Leonardo Cotta, “Causal Lifting and Link Prediction”
Leonardo Cotta, PhD
Postdoctoral Fellow
Vector Institute Toronto
Abstract: Current state-of-the-art causal models for link prediction assume an underlying set of inherent node factors —a innate characteristic defined at the node’s birth— that govern the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent, i.e., the outcome of link interventions depends on existing links. For instance, in the customer-product graph of an online retailer, the effect of an 85-inch TV ad (treatment) likely depends on whether the consumer already has an 85-inch TV. In order to remedy this shortcoming, I will present the first causal model capable of dealing with path dependencies in link prediction. Further, I will introduce the concept of causal lifting, a symmetry in causal models that, when satisfied, allows the identification of causal link prediction queries using limited interventional data. On the estimation side, I will show how invariant pairwise embeddings —a type of symmetry-based joint representation of pairs of nodes— presents lower bias, variance and PAC-Bayes bounds than existing node embedding methods, e.g., GNNs and matrix factorization.
Biography: Leonardo Cotta is a postdoctoral fellow at the Vector Institute in Toronto. He obtained his PhD at Purdue University under Prof. Bruno Ribeiro. His research interests are in invariant and causal representation learning, with a focus on sampling and modeling complex systems. He received fellowships from Vector Institute in 2022 and Qatar Computing Research Institute in 2017, apart from a “Young Talents of Science” award given by the Brazilian Government in 2013.
Tuesdays, 12pm-1:15pm
Held virtually in person at Clark 110 & over Zoom
Check for event details: https://www.minds.jhu.edu/events/calendar/
Join Zoom Meeting
https://wse.zoom.us/j/98624413365
Meeting ID: 986 2441 3365
One tap mobile
+13017158592,,98624413365# US (Washington DC)
+16469313860,,98624413365# US
Dial by your location
+1 301 715 8592 US (Washington DC)
+1 646 931 3860 US
+1 309 205 3325 US
+1 312 626 6799 US (Chicago)
+1 646 558 8656 US (New York)
+1 669 900 6833 US (San Jose)
+1 719 359 4580 US
+1 253 215 8782 US (Tacoma)
+1 346 248 7799 US (Houston)
+1 386 347 5053 US
+1 564 217 2000 US
+1 669 444 9171 US
Meeting ID: 986 2441 3365
Find your local number: https://wse.zoom.us/u/asoOElnUp
Join by SIP
Join by H.323
162.255.37.11 (US West)
162.255.36.11 (US East)
115.114.131.7 (India Mumbai)
115.114.115.7 (India Hyderabad)
213.19.144.110 (Amsterdam Netherlands)
213.244.140.110 (Germany)
103.122.166.55 (Australia Sydney)
103.122.167.55 (Australia Melbourne)
149.137.40.110 (Singapore)
64.211.144.160 (Brazil)
149.137.68.253 (Mexico)
69.174.57.160 (Canada Toronto)
65.39.152.160 (Canada Vancouver)
207.226.132.110 (Japan Tokyo)
149.137.24.110 (Japan Osaka)
Meeting ID: 986 2441 3365