Problem Formulation and Literature Review Questions

Demonstrate how you identify important research gaps and convert vague or underspecified problems into well scoped research questions with clear evaluation criteria. Explain your approach to conducting rapid and comprehensive literature reviews, connecting new problems to the state of the art, positioning proposed solutions relative to prior work, selecting baselines, and generating testable hypotheses. Interviewers will assess your ability to define success metrics, design experiments to answer core questions, and show awareness of related approaches and trade offs.

MediumTechnical

27 practiced

Describe how you would embed reproducibility and open-science practices into both the literature review and the experimental planning phases so other researchers can reproduce your pipeline and build on it. Provide concrete artifacts you would produce.

MediumTechnical

28 practiced

You aim to publish at a top-tier conference. Describe how a thorough literature review changes the framing of an idea to maximize novelty and clarity. Include how you would discover and cite closest prior work, craft the 'related work' section, and choose which experiments are essential vs optional.

MediumTechnical

22 practiced

As a research lead, you must prioritize research directions for your team over the next 12 months. Describe a decision framework that balances scientific novelty, publication potential, product impact, resource requirements, and risk. Include how you'd solicit input and metrics for success.

MediumTechnical

41 practiced

Estimate how you would compute required sample sizes and compute budget to detect a 2% absolute improvement on a binary classification AUC with 80% power. Describe assumptions, formulas, and practical workarounds if the required budget is prohibitive.

Sample Answer

**Approach & key assumptions**- Treat AUC estimates as approximately Normal; target absolute AUC improvement Δ = 0.02; two-sided α = 0.05, power 1−β = 0.8.- Use DeLong/paired variance when same cases scored by both models; use unpaired approximation if different test sets.- Need base AUC (A0). Assume class prevalence π (pos fraction). Assume balanced scoring variance approximated from AUC.

**Sample-size formula (unpaired, approximate)**- For two independent AUC estimates, sample size is driven by number of positives (n+) and negatives (n−). Approximate per-group total sample size using Normal difference:

text

n_total ≈ ( (Z_{1-α/2} + Z_{1-β})^2 * (σ1^2 + σ2^2) ) / Δ^2

- Use DeLong variance approximation components for AUC (depends on AUC and π). For planning, approximate σ^2 ≈ A*(1−A) / (n_pos * n_neg / (n_pos + n_neg)) — practical heuristic to convert to required counts of positives and negatives.

Z-values:

text

Z_{1-α/2} = 1.96
Z_{1-β} = 0.84

**Practical numeric workflow**1. Estimate baseline A0 and prevalence π from historical data.2. Compute or simulate DeLong variance per AUC at that A0 (preferred).3. Plug variances into formula to solve for n_total (or for n_pos/n_neg separately).4. If paired design possible (same samples scored by both models), use paired-variance (typically much smaller), recompute n.

**Workarounds if budget prohibitive**- Use paired within-subject design to reduce variance (often 3–5x fewer samples).- Enrich positive class (stratified sampling) to increase effective information; adjust analysis via weights.- Use surrogate endpoints or proxy metrics correlated with AUC for cheaper pilots.- Use sequential / adaptive testing: run smaller pilot, update variance estimate, stop early if promising.- Accept a larger Δ (effect size) or lower power for exploratory research; report uncertainty.- Run simulation-based power using bootstrap/Monte Carlo with realistic score distributions to refine estimates.

**Why this matters**- DeLong + paired design give most efficient, realistic estimates for research settings. If exact closed-form variance is unclear, run simulations with your model scores to get empirical σ and then apply the Normal formula above.

MediumTechnical

27 practiced

Design a checklist for selecting baselines in a research paper where compute cost is a major constraint. Include how to balance fairness, representativeness, and implementation cost, and propose a minimal but defensible baseline set.

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