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Fair Machine Learning Seminar: Variations of Tverberg’s Theorem for Machine Learning and Statistics
Location: SLMath: Baker Board Room Speakers: Jesus De Loera (University of California, Davis)There is an exciting fertile relationship between statistics/machine learning and combinatorial geometry. My talk shows how variations of Tverberg’s theorem (to be explained, don’t worry!) can answer several basic questions from machine learning and statistical inference. In particular, touches on the question: When do I have sufficient data in my sample to conclude learning was properly done?
I will not assume any prior knowledge. All new theorems are joint work with subsets of the following wonderful researchers: T. Hogan, D. Oliveros, E. Jaramillo-Rodriguez, and A. Torres-Hernandez.
Updated on Dec 06, 2023 11:10 AM PST -
MMD Seminar: "Multi-Channel Autobidding with Budget and ROI Constraints" & "New Results on Random Matching Markets"
Location: SLMath: Online/Virtual, Baker Board Room Speakers: Nicki Golrezaei (Massachusetts Institute of Technology), Pawel Pralat (Toronto Metropolitan University)We will have a talk and open reflexions by all the MMD participants.
"Multi-Channel Autobidding with Budget and ROI Constraints" - Nicki Golrezaei
Updated on Dec 05, 2023 08:29 AM PST -
Social Choice Seminar
Location: SLMath: Baker Board Room, Online/VirtualCreated on Sep 12, 2023 08:09 AM PDT -
Carbon Markets Informal Chat
Location: SLMath: Baker Board Room, Online/VirtualCreated on Nov 20, 2023 10:13 AM PST -
Fair Machine Learning Seminar: Problem Session
Location: SLMath: Baker Board Room, Online/VirtualUpdated on Dec 06, 2023 02:10 PM PST -
Network Science Lunch
Location: SLMath: Baker Board Room, Online/VirtualCreated on Nov 15, 2023 09:23 AM PST -
Graduate Students Seminar: An Axiomatic Characterization of Draft Rules
Location: SLMath: Online/Virtual, Baker Board Room Speakers: Jacob Coreno (University of Melbourne)Drafts are sequential allocation procedures for distributing heterogeneous and indivisible objects among agents subject to some priority order (e.g., allocating players’ contract rights to teams in professional sports leagues). Agents report ordinal preferences over objects and bundles are partially ordered by pairwise comparison. We provide a simple characterization of draft rules: they are the only allocation rules which are respectful of a priority (RP), envy-free up to one object (EF1), non-wasteful (NW) and resource-monotonic (RM). RP and EF1 are crucial for competitive balance in sports leagues. We also prove three related impossibility theorems: (i) weak strategy-proofness (WSP) is incompatible with RP, EF1, and NW; (ii) WSP is incompatible with EF1 and (Pareto) efficiency (EFF); and (iii) when there are two agents, strategy-proofness (SP) is incompatible with EF1 and NW. However, draft rules satisfy the competitive-balance properties, RP and EF1, together with EFF and maxmin strategy-proofness. If agents may declare some objects unacceptable, then draft rules are characterized by RP, EF1, NW, and RM, in conjunction with individual rationality and truncation-invariance. In a model with variable populations, draft rules are characterized by EF1, EFF, and RM, together with (population) consistency, top-object consistency, and neutrality; in this setting, the priority emerges endogenously from the properties.
Updated on Dec 05, 2023 10:11 AM PST -
Redistricting Working Group
Location: SLMath: Baker Board Room, Online/VirtualCreated on Sep 13, 2023 11:12 AM PDT -
Postdoc Professional Development Seminar
Location: SLMath: Eisenbud Auditorium, Online/VirtualUpdated on Dec 05, 2023 08:12 AM PST -
Carbon Markets Informal Chat
Location: SLMath: Baker Board Room, Online/VirtualCreated on Nov 20, 2023 10:13 AM PST -
Social Choice Seminar
Location: SLMath: Baker Board RoomCreated on Sep 12, 2023 08:09 AM PDT -
Fair Machine Learning Seminar
Location: SLMath: Baker Board RoomCreated on Sep 20, 2023 08:18 AM PDT -
Network Science Lunch
Location: SLMath: Baker Board Room, Online/VirtualCreated on Nov 15, 2023 09:23 AM PST
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ADJOINT 2024
ADJOINT is a yearlong program that provides opportunities for U.S. mathematicians – especially those from the African Diaspora – to conduct collaborative research on topics at the forefront of mathematical and statistical research. Participants will spend two weeks taking part in an intensive collaborative summer session at SLMath (formerly MSRI). The two-week summer session for ADJOINT 2024 will take place June 24 to July 5, 2024 in Berkeley, California. Researchers can participate in either of the following ways: (1) joining ADJOINT small groups under the guidance of some of the nation's foremost mathematicians and statisticians to expand their research portfolio into new areas, or (2) applying to Self-ADJOINT as part of an existing or newly-formed independent research group to work on a new or established research project. Throughout the following academic year, the program provides conference and travel support to increase opportunities for collaboration, maximize researcher visibility, and engender a sense of community among participants.
Updated on Oct 02, 2023 11:15 AM PDT