Lalit Jain

26 papers · 2015–2024 · 5 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) 🏃 Academic Marathon (9) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (5) 🗺️ Taxonomy Completionist (35) 🔬 Deep Specialist (12) 🏆 Keyword Champion (8) 🗃️ Keyword Collector (109) ⚡ Prolific Year (7) 🔥 Unstoppable (10) 📈 Trend Setter 💎 Century Club (26)

Conferences

NIPS (15) AISTATS (5) ICML (3) UAI (2) AAAI (1)

Top co-authors

Kevin G. Jamieson (9) Kevin Jamieson (8) Blake Mason (6) Robert Nowak (5) Romain Camilleri (5) houssam nassif (3) Tanner Fiez (3) Andrew Wagenmaker (3) Kwang-Sung Jun (3) Julian Katz-Samuels (2)

Keywords

experimental design (8) sample complexity (6) best-arm identification (6) linear bandit (6) active learning (6) pure exploration (5) multi-armed bandit (4) stochastic optimization (3) regret minimization (3) combinatorial bandit (3) binary classification (2) ordinal embedding (2) adaptive experimental design (2) adaptive sampling (2) convex optimization (1) image classification (1) interactive learning (1) online learning (1) regret analysis (1) causal inference (1)

Papers

Fair Active Learning in Low-Data Regimes UAI 2024

Nearly Minimax Optimal Submodular Maximization with Bandit Feedback NIPS 2024

Adaptive Experimentation When You Can't Experiment NIPS 2024

Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning NIPS 2024

A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity AISTATS 2024

Optimal Exploration is no harder than Thompson Sampling AISTATS 2024

Pessimistic Off-Policy Multi-Objective Optimization AISTATS 2024

Experimental Designs for Heteroskedastic Variance NIPS 2023

Active Learning with Safety Constraints NIPS 2022

Instance-optimal PAC Algorithms for Contextual Bandits NIPS 2022

An Experimental Design Approach for Regret Minimization in Logistic Bandits AAAI 2022

Nearly Optimal Algorithms for Level Set Estimation AISTATS 2022

Improved Algorithms for Agnostic Pool-based Active Classification ICML 2021

Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits ICML 2021

Selective Sampling for Online Best-arm Identification NIPS 2021

Finding All $\epsilon$-Good Arms in Stochastic Bandits NIPS 2020

Spectral Methods for Ranking with Scarce Data UAI 2020

An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits NIPS 2020

Sequential Experimental Design for Transductive Linear Bandits NIPS 2019

A New Perspective on Pool-Based Active Classification and False-Discovery Control NIPS 2019

Firing Bandits: Optimizing Crowdfunding ICML 2018

A Bandit Approach to Sequential Experimental Design with False Discovery Control NIPS 2018

Adaptive Sampling for Coarse Ranking AISTATS 2018

Learning Low-Dimensional Metrics NIPS 2017

Finite Sample Prediction and Recovery Bounds for Ordinal Embedding NIPS 2016

NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning NIPS 2015