Iterative Hard Thresholding for Keyword Extraction from Large Text Corpora. - Steve Yadlowsky; Preetum Nakkarin; Jingyan Wang 001; Rishi Sharma; Laurent El Ghaoui (ICMLA, 2014)

Adaptive Sampling Probabilities for Non-Smooth Optimization. - Hongseok Namkoong; Aman Sinha 001; Steve Yadlowsky; John C. Duchi (ICML, 2017)

Derivative Free Optimization Via Repeated Classification. - Tatsunori Hashimoto; Steve Yadlowsky; John C. Duchi (AISTATS, 2018)

A Calibration Metric for Risk Scores with Survival Data. - Steve Yadlowsky; Sanjay Basu; Lu Tian (MLHC, 2019)

Estimation and Validation of a Class of Conditional Average Treatment Effects Using Observational Data. - Steve Yadlowsky; Fabio Pellegrini; Federica Lionetto; Stefan Braune; Lu Tian (CoRR, 2019)

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. - Hongseok Namkoong; Ramtin Keramati; Steve Yadlowsky; Emma Brunskill (NeurIPS, 2020)

Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening. - Sehj Kashyap; Saurabh Gombar; Steve Yadlowsky; Alison Callahan; Jason A. Fries; Benjamin A. Pinsky; Nigam H. Shah (J. Am. Medical Informatics Assoc., 2020)

Deep Cox Mixtures for Survival Regression. - Chirag Nagpal; Steve Yadlowsky; Negar Rostamzadeh; Katherine A. Heller (MLHC, 2021)

Counterfactual Invariance to Spurious Correlations in Text Classification. - Victor Veitch; Alexander D'Amour; Steve Yadlowsky; Jacob Eisenstein (NeurIPS, 2021)

SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. - Steve Yadlowsky; Taedong Yun; Cory Y. McLean; Alexander D'Amour (NeurIPS, 2021)

Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests. - Victor Veitch; Alexander D'Amour; Steve Yadlowsky; Jacob Eisenstein (CoRR, 2021)

Calibration Error for Heterogeneous Treatment Effects. - Yizhe Xu; Steve Yadlowsky (AISTATS, 2022)

The MultiBERTs: BERT Reproductions for Robustness Analysis. - Thibault Sellam; Steve Yadlowsky; Ian Tenney; Jason Wei; Naomi Saphra; Alexander D'Amour; Tal Linzen; Jasmijn Bastings; Iulia Raluca Turc; Jacob Eisenstein; Dipanjan Das 001; Ellie Pavlick (ICLR, 2022)

Boosting the interpretability of clinical risk scores with intervention predictions. - Eric Loreaux; Ke Yu; Jonas Kemp; Martin Seneviratne; Christina Chen; Subhrajit Roy; Ivan Protsyuk; Natalie Harris; Alexander D'Amour; Steve Yadlowsky; Mingjun Chen (CoRR, 2022)

Underspecification Presents Challenges for Credibility in Modern Machine Learning. - Alexander D'Amour; Katherine A. Heller; Dan Moldovan; Ben Adlam; Babak Alipanahi; Alex Beutel; Christina Chen; Jonathan Deaton; Jacob Eisenstein; Matthew D. Hoffman; Farhad Hormozdiari; Neil Houlsby; et al. (J. Mach. Learn. Res., 2022)

Diagnosing Model Performance Under Distribution Shift. - Tiffany Tianhui Cai; Hongseok Namkoong; Steve Yadlowsky (CoRR, 2023)

Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models. - Steve Yadlowsky; Lyric Doshi; Nilesh Tripuraneni (CoRR, 2023)

Choosing a Proxy Metric from Past Experiments. - Nilesh Tripuraneni; Lee Richardson; Alexander D'Amour; Jacopo Soriano; Steve Yadlowsky (KDD, 2024)

LearnLM: Improving Gemini for Learning. - Abhinit Modi; Aditya Srikanth Veerubhotla; Aliya Rysbek; Andrea Huber; Brett Wiltshire; Brian Veprek; Daniel Gillick; Daniel Kasenberg; Derek Ahmed; Irina Jurenka; James Cohan; Jennifer She; et al. (CoRR, 2024)