Top Tweets for #datavaluation
Congrats to my co-authors @RachaelSim2 @YvonneFan12 @snoidetx @michael_xinyi @pjaillet!!!
@icmlconf #ICML2026
#CollaborativeLearning involves training high-quality models using datasets from a number of sources. To incentivize sources to share data, existing #DataValuation methods fairly reward each source based on its data submitted as is. However, as these methods do not verify nor incentivize data truthfulness, the sources can manipulate their data (e.g., by submitting duplicated or noisy data) to artificially increase their valuations and rewards or prevent others from benefiting. This paper presents the first mechanism that provably ensures (F) collaborative #fairness and incentivizes (T) #truthfulness at equilibrium for Bayesian models. Our mechanism combines semivalues (e.g., #ShapleyValue), which ensure fairness, and a truthful data valuation function (DVF) based on a validation set that is unknown to the sources. As semivalues are influenced by others' data, we introduce an additional condition to prove that a source can maximize its expected data values in coalitions and semivalues by submitting a dataset that captures its true knowledge. Additionally, we discuss the implications and suitable relaxations of (F) and (T) when the mediator has a limited budget for rewards or lacks a validation set. Our theoretical findings are validated on synthetic and real-world datasets.


Not all data is equal.
xKnownβs AI Agent listens, analyzes, and assigns a real-time value to every voice snippet you upload β based on content, uniqueness, and usefulness.
You speak. It evaluates. You earn.
#xKnown #DataValuation

2οΈβ£ 94.7% completion rate; a testament to the dedication and smooth experience.
Massive thanks to everyone who participated; this is what decentralized intelligence looks like in action.
#Codatta #AIResearch #CMU #DataValuation
π¨π€ How can we reduce the cost of cooperative game-based #DataValuation without retraining a model for every coalition?
π‘π DUPRE β Data Utility PREdiction for efficient data valuation β fits a #GaussianProcess predictor with a sliced Wasserstein kernel to estimate each coalitionβs utility from just a handful of evaluated subsets.
π©βπ»π¨βπ» nguyen pham @RachaelSim2 @qphong see kiong ng @bryanklow
π Paper: https://t.co/28qXTw5vdR
π Catch DUPRE @AAMASconf #AAMAS2025 β 22 May (afternoon)
π£Oral in Ambassador Ballroom β’ Salon 3 (3 pm)
πΌ Poster #1303 in Ontario Exhibit Hall (3rd floor) ( p.m.)
Drop by to see how DUPRE:
β
exploits ownersβ data similarity to predict utilities
β
plugs into any cooperative game theory techniques
β
delivers uncertainty-aware data valuations
πππ Ruth @qphong @_Hu_Wenyang @ZCODE0
@ray_qiaorui @JingtanW @PangWeiKoh et al.
Accepted @icmlconf #ICML2025: #DataValuation #DataCentricAI #DataSelection #LLM #LLMs #BayesianOptimization #FederatedLearning


The @icmlconf #ICML2024 work of @xiaoqiang_98 @michael_xinyi @WuZhaoxuan et Al. presents distributionally robust #DataValuation without a known validation distribution.
#DataCentricAI
Paper: https://t.co/ehh0zmawjA
Visit us at Poster Session 3 Wed 24 Jul 11:30AM Hall C 4-9 #2402
The @icmlconf #ICML2024 work of @RachaelSim2 @YvonneFan12 @snoidetx et al. presents DADS to select data for model training while anticipating #DataDeletions.
#DataSelection #ActiveLearning #DataValuation
https://t.co/M1XhwZb3IN
Poster Session 4 Wed 24 Jul 1:30PM Hall C 4-9 #2306
Our research group & collaborators have put together 4 chapters in the #FederatedLearning: Theory and Practice book: fairness (ch.8), #DataValuation (ch.15) & incentives (ch.16) in #FederatedLearning, and federated sequential decision making (ch.14).
https://t.co/rFgJNudTKM (1/n)

#DataDeletion challenges fairness & interpretability of #DataValuation when they co-exist.
The #AAAI2024 @RealAAAI work of @snoidetx fan jue @RachaelSim2 introduces DeRDaVa to solve this problem...
#ShapleyValue (1/n)
Congrats to @ZhuanghuaL luo luo @snoidetx fan jue @RachaelSim2 for their accepted papers to #AAAI2024 @RealAAAI
#Optimization #ShapleyValue #DataValuation

The #NeurIPS2023 @NeurIPSConf work of @RachaelSim2 yehong @nghiaht87 @michael_xinyi @pjaillet introduces #DifferentialPrivacy as an incentive for collaborative ML, besides fairness, individual rationality...
#ShapleyValue #DataValuation #FederatedLearning
https://t.co/cfXoni4i7W
The #NeurIPS2023 @NeurIPSConf work of @michael_xinyi @chi_thanh_lam chuan-sheng proposed the model #ShapleyValue for equitable model valuation (in contrast to #DataValuation).
#FederatedLearning
Congrats! to @RachaelSim2 @insebtion @michael_xinyi greg @chi_thanh_lam @arun_v3rma @Dai_Zh @ZCODE0 @qphong @nghiaht87 yehong chuan-sheng @pjaillet
Accepted papers @NeurIPSConf #NeurIPS2023: #ShapleyValue #DataValuation #FederatedLearning #BayesianOptimization #AI4Science

Value of Saudi data as a national treasure SAR 467 billion.
Learn More β‘οΈhttps://t.co/Ibf1OSs8PI
#DataValue #DataValuation #DataQuality #GDP #DataManagement #Data #Analytics #CDO #CheifDataOfficer #YDC

The #ICML2023 @icmlconf work of @ray_qiaorui @michael_xinyi @bryanklow introduces #ShapleyValue fairness incentive and #DataValuation in collaborative #CausalInference. #FederatedLearning
Paper: https://t.co/gtzpH6NhDU
Poster: Exhibit Hall 1 #422 Tue 25 Jul 2-3:30pm HST
Congrats to @apivich_h @Dai_Zh jasraj @ray_qiaorui @xiaoqiang_98 @michael_xinyi chuan-sheng see-kiong!
Accepted papers @icmlconf #ICML2023: #ActiveLearning #NeuralTangentKernel #DeepNeuralNetworks #ShapleyValue #DataValuation #FederatedLearning #CausalInference #GaussianProcess

π’ Want to find mislabeled points in your data? Then check out our new #ICML2023 paper introducing Data-OOB (out-of-bag), the latest advance in #datavaluation. It accurately finds noise + is fast (scales to >10^6 pts)π§΅
Paper https://t.co/cu7upPZ6HX
Code https://t.co/DmkMuob1qH

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