineUnlearning for #ACL2026. Open to internship opportunities—let’s connect!
I’m actively looking for opportunities to work on NLP topics, especially machine unlearning.
If anyone is looking for a motivated intern, I’d love to connect!
📍 Find me at Google Booth #1633 (Exhibit Hall A,B): I'll be there this Wed & Fri morning. Do drop by for the exciting demos!
#MachineLearning #DifferentialPrivacy #LLMs #TrustworthyAI #GraphLearning #MachineUnlearning
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