*(Bridges: Neural Network Policies for Risk-Reward Optimization × Universal Persistent Brownian Motions × ZNF740/BET bromodomain transcriptional programs [validated])*
Adversarial Debate Score
28% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- Convergence of Neural Network Policies for Risk--Reward Optimization
We develop a neural-network framework for multi-period risk--reward stochastic control problems with constrained two-step feedback policies that may be discontinuous in the state. We allow a broad cla...
- Count Bridges enable Modeling and Deconvolving Transcriptomic Data
Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are often not at the desired resolution: while th...
- Deep and Probabilistic Models for Gene Regulatory Network Inference
Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodolo...
- Risk-Controlled Post-Processing of Decision Policies
Predictive models are often deployed through existing decision policies that stakeholders are reluctant to change unless a risk constraint requires intervention. We study risk-controlled post-processi...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.