1 citations · 1 across the 8 of their papers we have counts for
8 papers
Statistical Inference and Learning for Shapley Additive Explanations (SHAP)
Justin Whitehouse, Ayush Sawarni, Vasilis Syrgkanis
The SHAP (short for Shapley additive explanation) framework has become an essential tool for attributing importance to variables in predictive tasks. In model-agnostic settings, SH…
CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
Ayush Sawarni, Jiyuan Tan, Vasilis Syrgkanis
Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effect (ATE). This approach conflate…
Policy Learning with Abstention
Ayush Sawarni, Jikai Jin, Justin Whitehouse +1
Policy learning algorithms are widely used in areas such as personalized medicine and advertising to develop individualized treatment regimes. However, most methods force a decisio…
Preference Learning with Response Time: Robust Losses and Guarantees
Ayush Sawarni, Sahasrajit Sarmasarkar, Vasilis Syrgkanis
This paper investigates the integration of response time data into human preference learning frameworks for more effective reward model elicitation. While binary preference data ha…
Generalized Linear Bandits with Limited Adaptivity
Ayush Sawarni, Nirjhar Das, Siddharth Barman +1
We study the generalized linear contextual bandit problem within the constraints of limited adaptivity. In this paper, we present two algorithms, and $\texttt{R…
Nash Regret Guarantees for Linear Bandits
Ayush Sawarni, Soumybrata Pal, Siddharth Barman
We obtain essentially tight upper bounds for a strengthened notion of regret in the stochastic linear bandits framework. The strengthening -- referred to as Nash regret -- is defin…
Learning Good Interventions in Causal Graphs via Covering
Ayush Sawarni, Rahul Madhavan, Gaurav Sinha +1
We study the causal bandit problem that entails identifying a near-optimal intervention from a specified set of (possibly non-atomic) interventions over a given causal graph. H…
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits
Siddharth Barman, Arindam Khan, Arnab Maiti +1
We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-armed bandit (MAB) framework, the current work quantifies the performance of bandit algo…