Flavien Prost

@Google researcher, working on Gemini Safety.

[Robert Dadashi @robdadashi]

Today we are releasing the best open-weights model you can run on a single device reaching 1339 Elo on LMsys for Gemma 3 27B (aka zizou-10)!

Very strong capabilities on math, multilingual, coding, instruction following, function calling !

[Alexandru Tifrea @alexandrutifrea]

Very excited about this tutorial at #AAAI2025 on inducing privacy, fairness or robustness to distribution shifts when data is imperfect (e.g. unlabeled, noisy)! You can check out the slides at https://t.co/8oivNIpnRn

[Ziteng Sun @SZiteng]

Inference-time procedures (e.g. Best-of-N, CoT) have been instrumental to recent development of LLMs. The standard RLHF framework focuses only on improving the trained model. This creates a train/inference mismatch.

Can we align our model to better suit a given inference-time procedure?

We answer this affirmatively, check out the thread below.

[Ahmad Beirami @abeirami]

Excited to share 𝐈𝐧𝐟𝐀𝐥𝐢𝐠𝐧!

Alignment optimization objective implicitly assumes 𝘴𝘢𝘮𝘱𝘭𝘪𝘯𝘨 from the resulting aligned model. But we are increasingly using different and sometimes sophisticated inference-time compute algorithms.

How to resolve this discrepancy?🧵

[Flavien Prost @FlavienProst]

Come and join us! We are looking for great and passionate people who want to improve Gemini Safety!

[Preethi Lahoti @PreethiLahoti]

My team at @GoogleDeepMind in Zurich is hiring. If you are passionate about safety, and the opportunity to actually do pre-training on Gemini scale models (!!) excites you then this is for you!

[Alexandru Tifrea @alexandrutifrea]

Come to the poster and check out the paper if you'd like to learn more! https://t.co/yS9LbFN0Rx

This is joint work with amazing collaborators at @GoogleDeepMind: @PreethiLahoti, @packer_ben, Yoni Halpern, @abeirami, and @FlavienProst.

[Robert Dadashi @robdadashi]

I am very happy to announce that Gemma 1.1 Instruct 2B and “7B” are out! Here are a few details about the new models:
1/11

[Alexandru Tifrea @alexandrutifrea]

@FannyYangETH Finally, we've just published on arXiv my work during an internship in the Responsible AI team@Google Research (joint work with @PreethiLahoti, @packer_ben, Yoni Halpern, @abeirami, @FlavienProst). Let's chat, if you're interested in (post-proc) mitigations for group fairness!

[Ahmad Beirami @abeirami]

⚠️#Internship2024 Alert⚠️

Are you a 𝐏𝐡𝐃 𝐬𝐭𝐮𝐝𝐞𝐧𝐭 excited about #ResponsibleAI in foundation models? Do you have experience training/evaluating them?

Team members: @PreethiLahoti @infoxiao @ananthbshankar @sroy_subhrajit @aradsinha @YaoQinUCSD (RADML at @GoogleResearch)

[Ahmad Beirami @abeirami]

[Call for papers]
#NeurIPS2023 R0-FoMo Workshop

Robustness of Zero/Few-shot Learning in Foundation Models We solicit novel contributions that relate broadly to zero/few-shot learning in foundation models, with both empirical and theoretical nature.

Please R/T

[Google AI @GoogleAI]

Announcing the release of MinDiff, a new regularization technique available in the @TensorFlow Model Remediation library for effectively and efficiently mitigating unfair biases when training #MachineLearning models. Learn more below: https://t.co/xnhoOrUUYK

[Preethi Lahoti @PreethiLahoti]

Super happy to share that our paper “Fairness without Demographics through Adversarially Reweighted Learning" has been accepted at #NeurIPS2020.

A big thanks to all my co-authors, reviewers, and colleagues at @GoogleAI, #MPI-INF and #MPI-SWS for their valuable feedback!

[Maciej Kula @Maciej_Kula]

Excited to announce TensorFlow Recommenders, just in time for #RecSys2020!

[Flavien Prost @FlavienProst]

This work focuses on improving the model performance on worst case groups when no demographic information are available! It is achieved via an adversarial re-weighting learner which identifies computationally identifiable subgroups that underperform.

[Preethi Lahoti @PreethiLahoti]

How can we train ML models to achieve fairness when we do not know protected group memberships? Excited to share a new paper on this (https://t.co/Ja4jSPSgi4) from my internship @GoogleAI. Joint work with @alexbeutel @edchi @chenjilin @Nithum @FlavienProst and others. 1/n

[Flavien Prost @FlavienProst]

Check out our poster at #neurIPS19 in the workshop "ML with Guarantees".
We will describe how Maximum Mean Discrepancy can improve the trade-off between performance and fairness in production systems (https://t.co/VOHoC7qfwG).