64 citations · 484 across the 23 of their papers we have counts for
25 papers
Evaluating Rubric Generation with Interventional Transfer
Erik Skalnes, Layne C. Price, Raviteja Anantha +1
Instance-specific rubrics are common in AI benchmarks where reliable evaluation requires specific expert knowledge. This approach is difficult to scale, prompting research into the…
Align-RAG: Alignment Is All You Need for TSFM In-Context Learning
Mohammad Asadi, Soheil Hor, Bardiya Akhbari +6
Retrieval-augmented forecasting promises to adapt frozen Time Series Foundation Models (TSFMs) to new domains without fine-tuning, but recent methods typically rely on learned fusi…
NanoFlux: Adversarial Dual-LLM Evaluation and Distillation For Multi-Domain Reasoning
Raviteja Anantha, Soheil Hor, Teodor Nicola Antoniu +1
We present NanoFlux, a novel adversarial framework for generating targeted training data to improve LLM reasoning, where adversarially-generated datasets containing fewer than 200…
Activity Classification Using Unsupervised Domain Transfer from Body Worn Sensors
Chaitra Hedge, Gezheng Wen, Layne C. Price
Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are…
Training self-supervised peptide sequence models on artificially chopped proteins
Gil Sadeh, Zichen Wang, Jasleen Grewal +2
Representation learning for proteins has primarily focused on the global understanding of protein sequences regardless of their length. However, shorter proteins (known as peptides…
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Yulun Wu, Robert A. Barton, Zichen Wang +5
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variatio…
Variational Causal Inference
Yulun Wu, Layne C. Price, Zichen Wang +3
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the out…
Robust posterior inference when statistically emulating forward simulations
Grigor Aslanyan, Richard Easther, Nathan Musoke +1
Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters. While various emulation schemes exist to approximate…
Discovering Invariances in Healthcare Neural Networks
Mohammad Taha Bahadori, Layne C. Price
We study the invariance characteristics of pre-trained predictive models by empirically learning transformations on the input that leave the prediction function approximately uncha…
Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau +4
A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is…
Effect of reheating on predictions following multiple-field inflation
Selim C. Hotinli, Jonathan Frazer, Andrew H. Jaffe +3
We study the sensitivity of cosmological observables to the reheating phase following inflation driven by many scalar fields. We describe a method which allows semi-analytic treatm…
The Spectrum of the Axion Dark Sector
Matthew J. Stott, David J. E. Marsh, Chakrit Pongkitivanichkul +2
Axions arise in many theoretical extensions of the Standard Model of particle physics, in particular the "string axiverse". If the axion masses, , and (effective) decay consta…
Reconstructing the redshift evolution of escaped ionizing flux from early galaxies with Planck and HST observations
Layne C. Price, Hy Trac, Renyue Cen
While galaxies at are believed to dominate the epoch of cosmic reionization, the escape fraction of ionizing flux and the photon product…
The Velocity Distribution Function of Galaxy Clusters as a Cosmological Probe
M. Ntampaka, H. Trac, J. Cisewski +1
We present a new approach for quantifying the abundance of galaxy clusters and constraining cosmological parameters using dynamical measurements. In the standard method, galaxy lin…
Ultracompact minihalos as probes of inflationary cosmology
Grigor Aslanyan, Layne C. Price, Jenni Adams +5
Cosmological inflation generates primordial density perturbations on all scales, including those far too small to contribute to the cosmic microwave background. At these scales, is…
Designing and testing inflationary models with Bayesian networks
Layne C. Price, Hiranya V. Peiris, Jonathan Frazer +1
Even simple inflationary scenarios have many free parameters. Beyond the variables appearing in the inflationary action, these include dynamical initial conditions, the number of f…
Power spectrum oscillations from Planck-suppressed operators in effective field theory motivated monodromy inflation
Layne C. Price
We consider a phenomenological model of inflation where the inflaton is the phase of a complex scalar field . Planck-suppressed operators of modi…
Learn-As-You-Go Acceleration of Cosmological Parameter Estimates
Grigor Aslanyan, Richard Easther, Layne C. Price
Cosmological analyses can be accelerated by approximating slow calculations using a training set, which is either precomputed or generated dynamically. However, this approach is on…
MultiModeCode: An efficient numerical solver for multifield inflation
Layne C. Price, Jonathan Frazer, Jiajun Xu +2
We present MultiModeCode, a Fortran 95/2000 package for the numerical exploration of multifield inflation models. This program facilitates efficient Monte Carlo sampling of prior p…
Gravitational wave consistency relations for multifield inflation
Layne C. Price, Hiranya V. Peiris, Jonathan Frazer +1
We study the tensor spectral index and the tensor-to-scalar ratio in the simplest multifield extension to single-field, slow-roll inflation models. We show that multifiel…
Inflating an Inhomogeneous Universe
Richard Easther, Layne C. Price, Javier Rasero
While cosmological inflation can erase primordial inhomogeneities, it is possible that inflation may not begin in a significantly inhomogeneous universe. This issue is particularly…
The Knotted Sky II: Does BICEP2 require a nontrivial primordial power spectrum?
Kevork N. Abazajian, Grigor Aslanyan, Richard Easther +1
An inflationary gravitational wave background consistent with BICEP2 is difficult to reconcile with a simple power-law spectrum of primordial scalar perturbations. Tensor modes con…
The Knotted Sky I: Planck constraints on the primordial power spectrum
Grigor Aslanyan, Layne C. Price, Kevork N. Abazajian +1
Using the temperature data from Planck we search for departures from a power-law primordial power spectrum, employing Bayesian model-selection and posterior probabilities. We param…
Simple predictions from multifield inflationary models
Richard Easther, Jonathan Frazer, Hiranya V. Peiris +1
We explore whether multifield inflationary models make unambiguous predictions for fundamental cosmological observables. Focusing on -quadratic inflation, we numerically evaluat…