26 citations · 44 across the 17 of their papers we have counts for
4 papers · 1 filter
On-the-go Forgetting without Explicit Unlearning via ERASE
Kushal Chakrabarti, Mayank Baranwal
Existing unlearning approaches typically rely on post hoc weight adaptation or distillation, leading to duplicated memory costs, degraded generalization, and limited scalability. I…
SOVER: Formal Certification of Optimization Reformulations via LLM-Assisted SMT Verification
Swapnil Bhattacharyya, Mayank Baranwal
Large Language Models (LLMs) have shown remarkable promise in translating and reformulating complex mathematical optimization problems across modeling languages. However, validatin…
Quantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry
Susmit Sarkar, Abhinav Raghuvanshi, Kushal Chakrabarti +1
We study distributed optimization with stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD, a quantized stochastic primal…
On a Gradient Approach to Chebyshev Center Problems with Applications to Function Learning
Abhinav Raghuvanshi, Mayank Baranwal, Debasish Chatterjee
We introduce , the first gradient-based optimization framework for solving Chebyshev center problems, a fundamental challenge in optimal function learning and geom…