The Machine Learning & Inference Research team I co-lead @Netflix @NetflixResearch is hiring interns for Summer 2024. Looking for a research internship (tackling industry problems while also focusing publishable research!)? Apply thru this listing: jobs.netflix.com/jobs/300628646
06.11.2023 21:49 โ ๐ 6 ๐ 1 ๐ฌ 1 ๐ 1
Remember โLGBT freeโ zones in Poland? New research finds that annual suicide attempts increased by 16%, or 5 attempts per 100k capita after these laws were enacted! www.nber.org/papers/w31702
30.09.2023 20:17 โ ๐ 19 ๐ 12 ๐ฌ 0 ๐ 0
arxiv.org/abs/2302.02392 In offline RL, we replace exploration with assumptions that data is nice. We try to make these minimal by refining standard realizability and coverage assumptions to single policies. We do this via a minimax formulation and strong guarantees for learning the saddle point.
27.09.2023 19:09 โ ๐ 2 ๐ 0 ๐ฌ 0 ๐ 0
arxiv.org/abs/2305.15703 RL only needs mean reward to go (q-fn) so why is distRL (learn whole reward-to-go dist) so empirically effective? We prove distRL is really good when optimal policy has small loss. When that's true then least-squares (q-learning) misses the signal due to heteroskedasticity.
27.09.2023 19:08 โ ๐ 1 ๐ 0 ๐ฌ 0 ๐ 0
arxiv.org/abs/2207.13081 Off-policy eval in POMDPs is tough b/c hidden states ruin memorylessness inducing a curse of horizon. Using histories as instrumental variables, we derive a new Bellman eq for a new kind of v-fn. We solve it using minimax learning to get model-free eval using general fn apx.
27.09.2023 19:06 โ ๐ 2 ๐ 0 ๐ฌ 0 ๐ 0
New platform just in time for shameless plugs of neurips papers! (Tho hoping to use this platform for more than shameless paper plugs... let's see.) Genuinely excited about each of these. Let me try to get you excited about them too with a couple sentences each.
27.09.2023 19:04 โ ๐ 10 ๐ 1 ๐ฌ 3 ๐ 0
Assistant professor of biostatistics at Columbia University
Casual inference, statistics, etc
Pauca sed Matura
Associate professor, Chalmers University of Technology. Machine learning for decision making & healthcare. http://healthyai.se, http://fredjo.com
Cornell Tech professor (information science, AI-mediated Communication, trustworthiness of our information ecosystem). New York City. Taller in person. Opinions my own.
PERSONAL ACCOUNT
ไป/he/him
Some call me NYC's AI Czar.
๐โ๐ฆบ๐ธ๐ฌ๐บ๐ธ
causal ml; ai+society; social media, comp social science. having fun.. my opinions. he/him. http://hci.social/@emrek
Associate Professor, CMU. Researcher, Google. Evaluation and design of information retrieval and recommendation systems, including their societal impacts.
Harvard Professor.
ML and AI.
Co-director of the Kempner Institute.
https://shamulent.github.io
Professor @UCLA, Research Scientist @ByteDance | Recent work: SPIN, SPPO, DPLM 1/2, GPM, MARS | Opinions are my own
Principal Researcher in AI/ML/RL Theory @ Microsoft Research NE/NYC. Previously @ MIT, Cornell. http://dylanfoster.net
RL Theory Lecture Notes: https://arxiv.org/abs/2312.16730
Professing computing and applied math at Cornell. Numerical methods for data science, plasma physics, other stuff depending on the day. Director, Cornell Center for Applied Mathematics; Director, Simons Collaboration on Hidden Symmetries and Fusion Energy.
Econ prof at Oxford.
Machine learning, politics, econometrics, inequality, random reading recs.
maxkasy.github.io/home/
Professor in Operations Research at Copenhagen Business School. In โค๏ธ with Sevilla and its Real Betis Balompiรฉ.
Prof @Wharton @Penn; machine learning for health & social good; foodie, gamer, homebody
Professor of Public Policy at Harvard, co-director of @comppolicylab.bsky.social, applying a computational approach to public policy, including to issues in education, healthcare, and criminal justice. https://5harad.com
associate professor of statistics @uw โข causal inference, machine learning, nonparametrics
alexluedtke.com
Assistant Prof. of CS at Johns Hopkins
Visiting Scientist at Abridge AI
Causality & Machine Learning in Healthcare
Prev: PhD at MIT, Postdoc at CMU
Ph.D. Student @uwstat; Research fellowship @Netflix; visiting researcher @UCJointCPH; M.A. @UCBStatistics - machine learning; calibration; semiparametrics; causal inference.
https://larsvanderlaan.github.io
Assistant Professor at UC Berkeley and UCSF.
Machine Learning and AI for Healthcare. https://alaalab.berkeley.edu/
ML for healthcare and health equity. Assistant Professor at UC Berkeley and UCSF.
https://irenechen.net/
