Parthe Pandit - RankMe

47 papers A* 8A 3B 1C 1Misc 2Journal 31Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Asymptotic convexity of wide and shallow neural networks. CoRR Vivek Borkar, Parthe Pandit
2025	A	`conf`	AxlePro: Momentum-Accelerated Batched Training of Kernel Machines. AISTATS Yiming Zhang, Parthe Pandit
2025	A*	`conf`	Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. ICML Neil Mallinar, Daniel Beaglehole, Libin Zhu, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin
2025	J	`jnl`	Feature maps for the Laplacian kernel and its generalizations. CoRR Sudhendu Ahir, Parthe Pandit
2025	J	`jnl`	GAN Training With Kernel Discriminators: What Parameters Control Convergence Rates? IEEE Trans. Signal Process. Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2025	J	`jnl`	Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime. J. Mach. Learn. Res. Parthe Pandit, Zhichao Wang, Yizhe Zhu
2024	J	`jnl`	Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. CoRR Neil Mallinar, Daniel Beaglehole, Libin Zhu, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin
2024	J	`jnl`	Fast training of large kernel models with delayed projections. CoRR Amirhesam Abedsoltan, Siyuan Ma, Parthe Pandit, Mikhail Belkin
2024	J	`jnl`	Mirror Descent on Reproducing Kernel Banach Spaces. CoRR Akash Kumar, Mikhail Belkin, Parthe Pandit
2024	A	`conf`	On the Nyström Approximation for Preconditioning in Kernel Machines. AISTATS Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin
2024	J	`jnl`	Universality of kernel random matrices and kernel regression in the quadratic regime. CoRR Parthe Pandit, Zhichao Wang, Yizhe Zhu
2023	Misc	`conf`	Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks. ACSSC Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2023	J	`jnl`	Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks. CoRR Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2023	J	`jnl`	Mechanism of feature learning in convolutional neural networks. CoRR Daniel Beaglehole, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin
2023	J	`jnl`	On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions. SIAM J. Math. Data Sci. Daniel Beaglehole, Mikhail Belkin, Parthe Pandit
2023	J	`jnl`	On the Nystrom Approximation for Preconditioning in Kernel Machines. CoRR Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit, Luis Rademacher
2023	A*	`conf`	Toward Large Kernel Models. ICML Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit
2023	J	`jnl`	Toward Large Kernel Models. CoRR Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit
2022	J	`jnl`	A note on Linear Bottleneck networks and their Transition to Multilinearity. CoRR Libin Zhu, Parthe Pandit, Mikhail Belkin
2022	J	`jnl`	Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting. CoRR Neil Mallinar, James B. Simon, Amirhesam Abedsoltan, Parthe Pandit, Mikhail Belkin, Preetum Nakkiran
2022	A*	`conf`	Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting. NeurIPS Neil Mallinar, James B. Simon, Amirhesam Abedsoltan, Parthe Pandit, Mikhail Belkin, Preetum Nakkiran
2022	J	`jnl`	Feature learning in neural networks and kernel machines that recursively learn features. CoRR Adityanarayanan Radhakrishnan, Daniel Beaglehole, Parthe Pandit, Mikhail Belkin
2022	A*	`conf`	Instability and Local Minima in GAN Training with Kernel Discriminators. NeurIPS Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2022	J	`jnl`	Instability and Local Minima in GAN Training with Kernel Discriminators. CoRR Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2022	J	`jnl`	Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions. CoRR Mojtaba Sahraee-Ardakan, Melikasadat Emami, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2022	J	`jnl`	Kernel Ridgeless Regression is Inconsistent for Low Dimensions. CoRR Daniel Beaglehole, Mikhail Belkin, Parthe Pandit
2021	A*	`conf`	Implicit Bias of Linear RNNs. ICML Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2021	J	`jnl`	Implicit Bias of Linear RNNs. CoRR Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2020	A*	`conf`	Generalization Error of Generalized Linear Models in High Dimensions. ICML Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2020	J	`jnl`	Generalization Error of Generalized Linear Models in High Dimensions. CoRR Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2020	J	`jnl`	Generalized Autoregressive Linear Models for Discrete High-Dimensional Data. IEEE J. Sel. Areas Inf. Theory Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash A. Amini, Sundeep Rangan, Alyson K. Fletcher
2020	J	`jnl`	Inference With Deep Generative Priors in High Dimensions. IEEE J. Sel. Areas Inf. Theory Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
2020	J	`jnl`	Inference in Multi-Layer Networks with Matrix-Valued Unknowns. CoRR Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
2020	J	`jnl`	Low-Rank Nonlinear Decoding of $μ$-ECoG from the Primary Auditory Cortex. CoRR Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Alyson K. Fletcher, Sundeep Rangan, Michael Trumpis, Brinnae Bent, Chia-Han Chiang, Jonathan Viventi
2020	A*	`conf`	Matrix Inference and Estimation in Multi-Layer Models. NeurIPS Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
2019	B	`conf`	Asymptotics of MAP Inference in Deep Networks. ISIT Parthe Pandit, Mojtaba Sahraee, Sundeep Rangan, Alyson K. Fletcher
2019	J	`jnl`	Asymptotics of MAP Inference in Deep Networks. CoRR Parthe Pandit, Mojtaba Sahraee, Sundeep Rangan, Alyson K. Fletcher
2019	J	`jnl`	High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence. CoRR Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash A. Amini, Sundeep Rangan, Alyson K. Fletcher
2019	J	`jnl`	Inference with Deep Generative Priors in High Dimensions. CoRR Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
2019	A	`conf`	Sparse Multivariate Bernoulli Processes in High Dimensions. AISTATS Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash A. Amini, Sundeep Rangan, Alyson K. Fletcher
2018	J	`jnl`	A linear complementarity based characterization of the weighted independence number and the independent domination number in graphs. Discret. Appl. Math. Parthe Pandit, Ankur A. Kulkarni
2018	C	`conf`	Discount-Based Pricing and Capacity Planning for EV Charging Under Stochastic Demand. ACC Parthe Pandit, Samuel Coogan
2018	A*	`conf`	Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis. NeurIPS Alyson K. Fletcher, Parthe Pandit, Sundeep Rangan, Subrata Sarkar, Philip Schniter
2017	—	`conf`	Non-constructive lower bounds for binary asymmetric error correcting codes. NCC Parthe Pandit, Ankur A. Kulkarni
2016	J	`jnl`	A linear complementarity based characterization of the weighted independence number and the independent domination number in graphs. CoRR Parthe Pandit, Ankur A. Kulkarni
2016	J	`jnl`	Refinement of the Equilibrium of Public Goods Games over Networks: Efficiency and Effort of Specialized Equilibria. CoRR Parthe Pandit, Ankur A. Kulkarni
2015	Misc	`conf`	Structural segmentation of Hindustani concert audio with posterior features. ICASSP Prateek Verma, Vinutha T. P., Parthe Pandit, Preeti Rao

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.