2 citations · 2 across the 9 of their papers we have counts for
9 papers
Evolutionary Feature Engineering for Structured Data
Ege Onur Taga, Yilin Zhuang, M. Emrullah Ildiz +4
Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using…
Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery
Halil Alperen Gozeten, Xuechen Zhang, Emrullah Ildiz +3
Recent LLM-guided evolutionary search methods have shown that iterative program mutation can discover strong algorithms, but they typically optimize each task independently, even w…
Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought
Muhammed Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga +1
State-of-the-art reasoning models utilize long chain-of-thought (CoT) to solve increasingly complex problems using more test-time computation. In this work, we explore a long CoT s…
Learning to Bet for Horizon-Aware Anytime-Valid Testing
Ege Onur Taga, Samet Oymak, Shubhanshu Shekhar
We develop horizon-aware anytime-valid tests and confidence sequences for bounded means under a strict deadline . Using the betting/e-process framework, we cast horizon-aware be…
Covariance-Aware Transformers for Quadratic Programming and Decision Making
Kutay Tire, Yufan Zhang, Ege Onur Taga +1
We explore the use of transformers for solving quadratic programs and how this capability benefits decision-making problems that involve covariance matrices. We first show that the…
TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
Ege Onur Taga, M. Emrullah Ildiz, Samet Oymak
The diversity of time series applications and scarcity of domain-specific data highlight the need for time-series models with strong few-shot learning capabilities. In this work, w…
Retrieval Augmented Time Series Forecasting
Kutay Tire, Ege Onur Taga, Muhammed Emrullah Ildiz +1
Retrieval-augmented generation (RAG) is a central component of modern LLM systems, particularly in scenarios where up-to-date information is crucial for accurately responding to us…
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws
M. Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga +2
A growing number of machine learning scenarios rely on knowledge distillation where one uses the output of a surrogate model as labels to supervise the training of a target model.…
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
Xuechen Zhang, Zijian Huang, Ege Onur Taga +3
Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accur…